We study in this paper the time evolution of stock markets using a statistical physics approach. We consider an ensemble of agents who sell or buy a good according to several factors acting on them: the majority of the neighbors, the market ambiance, the variation of the price and some specific measure applied at a given time. Each agent is represented by a spin having a number of discrete states q or continuous states, describing the tendency of the agent for buying or selling. The market ambiance is represented by a parameter T which plays the role of the temperature in physics: low T corresponds to a calm market, high T to a turbulent one. We show that there is a critical value of T, say T_c, where strong fluctuations between individual states lead to a disordered situation in which there is no majority: the numbers of sellers and buyers are equal, namely the market clearing. The specific measure, by the government or by economic organisms, is parameterized by $H$ applied on the market at the time t₁ and removed at the time t₂. We have used Monte Carlo simulations to study the time evolution of the price as functions of those parameters. In particular we show that the price strongly fluctuates near T_c and there exists a critical value H_c above which the boosting effect remains after H is removed. Our model replicates the stylized facts in finance (time-independent price variation), volatility clustering (time-dependent dynamics) and persistent effect of a temporary shock. The second party of the paper deals with the price variation using a time-dependent mean-field theory. By supposing that the sellers and the buyers belong to two distinct communities with their characteristics different in both intra-group and inter-group interactions, we find the price oscillation with time. Results are shown and discussed.

China combines green energy and industrial policy in its power market reform with various policy initiatives, including price support scheme for electricity from renewable sources and subsidies in the push for broader use of greener energy. This study focuses on the impacts of power market reform on the stock price volatility of listed power companies: 1) we use the Iterative Cumulative Sums of Squares (ICSS) algorithm to identify structural break points in stock prices; 2) we analyze the characteristics of stock price volatility based on the GARCH model; 3) we report the impact of power regulation on stock price fluctuations based on the Autoregressive Distributed Lag (ARDL) model. The result suggests three structural breaks in China’s power stock price volatility were related to the promulgation of power market reform policies. We find that industrial policies promote the reduction of power stock price fluctuations and its impact on power stock price volatility is consistent in the long run. However, our study also indicates the recent policies related to renewable energy do not have a very significant impact on the power stock market.

This research article focused on discovering appropriate liquidity risk concerns in the financial Sector. Primarily, it deals with the appropriate liquidity factors that affect financial performance services pertained to several liquidity crises and the respective liquidity measures. Liquidity is considered the primary constraint. The Liquidity Ratios are the defective condition of calculating the financial performance services and their respective status. For fulfilling this performance service status prediction, this research use calculation for consistency index procedure. Liquidity index-based predictions have collaborated concerning the price movement in the stock market. The Liquidity will affect the trading and market transparency whenever the price moment varies. To control this trading and not market transparency of liquidity index be employed the greedy approach for estimating the most factor affected by the liquid index. Hence, this research elaborates on the greedy approach today with 25 essential financial factors. Each Panther will have combined with the tractor-confirmed liquidity crisis whenever the factors of liquidity crisis are combined with the actual financial factor, the minimum and maximum Degree of matching are calculated. This calculation determines the factors that affected the Liquidity concerning their exact, Plugin, subsume, and fail condition. Eventually, the search concludes the determination of factors affecting Liquidity concerning the actual finance factor and liquidity crisis. Finally, the result was concluded with the Degree of matching and their efficiency of finding liquidity factors with a successful reasonable result. Based on the Degree of matching result, this research found the actual reason for Liquidity with the help of various financial factors

The purpose of this paper is to analyze market reflexivity in agricultural futures contracts with different maturities. To this end, we apply a four-dimensional Hawkes model to storable and non-storable agricultural commodities. We find market reflexivity for both storable and non-storable commodities. Reflexivity accounts for about 50 to 70 percent of the total trading activity. Differences between nearby and deferred contracts are less pronounced for non-storable than for storable commodities. We conclude that the co-existence of exogenous and endogenous price dynamics does not change qualitative characteristics of the price discovery process that have been observed earlier without consideration of market reflexivity.

Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of the electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and can not outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with rolling 3-year window and compared the results with the RNNs. In our experiments, 3-layered GRUs outperformed all other neural network structures and state of the art statistical techniques in a statistically significant manner in the Turkish day-ahead market.

This study is the first to investigate the asymmetric effects of oil price volatility on stock returns for the Ho Chi Minh Stock Exchange (HOSE). We utilized weekly series of VN30-Index, WTI crude oil prices, geopolitical risks (GPR) index and gold prices spanning from 6 February 2012 to 31 December 2023 as data sources. Using a nonlinear Autoregressive Distributed Lag (NARDL) bounds testing approach, we found that in the short-term, oil price volatility has negative asymmetric effects on the market returns. Specifically, in the short-term, a one percent increase in oil price volatility immediately leads to a 2,6868 percent decrease in the market returns while a similar magnitude decrease in oil price volatility is associated with 6.3180 percent increase in the market returns. In addition, the results obtained from the NARDL model indicated that, in the long-term, the negative and positive changes of oil price volatility have significantly negative effects on the market returns. Finally, the findings derived from the error correction model (ECM) show that 98.21 percent of the disequilibria from the previous week are converged and corrected back to the long-term equilibrium in the current week.

Our research proposes two different training and modelling approaches of Generative Pre-trained Transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. Our findings indicate that integrating GPT insights into electricity price trend forecasting can result in more precise predictions and a deeper understanding of market dynamics. Through our research, we aim to provide insights to understand the capabilities of GPT solutions, and how those can be used to enhance prediction accuracy, ultimately supporting informed decision-making for stakeholders across the Spanish electricity market and companies whose margins heavily depend on electricity costs and price volatility.

A simple bounded stochastic motion is employed for modelling equity price dynamics under shocks, where the prices are constrained to lie between two positive bounds. While the stochastic process has an inaccessible boundary, the other boundary is quasi-bounded, implying that the boundary can be breached if the probability leakage condition is met. The quasi-boundedness of the process at the boundary can thus provide an indicator of possible risk of equities under price shocks. Empirical calibration of model parameters of the proposed process for equities can be performed easily due to the availability of an analytically tractable probability density function which generates fat-tailed distributions consistent with empirical observations. The volatility and mean-reversion of the S&P500 dynamics calibrated by the process is positively and negatively co-integrated respectively with the VIX index representing the level of market distress. The process captures the high likelihood of Hertz’s default about two months earlier, using only information until that point, and before the firm filed for Chapter 11 bankruptcy in May 2020 as a result of the Covid-19 pandemic. Empirical calibration of the process for GameStop’s stock price shows that the short squeeze of the stock occurred when the condition for breaching an upper boundary was met on 14 January 2021, i.e., about two weeks before major short sellers closed out their positions with significant losses. The trading volume of the stock was positively co-integrated with the probability leakage ratio.

This paper identifies a dilemma in the relationship between R^2 and price efficiency: After comprehensively studying the R^2 change around 7 well-known corporate events, neither the traditional understanding of R^2 as price inefficiency, nor the behavioral way of R^2 as price efficiency can explain the observed R^2 change around the events. We adopt an alternative methodology to replace the standard difference-in-difference regression and directly decompose the R^2 change. We find that, due to the endogeneity of events, the changes of R^2 are over-estimated. We further propose that in the event study setting, the R^2 change may be simply the consequence of the inflow/outflow of some trend-chasing investors, and it may be detached from price (in) efficiency. Empirical evidences are consistent with this hypothesis.

The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various Machine Learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to be studied. This research introduces two datasets from different markets, one with over 300,000 entries from Germany to serve as a training base for deep prediction models and a second dataset from Romania containing more than 15,000 car quotes used mainly to observe local traits. As such, we include extensive cross-market analyses by comparing the emerging Romanian market versus one of the world’s largest and most developed car markets, Germany. Our study uses several neural network architectures that capture complex relationships between car model features, individual add-ons, and visual features to predict used car prices accurately. Our models achieved a high R2 score exceeding 0.95 on both datasets, indicating their effectiveness in estimating used car prices. Moreover, we experimented with advanced convolutional architectures to predict car prices based solely on visual features extracted from car images. This approach exhibited transfer-learning capabilities, leading to improved prediction accuracy, especially since the Romanian training dataset is limited. Our experiments highlight the most important factors influencing the price, while our findings have practical implications for buyers and sellers in assessing the value of vehicles. At the same time, the insights gained from this study enable informed decision-making and provide valuable guidance in the used car market.

This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. The study evaluates model performance using key regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results indicate that CNN and GRU models generally outperform the others, depending on the specific stock, and demonstrate superior capabilities in forecasting stock price movements. This investigation provides insights into the strengths and limitations of each model while highlighting potential avenues for improvement through feature engineering and hyperparameter optimization.

Uncertain price movement in staple food commodities puts agrarian economies at risk if not monitored and managed consistently. Hence, an attempt has been made to analyze the price behavior and integration across major wholesale and retail markets for rice and wheat in India. Monthly data (July 2000 to June 2022) on prices viz., wholesale and retail were sourced from the Food and Agriculture Organization and analyzed using growth rate, instability index, seasonal price index, Johansen’s test on cointegration, Granger causality test, and impulse response function. Findings indicated strong evidence of price dynamics in the selected markets in terms of spatial and temporal variation, clear-cut seasonality linking to production, and price divergence between wholesale and retail markets. Johansen’s test indicated a strong integration between wholesale and retail markets exhibiting unidirectional-, bidirectional- and no-causality. Impulse response analysis revealed that the selected wheat and rice markets are efficient in terms of ‘price discovery’ which takes place initially in the wholesale market, and then transmitted to the retail market. The study advocates decision-making information to the producers, traders, and consumers who have a potential interest in getting advantage of the price movement. It is concluded that strengthening market intelligence and reducing the distortion in markets will improve the existing overall performance.

The In this paper, a universal method has been developed, which is a combination of an optimization method and a method for assessing the marginal utility. At present, the problem of optimal load distribution in the power system between a hydropower plant (HPP) and thermal power plants (TPP) is solved using the equality of the differential incremental rate characteristics of fuel consumption at TPP and water consumption at HPP by the Lagrangian multiplier method. In this case, the number of iterations can be five or more. The proposed approach is based, first of all, on the correct representation of the differential characteristics and calculation of a hydro resource price for the operational control of the HPP. Based on the comparison of water volume at a HPP and fuel amount at combined heat and power plants (CHPP) used for generation of 1 kW power, it is possible to determine a water price for a HPP. As a result of implementing the developed method for the HPP, a price of sold electricity in the flexible energy market will be comparable with the price for sold electricity produced at CHPPs, being equal to approximately 120 rubles/MW∙h.

The penetration of modern supermarkets is believed to be the cause of the declining role of traditional markets and street vendors in Indonesia. But the competition between state-promoted markets and traditional markets is rarely discussed. This investigation focuses on traditional markets as social institutions, which continuously have developed a variety of strategies in order to remain competitive in the midst of intense rivalry. Firstly, we will outline a theoretical understanding of the traditional markets positioning along the concepts of flexibility and market devices. Secondly, we empirically reflect the strategies of four traditional vegetable markets in the District of Malang, East Java Province, as case studies. We show that the traditional markets build flexibility without governmental support by: (1) specifying commodities, (2) segmenting customers, (3) managing the load time, (4) modifying transportation to operate more efficiently, and (5) minimizing transaction costs by leveraging social capital. We argue that traditional vegetable markets institutionally reduce the potential of transaction costs to be competitive and avoid problems of coordination by building social capital through networks. All in all, market institutions supported by market devices are flexibly capable of adapting to the pressures of both selfishness among actors and competition with other markets.

The new regulation was introduced to the franchise market of South Korea in that an applicant who wants to be a franchiser must set up direct retail stores and run them for at least one year before recruiting franchisees. Considering the purpose of the regulation, it is inferred that once franchisers run their own stores, their franchisees would be better off than otherwise. This paper addresses one reason that the Korea Fair Trade Commission has no choice but to introduce the regulation. Along the line, this paper investigates whether franchisees would have the more likelihood to make profit in the case of that franchisers operate their own stores. The result demonstrates that this case could be true since the operation of franchiser owned stores could be helpful in reducing cost of franchisees.

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.

The widespread integration and growing dependence among currency, stock and commodity markets make these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we explore the spillovers and nonlinear dependences between the seven major foreign ex-change rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by em-ploying directional spillover network approach. We also use multi-scale de-composition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. Major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the EUR as the largest contributor of connectedness to the other series. The central role of the EUR is a net transmitter of connectedness to gold, oil, the GBP, JPY, and CHF.

Market uncertainty has a significant impact on market performance. Previous studies put much efforts in investigations on market uncertainty related to information asymmetry and risk. However, they have neglected the uncertainty inherent in market transactions, which is also an important aspect of market performance, besides quantity of transactions and market efficiency. In this paper, we put forward the concept of transaction entropy to measure the market uncertainty and see how it changes with price. The definition of transaction entropy is the ratio of total information entropy of all traders to quantity of transactions, reflecting the level of uncertainty in making transactions. And the transaction entropy is the lowest at equilibrium, it will be decreasing in a shortage market, and increasing in a surplus market. Additionally, we make a comparison of total entropy of the centralized market with that of the decentralized market, revealing that the price filtering mechanism could effectively reduce market uncertainty. Overall, the introduction of transaction entropy enriches our understanding of market uncertainty and facilitates a more comprehensive assessment of market performance.

With increasing global attention on carbon emission control, the carbon market has become an important tool for carbon emission regulation. As an important part of energy trading, the electricity market is closely related to the carbon market. Given the relative maturity of the EU carbon market and electricity market, this paper identifies key points of market volatility through the Coupling coordination model analysis, and uses the GPT large model analysis to analyze policy factors that have a greater impact on carbon market and electricity market transactions. The research results show that market-regulating policies and expectation management policies have the greatest impact on the degree of coupling between the carbon and electricity markets. The impact of secondary policies such as international cooperation policies and structural adjustment policies is relatively insignificant. Although the development of China's carbon market faces many challenges, reasonable policy interventions are expected to achieve significant results in the domestic carbon market. Finally, this paper also draws on analogy analysis to draw corresponding implications for China from the EU's carbon-electricity coupling policy promulgation.

For a long-only active equity manager (or investor), the ability to hedge the downside risk before a significant price correction is a valuable skill. The authors proposed constructing a signal that provides timing information for a manager to hedge the downside risk. This systematic signal can help a manager put on a hedge before the price corrections of the dot com bubble in 2000 and the global financial crisis in 2008. The signal is constructed for each GICS sector index in the S&P 500. When a sector’s PE indicates a high valuation and, at the same time, if the high valuation sector’s correlations with some other sectors are out of their historical norm, then these two conditions contribute a timing signal for the significant price swing of the index during the following six months. After observing the timing signal, managers can benefit significantly by hedging the downside risk. The signal can also be interpreted as the beginning of a high volatility regime for a sector.

With the rise of internet technology and the growth of mobile communications, the global economy has undergone a significant transformation, shifting towards digital and soft markets. As a result, great efforts have been made to establish virtual marketplaces that provide customers with an exceptional shopping experience. One crucial aspect of this new paradigm is marketing, which has led to the emergence of a new stage known as E-marketing. In E-marketing, understanding customer behavior is of utmost importance. Marketing teams and companies are eager to gain insights into how different customer segments react to their products. Marketing strategies are now developed based on customer information such as financial status, location, age, gender, and more. As a result, the abundance of customer data generated by these new commerce technologies has surpassed the capabilities of traditional data analysis methods. To extract valuable knowledge from this vast amount of data, data mining technologies have come into play. Techniques such as clustering, classification, and prediction are utilized to mine marketing data and uncover hidden patterns and trends. The impact of data mining on E-marketing has been the subject of analysis in this study. A questionnaire-based review was conducted to collect the necessary data, which was then analyzed using SPSS 23 software. The study's results demonstrate that data mining can significantly enhance the performance of E-marketing by providing intelligent and efficient predictions of customer behaviour. Moreover, this technology offers similar advantages for end-customers as well. By leveraging the power of data mining, companies can gain valuable insights, tailor their marketing efforts, and ultimately provide better products and services to their customers in the digital marketplace.

The intraday electricity market is increasing its importance within the energy markets. This article introduces a case study, basing on real examples of the contribution electricity markets make to prevent and/or solve emergencies in electric system. The case study was conducted in four possible states on short-term markets – different variants of trading period lengths and intervals in which the system imbalance is evaluated. The outage of the biggest power plant´s unit in the transmission system was always considered, and together with the situation in the short-term markets, the impact on the operation and maintenance on a transmission system was evaluated from that point-of view, system imbalance, and necessity to activate necessary ancillary services by an operator.

Emission trading schemes (ETSs) have become vital tools in striving toward global emission reduction objectives and promoting a sustainable economy. This paper empirically investigates the impact of market conditions on interconnectedness within precious metals, clean energy, green bonds, and carbon markets, focusing on China and the EU ETSs as the study subjects. A TVP-VAR-based asymmetric connectedness approach is adopted to capture the evolving relationships between the variables of interest in the time–frequency domain. The empirical findings reveal that: First, time-varying asymmetric connectedness and spillover effects among clean energy, precious metals, green bonds, and carbon markets are identified in both time and frequency domains under diverse market conditions, with long-term connectedness asymmetry being particularly sensitive, although spillovers exhibit variations. Second, the role and intensity of net transmitters or receivers vary across market conditions and frequency domains, indicating the complex and nuanced nature of market interactions. The EU ETS demonstrates a higher level of connectivity with global markets for clean energy, precious metals, and green bonds when compared to the Chinese ETS. However, it also shows greater susceptibility to fluctuations in market conditions. These results have significant implications for both investors striving to build sustainable diversified portfolios and regulators endeavoring to craft effective policies for risk regulation and sustainable development.

The current study explores the problem of illiquidity in the corporate bond market and examines a broad set of liquidity proxies through empirical investigation. Purpose: The purpose of this paper is to give solution to the global issue of illiquidity in the corporate bond market. The problem is identified by many researchers and this paper attempts to find out the viable solution of the "fungibility route" as an alternative to the "liquidity route". Design/ Methodology: An Analysis of a sample size of 2,34,772 trade data of corporate bonds and a sample size of 2,00,607 trade data of G-securities is done to identify the problem. Findings / Solution proposed: A mathematical model based on credit risk differential and sensitivity differential is proposed to find out the fair value at which an illiquid bond can be swapped by a liquid bond. To arrive at the fair value of the illiquid bond, we have calculated risk adjusted discount rate using modified duration and credit risk differential. With our solution, it is possible to enhance the liquidity of the bond not directly but through fungibility route. Originality: the research is first of its kind in which solution is given to the global problem of corporate bond illiquidity. To execute the trade in the fungibility route, proposed parties involved are the Fungibility platform, the initiator, and the bidder. The derived fair value would facilitate trade and serve as an exit strategy for illiquid bond holders worldwide.

Does the choice of proxy for stock market development matter? This paper suggests that the growth effect of stock market development is sensitive to the choice of proxy and using alternative financial development indicators have practically no influence on the results. We found that using either the stock market capitalization to GDP ratio or the stock market returns; have a positive and significant effect on growth. However, we cannot make same conclusion when one uses either the ratio of total value of trades on the major stock exchanges to GDP or stock market turnover ratio to proxy for stock market development as the coefficient on these variables were found to be statistically insignificant. The indexes extracted from principal component analysis confirm the sensitivity of the effect to the choice of proxy. This finding suggest that stock market development is a conceptual terms, thus, representing it with single indicators make it impossible to identify which stock market development indicators have a significant positive growth effects.

This comprehensive review delves into gold market business management, covering historical perspectives, market dynamics, mining models, investment strategies, technology, regulations, global trends, and sustainability. Starting with a historical overview, it traces the gold market's evolution and analyzes supply-demand dynamics, price fluctuations, and geopolitical influences. The article scrutinizes gold mining models, evaluating environmental and social responsibilities, and explores financial management strategies, emphasizing risk and gold's role in portfolios. Examining technology's role, it highlights innovations in exploration, mining, and processing for improved efficiency and sustainability. The regulatory landscape analysis encompasses compliance challenges, environmental regulations, and ethical considerations, emphasizing global impacts. Global market trends are explored, identifying potential opportunities and challenges, offering strategic insights. The review concludes by evaluating sustainability practices, emphasizing environmental conservation, community engagement, and ethical sourcing.

The deregulated markets have replaced the traditional way of trading electricity from the producers to the consumer, via government-owned organizations and regulated tariffs. Nowadays, electricity prices are determined by the offer-demand mechanism and consumers can negotiate tariffs with their supplier of choice. For classic wholesale suppliers, the tariffs are a result of the transactions performed on the wholesale market and the energy mix available in certain geographical regions. In parallel with becoming eligible and participating in retail electricity markets, the consumers use increasingly local generation sources based mostly on renewable energy technologies such as PV panels, and become prosumers. They want to be able to sell back to the market the generation surplus, in order to obtain the maximum benefits from their initial investment. Currently, several trading mechanisms for prosumers are available, ranging from the simplest, selling back the surplus to an aggregator at fixed tariffs, to more complex market schemes. This paper proposed a two-tier local market model for prosumers and consumers connected in microgrids, based on the blockchain technologies and other technologies and concepts such as remote sensing, smart grids, crowdsourcing and energy poverty.

The deregulated markets have replaced the traditional way of trading electricity from the producers to the consumer, via government-owned organizations and regulated tariffs. Nowadays, electricity prices are determined by the offer-demand mechanism and consumers can negotiate tariffs with their supplier of choice. For classic wholesale suppliers, the tariffs are a result of the transactions performed on the wholesale market and the energy mix available in certain geographical regions. In parallel with becoming eligible and participating in retail electricity markets, the consumers use increasingly local generation sources based mostly on renewable energy technologies such as PV panels, and become prosumers. They want to be able to sell back to the market the generation surplus, in order to obtain the maximum benefits from their initial investment. Currently, several trading mechanisms for prosumers are available, ranging from the simplest, selling back the surplus to an aggregator at fixed tariffs, to more complex market schemes. This paper proposed a two-tier local market model for prosumers and consumers connected in microgrids, based on the blockchain technologies and other technologies and concepts such as remote sensing, smart grids, crowdsourcing and energy poverty.

The aim of this study was to determine the impact of information technology in management of risks in the capital market-listed company is in Tehran Stock Exchange. The purpose of the present study is an applied descriptive approach. The target population for the survey, companies that from 2009 to the first half of 2015 have been a member of the Tehran Stock Exchange, through Cochranʼs sample size of 140 companies, respectively. We used cluster sampling method. In order to collect data from two questionnaires: risk management questionnaire Foakeh (2013) has 38 items and a standard questionnaire Chanvyas (2006) has 40 items, the whole five-item Likert scale questionnaire is above has been used. Data gathered through the questionnaire, sign the application was 21spss. For inferential analysis of the variables and to analyze the data from different statistical tests and regression was used Kolmogorov-Smirnov test. The results showed that information technology on risk management and its dimensions (primary market risk, market risk and the risk of secondary non-financial) impact.

This paper examines the transformation of the Ukrainian stock market by analyzing daily returns from the PFTS stock index spanning 1995-2022. Segmented into sub-periods, it tests the hypothesis that the market's efficiency has grown over time. Various data properties, including persistence, volatility, normality, and resistance to anomalies, are explored using different statistical techniques and methods. Volatility, indicated by standard deviation, exhibits a general decreasing trend, suggesting a shift towards a more efficient market. Returns show no significant differences across sub-periods, except for 2005-2007. R/S analysis indicates the decrease of persistence in the market: a trend towards increased efficiency. Normality tests support a normal distribution of daily returns throughout sub-periods. An analysis of anomalies reveals the market's resilience to day-of-the-week effects, with no specific trends observed. The results suggest the absence of serious changes in data properties in the Ukrainian stock market over the time, means evolutionary processes over 25 years can’t be clearly observed. Reforms and changes have had limited impact, justifying continued reform for the market's evolution.

Climate change, the scarcity of fossil fuels, technical advances in clean energy, and the volatility of crude oil prices are just a few of the factors that have prompted the world to recognize clean energy as a viable alternative to dirty energy. As part of the Paris Climate Accord of 2015, many countries agreed to change their economies to be more sensitive to climate change. Due to this Accord, which increased interest from investors and decision-makers, investments in clean energy companies have benefited [1,2]. Clean energy stocks, which are a part of the larger world of tradable reserves, might experience pricing inefficiencies. In this paper, we investigate the multifractal scaling behavior and efficiency of green finance markets, as well as traditional markets like gold, crude oil, and natural gas between January 1, 2018, and March 9, 2023, which covers periods of low volatility and financial instability (2020 and 2022 events). To test the serial dependency (autocorrelation) and the efficient market hypothesis, in its weak form, we employed the Lo and Mackinlay test and the DFA method. The empirical findings demonstrated that both periods exhibit severe multifractal and significant asymmetry, indicating that the price indices under study are not at all efficient.

The energy matrix worldwide has been going through difficulties in its discussions - such as irregular exploration, inefficient public policies, and arbitrariness concerning diplomatic and political definitions of those involved in this market. This work's general objective consists in analyzing associations and statistical inferences of the largest world oil producers, assimilating the contributions and singularities of this market from 1993 to 2020. Based on the Organization of the Petroleum Exporting Countries (OPEC), it was possible to identify the possible inferences and contributions of the ten largest oil producers in the world in more than two decades using statistical analysis through correlation, regression, and statistical analysis of variables. According to the research and the literature on the area, the oil market proposes support to its discussions, mainly in its productive approaches. It is possible to identify this market as a solid link to geopolitical actions, distributing the possibilities through economic bias and socio-cultural and historical factors on a global level.

The study tries to find out the impact of investors demographic variables on the return from the stock market. The data has been collected from the brokerage and Marchant banks for individual investors investments, return and demographic characteristics. The research covers the period from 2009 to 2021 and have a decade long enough to identify the trend. The structural break in the data also been adjusted to avoid any kind of endogeneity. The regression results suggest that demographic variables affect the investment pattern and beta return for the investors. the demographic variables such as education, location and experience have positive links with the beta whereas age and income show negative association. The education, income and experience variables are also found to be significant in the empirical analysis.

Climate adaptation is a major challenge. Chasing the sufficient amount of hay is getting in higher priority. Distant mass hay producers give favourable offers despite long distances. Quality is also gaining position and indicators like RFQ (Relative Forage Quality) is highlighting the marketing language. Hay market as we knew no longer exists in Hungary. Most farmers produce their own hay and do not spend extra cents to buy bales. Climate change however, force them to adapt and store more bales for the future. Horse owners and dairy farmers are the main driver to convince hay producers to provide high quality forage. We gathered Hungarian regional hay-price information and evaluated the trends in this sector. The demand-driven hay-price is in contradiction with premium quality timothy grass hay.

Many studies in the current literature annotate patterns in stock prices and use computer vision models to learn and recognize these patterns from stock price-action chart images. Additionally, current literature also use Long Short-Term Memory Networks to predict prices from continuous dollar amount data. In this study, we combine the two techniques. We annotate the consolidation breakouts for a given stock price data, and we use continuous stock price data to predict consolidation breakouts. Unlike computer vision models that look at the image of a stock price action, we explore using the convolution operation on raw dollar values to predict consolidation breakouts under a supervised learning problem setting. Unlike LSTMs that predict stock prices given continuous stock data, we use the continuous stock data to classify a given price window as breakout or not. Finally, we do a regularization study to see the effect of L₁, L₂, and Elastic Net regularization. We hope that combining regression and classification shed more light on stock market prediction studies.

This research discusses the business model challenges faced by cag aggregator platform models in developing countries. Technology-based platforms have been disrupting traditional businesses and rewriting the rules of the game. Ola cabs has been instrumental in shaping the Indian platform business landscape in a big way. Using case methodology the author analyses the situation and business model to present the salient features of Ola’s model. However, this paper also puts the spotlight on the inherent challenges in this model and warns the need for continuous business model innovation and service quality.

The objective of this study is to determine the impact of COVID-19 on the performance of Pakistani Stock Market. This study uses the data of COVID-19 related positive cases, fatalities, recovers and the closing prices of PSX 100 index of the first half of 2020. The findings of the study suggest that only COVID-19 recoveries are influencing the performance of the index and the daily positive cases and fatalities are insignificantly related to the performance. Further studies can be performed by incorporating other variables such as economic growth, interest rate and inflation rate along with the COVID-19 related variables at a cross-country level.

The driving force behind adopting the idea of ​​sustainable development are producers and retailers. Unfortunately, when preparing their product range for consumers, many of them still only pay attention to the size of the customers’ earnings, how often they shop and how much they buy when shopping. In consumer segmentation, sustainable values ​​that consumers apply when making their purchasing decisions are rarely taken into account. The aim of this article is to show that values ​​such as environmental protection, the ethical behaviour of producers, fair trade or maximising the utility function of consumption are so important in the purchasing process in the coffee market that they can be used as segmentation variables. The Polish coffee market was selected to carry out segmentation taking into account the consumers' sustainable values. The main source of the article is the results of a standardized online survey conducted on a nationwide sample of 800 coffee consumers in July 2018. Multi-dimensional analyses such as extrapolative factor analysis (EFA) and cluster analysis (CA) were used to describe the obtained results. As a result, six segments of coffee consumers were identified and described: “responsible, aspiring to be connoisseurs”, “loyal coffee enthusiasts”, “pragmatic users”, “coffee laypersons”, “sophisticated connoisseurs”, “consumerists, connoisseurs, but not at any price”. Among the identified segments, the sustainable consumption values ​​most often mentioned refer to "responsible, aspiring to be connoisseurs", and the least often - ”consumerists, connoisseurs, but not at any price”. The conclusions presented in the last part of the article may be used by manufacturing and trade enterprises, operating on the coffee market, in order to respond to the identified needs and expectations of consumers and by governmental and social organisations so as to determine the directions of pro-ecological education of consumers.

This qualitative research investigates the intricate relationship between market dynamics and supply chain sustainability in the renewable energy sector. Amidst the global energy transition, renewable energy has emerged as a crucial player in mitigating climate change and fostering sustainable development. However, ensuring the sustainability of renewable energy supply chains presents complex challenges shaped by various market forces. Through semi-structured interviews and thematic analysis, this study explores how policy frameworks, technological innovation, market competition, consumer preferences, and investor demands influence sustainability practices across the renewable energy supply chain. The findings highlight the critical role of supportive policy environments, technological advancements, and stakeholder collaboration in driving sustainability performance and mitigating risks within renewable energy supply chains. Furthermore, the study identifies challenges such as supply chain complexity, regulatory compliance, and stakeholder engagement, underscoring the need for collaborative efforts and multi-stakeholder partnerships. By addressing these challenges and leveraging opportunities, stakeholders can advance sustainability goals, mitigate climate change impacts, and foster a more resilient and equitable renewable energy sector. The research contributes to a deeper understanding of market dynamics and supply chain sustainability in the renewable energy sector, offering insights for policymakers, industry stakeholders, and sustainability practitioners to inform evidence-based decision-making and drive positive change.

This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.

Market crises pose significant challenges for businesses, emphasizing the importance of effective crisis management strategies. Central to these strategies is the ability to identify and retain loyal customers, who often serve as the bedrock of stability during tumultuous times. This paper investigates the application of deep meta-learning analysis to predict loyal customers as a cornerstone of market crisis management. Drawing upon an extensive literature review, the study explores previous research on market crises, customer loyalty, and the evolution of deep learning and meta-learning in crisis management contexts. Methodologically, the paper outlines data collection, model development, and evaluation procedures tailored for deep meta-learning-based customer prediction. In results, the overall accuracy of model 85%, a precision 0.88, recall 0.82, and F1-score 0.85 were obtained. The analysis demonstrates the effectiveness of deep meta-learning models in accurately identifying loyal customers during market crises, offering insights into their performance and applicability compared to traditional methods. Practical implications include potential applications in crisis management for businesses and considerations for real-world implementation.

The present paper examines the impact of herding behavior on market capitalization in the Egyptian stock market before and after the disclosure of environmental-social-governance scores (ESG Score Index). This line of thought and research contributes to the literature on the interrelationships between ESG factors and market dynamics. Consequently, the evolving landscape of how to sustain investments in emerging markets like Egypt can be enlightened. Two main hypotheses were tested in the paper. The first hypothesis is on the expected significant impact of herding behaviour on market capitalization. The second hypothesis is on the expected positive impact of ESG on the relationship between herding behaviour and market capitalization. Panel data analysis using data extracted from financial statements of 22 Egyptian EGX 30 companies, over the period 2018-2023 will be used. This is to control for the cross-section and time characteristics of the research sample. The empirical findings reveal a positive significant effect of herding behavior on market capitalization before disclosing ESG scores. Another finding is that after the implementation of mandatory ESG disclosure, the relationship between herding behavior and market capitalization is strengthened. Moreover, it is empirically evident that companies with higher ESG performance attract more investments and higher valuations. The importance of ESG in shaping investment decision-making, enhancing transparency, and market dynamics has important policy implications.

This exploration delves into the intricacies of decision-making in business management. It covers decision-making models, influencing factors, real-world case studies, and strategies for effectiveness. Emphasizing the role of data-driven approaches, the exploration highlights the transformative impact of effective decisions on organizational success. Challenges and pitfalls are addressed, providing insights for overcoming obstacles. The importance of mastering decision-making is underlined as a strategic advantage in the competitive landscape of business management. In summary, this exploration offers a nuanced understanding, empowering leaders to make informed choices that shape organizational destinies.

This paper aims to measure the effects of delisting on stock returns for Vietnam stock market. The study employs a sample of 118 stocks that were compulsorily delisted from the market between January 2011 and December 2021. Using an event study methodology, the empirical findings confirm that the delisting has negative effects on stock returns in the Vietnam stock market. Specifically, results derived from tests show that the average abnormal return of delisted stocks continuously declines during three trading days following the announcement of delisting. Moreover, it is found that the differences in cumulative abnormal returns between post-delisting and pre-delisting periods are significantly negative for all tracking periods. Apart from the negative effect of delisting on stock abnormal returns, we also find that the impact of delisting on stock returns for smaller companies is greater than bigger companies.

In an increasingly changing world, critical thinking is one of the key skills that ensure organisations' competitive advantage. Thus, in higher education institutions there is an accelerating emphasis on developing critical thinking (CT) among students. This paper presents the results of three experimental courses (Pedagogy and didactics of financial accounting, Virtual Learning Environments in Economics, Business Communication), using a blended learning method, aimed at improving students' CT skills. The three courses were delivered by both higher education teachers and trainers from the labour market. After analysing the data from the three experimental courses, it will be possible to assess whether the intervention of labour market trainers contributed to the improvement of students' critical thinking skills, and, in particularly at the level of which components improvements have been identified.

The present study reports a critical review of NIO's business model, considering the evolving landscape of the electric vehicle market and servicing. The objective of this study is to develop a comprehensive framework that facilitates the identification of key elements characterizing a company's business model and highlights ongoing transformations, crucial for adaptation and survival in a rapidly changing environmental context. Focusing on the case study of NIO, a relatively young Chinese original equipment manufacturer (OEM) specializing in high-tech electric cars, the research delves into the challenging scenario of the Chinese electric vehicle market, which has recently faced a bubble in 2023. The market proliferation, supply chain disruptions, and price wars triggered by Tesla have resulted in a survival struggle for numerous automotive startups, leaving larger companies with increasing market shares. Despite facing adversities, NIO managed to secure a promising segment, catering to premium-range battery electric vehicles (BEVs), establishing a competitive advantage through differentiation. By pursuing ambitious investments, the company aims to create economies of scope and achieve cost leadership, venturing into new market sectors and vertically integrating the production chain. Given NIO's agility in adapting to market conditions, aggressive entry into new segments, and a strategic vision for the future, it serves as an excellent candidate for testing and validating the proposed framework. The research sheds light on NIO's trajectory and offers insights into its potential for sustained growth in the dynamic electric vehicle market.

The idea of the current research started from noticing a major gap in the customer behavior literature - researchers scarcely analyzed the impact of scandals on the customer purchase decision in the case of celebrity endorsement. Using a quantitative approach based on questionnaires, 101 people were asked about their opinions, the goals being to find out if people are influenced by the celebrity scandals, if people tend to have different reactions in terms of consumer purchase decision when it comes to various different negative actions carried on by the endorsers - will a murder upset the customers more than an infidelity scandal? - and if there are any differences in treating Romanian and foreign celebrities once they would enter into such a scandal.

The purpose of this paper is to identify factors that influenced customer satisfaction in the Moscow taxi market. The previous researches in customer satisfaction of transportation services laid the foundation of factor model. The resulting list of factors was revised by taxi market experts. The augmented model provided the basis for quantitative survey. The research sample included 310 respondents who have been using taxi services in Moscow during past 2 years. Survey data was analyzed using multiple choice regression method. Analysis results show significant differences between frequent and casual taxi users and a certain number of passenger experience factors that have no influence on customer satisfaction:for frequent users it is discount, for regular users - trip environment, and for rare clients - technical support&security and trip comfort.

In the dynamic landscape of Sabah's socio-economic development, the integration of AI technologies holds both promise and challenge. While AI has the potential to drive economic growth, enhance productivity, and elevate quality of life, it also brings forth concerns regarding job displacement, skill gaps, and unequal distribution of benefits. The latest research problem in the realm of AI and employment in Sabah revolves around ensuring inclusive and sustainable development amidst this technological disruption. This encompasses two key dimensions: skill development and education, and labor market dynamics. Addressing the research questions necessitates a multifaceted approach. Firstly, in the realm of skill development and education, there is a critical need to assess the adequacy of existing programs in equipping the workforce with AI-driven skills. Strategies such as promoting STEM education, fostering digital literacy, and facilitating lifelong learning initiatives, particularly among marginalized communities, are essential. Additionally, fostering collaboration between stakeholders and implementing curriculum reforms are pivotal steps in enhancing the alignment between educational curricula and the demands of an AI-driven economy.

In this paper, we consider an economy, in which consumers exhibit unrealistic optimism, and show that such an economy is more likely to undergo early recessions and underinvestment rather than experiencing bubbles or overinvestment. In this model, each consumer incorrectly believes that a negative aggregate preference shock will hit all other consumers, but not herself. When consumers have such an unrealistic optimism, the output price must fall prematurely before the aggregate shock occurs. The reason is that if the price does not plummet today, all consumers would postpone making purchases by falsely expecting that an arbitrage opportunity will arise in the future when the aggregate shock hits the economy, in which case, the output market cannot clear. We also show that the government has the potential to improve welfare of the economy by providing subsidies, if the government judiciously selects the right timing for intervention.

In this study, we investigate the growth of EV ownership among Swedish households. Through detailed socio-economic data and the Swedish vehicle register, we are able compare characteristics of households that bought an EV in 2016 with those that became EV owners in 2020. We are thus able to describe and analyse the transition from the early EV owners to the early majority. Unlike many prior studies in the field, which are based on surveys or interviews, this study relies on registry data, thereby encompassing all car owners in Sweden. In this study, we have also scrutinized the significance of ownership mode by distinguishing between households that have chosen to purchase a car and those that have chosen to lease one. The results show that the demand for EVs has reached new consumer groups, e.g. both younger and older car buyers, and that the option to lease a car privately has broadened the market for new cars in general, and for EV’s in particular.

In this study, we employ the GARCH-MIDAS (Generalised Autoregressive Conditional Heteroskedasticity variant of Mixed Data Sampling) model to explore the predictability of commodity return volatility in relation to US climate policy uncertainty (CPU). Our analysis utilizes the 20-day annualized realized commodity volatility returns of nine global commodities (Aluminium, Cocoa, Coffee, Copper, Cotton, Rice, Soybean, Sugar, and Wheat) to construct the GARCH-MIDAS model, with Climate Policy Uncertainty (CPU) serving as the predictive variable. The outcomes of our investigation reveal a consistently positive and statistically significant relationship between CPU and the selected commodities. This implies that CPU effectively serves as a robust predictor of volatility in commodity returns. The implications drawn from our results suggest that climate change policies play a pivotal role in influencing economic activities within the commodity market. This underscores the substantial impact of climate change considerations on investment decisions and further emphasizes the integral role of climate change in shaping economic choices.

Artificial Intelligence (AI) stands as an emerging technology poised to reshape various facets of our daily lives, with a particularly noteworthy influence on the labor market. In Morocco, AI adoption has commenced in diverse sectors, including finance, insurance, and telecommunications, opening up fresh avenues for employment. Nevertheless, this technological shift also ushers in challenges for Moroccan workers, necessitating training and upskilling to effectively engage with AI-driven systems. Within this article, we delve into the burgeoning impact of AI on the Moroccan economy and job landscape, delving into both the opportunities and obstacles that accompany its proliferation. Furthermore, we explore strategies that authorities and businesses can employ to harness the benefits of AI while mitigating its potential adverse effects on Morocco's workforce.

Food insecurity remains a critical challenge in developing countries like Ethiopia. Majority of its population derive their livelihood from agriculture characterized by mixed farming systems. Smallholder farmers the principal actors of agriculture are also facing a rapidly increasing population and urbanization had been unable to be self-sufficient in food. This paper examines the role of commercialization on food security and its determinants in rural settings. We used cross-sectional data from 295 households randomly selected in northwest Ethiopia. Results from bivariate Tobit regression revealed both crop and livestock commercialization improves household food security but in quite different ways. The income from crop sales was instrumental in allowing households to buy additional foods while livestock sales foster crop diversity for own consumption by allowing them to purchase nonfood items. Thus, agriculture-food security nexus may not require the production systems to be subsistent. Improving commercialization with better access to off-farm employment and institutional services (credit and market infrastructure), and enhanced awareness of the extension towards promoting commercialization and diversified food consumption seem to be more promising. The research paper concludes commercialization is vital not just for economic growth and food security, but also for the smallholder agriculture in providing additional nutrition.

This paper considers the theoretical framework of the consumption-based asset-pricing model and derives successive approximations of the modified basic pricing equation using Taylor series expansions of the investor’s utility function during the averaging time interval Δ. For linear and quadratic Taylor approximations we derive new expressions for the mean asset price, mean payoff, their volatilities, skewness and amount of asset that delivers max to investor’s utility. We introduce new market-based price probability determined by statistical moments of the market trade values and volumes. We show that the market-based price probability results zero correla-tions between time-series of n-th power of price pn and trade volume Un, but doesn’t cause statis-tical independence and we derive correlation between time-series of price p and squares of trade volume U2. The market-based treatment of the random trade price describes impact of the size of market trade values and volumes on price probability. Predictions of the market-based price probability at horizon T should match forecasts of statistical moments of the trade values and volumes at the same horizon T. The market-based price probability emphasizes direct dependence on random properties of market trades.

In every economy, Stock markets are part of the key elements the build it up. A few decades ago, there has been a significant change in Ghana stock market returns (GSE). Our study examines the statistical and economic significance of investor sentiment, based on weather conditions/changes, on stock market returns. OLS models, assisted by unit root tests were employed in analyzing the data obtained from the Ghana stock exchange platform from 2000 to 2017. From our literature review, we discovered that investors’ perceptions play a central role in finalizing the direction of stock market returns. Regarding our empirical results, we tested whether weather variations influence the investment decisions of investors; we discovered that temperature and cloud cover significantly influences stock market returns. This is because of mood changes is associated with weather conditions variations. However, sunshine per our regression coefficient shows a statistically insignificant impact on investors’ investment choices. Precipitation to a large extend influence stock market activities further affecting its results negatively as our regression results depicted. We concluded stock brokerage firms, companies, and investors (foreign/local) must incorporate weather changes/effects when strategizing about their investment outcomes.

Pearl millet is an important food crop in the arid and semi-arid tropical regions of Africa and Asia. These regions are home to millions of poor smallholder’s households living in harsh agro ecology and reported higher prevalence of malnutrition. Such poor households have few options in terms of food crops, besides the limited markets. Indeed, pearl millet is one of the food crops they continue to grow for their food and nutritional security. Pearl millet is important sources of dietary carbohydrates, energy, protein, and important minerals such as calcium, iron and zinc. Considering inherent high nutritional values and climate resilient nature (drought and heat), demand for pearl millet as food, beside valued for its Stover as a source of livestock fodder, is projected to grow strongly in Asia (India) and Africa (West and Central Africa). Iron (cause anemia) and zinc (cause stunting) deficiencies are widespread and serious public health problems worldwide, including India and Africa. Biofortification is a cost-effective and sustainable agricultural strategy to address this problem. Research on pearl millet has shown that large genetic variability (30-140 mg/kg Fe and 20-90 mg/kg Zn) available in this crop can be effectively utilized to develop high-yielding cultivars with high iron and zinc densities. Both Open –pollinated varieties (Dhanshakti and Chakti) and hybrids (ICMH 1202, ICMH 1203 and ICMH 1301) of pearl millet with high grain yield (>3.5 tons/ha in hybrids) and high levels of iron (70-75 mg/kg) and zinc (35-40 mg/kg) densities have been developed and released. Currently, India growing >70,000 ha of biofortified pearl millet, besides more pipeline hybrids and varieties are under various stage of testing at the national (India) and international (west Africa) trials for possible release. Genomic tools will be an integral part of breeding program particularly for nutritional traits to use diagnostic markers and genomic selection. Clinical studies showed that 200g grains from biofortified cultivar would provide bioavailable Fe to meet full recommended daily allowance (RDA) in children, adult men and 80% of the RDA in women. Till today, no markets to promote biofortified cultivars/grains/products as no incentive price and such products aims to address food and nutritional security challenges simultaneously. The demand is likely to increase only after investment and integration into modern public distribution system, nutritional intervention schemes, private seed and food companies with strong mainstreaming nutritional policies. In the non-traditional regions, this will contribute to livestock and poultry feed industry as spill-over benefits to improve nutrition.

The article addresses the issue of consumer preferences for types of coffee in Poland (ground coffee, instant coffee, coffee beans, instant coffee mixes and instant cappuccino). The main source of the study is the results of primary research conducted using a structured interview (CAPI—Computer Assisted Personal Interview) on a nationwide sample of 800 coffee consumers. The research was conducted by Grupa IQS Sp. z o.o. from Warsaw in January 2016 for the MOKATE Group based on the assumptions and guidelines developed by the authors of the article. The main purpose of the article is to present the identified coffee consumer preferences in Poland with regard to various kinds of coffee. The results of the research which have been included in the article indicate, among others, ongoing changes in the preferences of Polish consumers regarding their coffee choices. They prove that Polish consumers are drinking ground coffee more and more often. The consumption of instant coffee is decreasing.

Background: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as business-to-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issues.

The sustainability of social security is increasingly important to countries worldwide, yet the impact of regulation on social security remains controversial. Most scholars and policymakers focus primarily on the influence of social regulation on social security, while to varying degrees neglecting the effects of market regulation. This study employs data on the relaxation of market regulation (RMR) from the Marketization Index of China’s Report (MICR) and balanced panel data for 31 provinces in China from 2008 to 2019. Using instrumental variable methods to address endogeneity issues in the model, we empirically evaluate the impact of China's RMR on the level of social security (SSL) and its underlying mechanisms. The conclusion indicates that for every standard deviation increase in the intensity of RMR by local governments, there is a 2.25% increase in SSL. The study also found that social security expenditure and efficiency serve as mediating variables for the impact of RMR on SSL, but it did not support the mediating role of social security fairness. These findings offer valuable insights for optimizing regulation and social security reform in China, as well as useful references for other emerging market economies in identifying priorities for their coordinated regulatory and social security reform.

Thanks to the Chinese government's strong monetary stimulus initiative, the Chinese stock market has seen a recent surge. A similar scenario in history reminds us that such a surge carries significant potential risks and undermines economic sustainability. This study examines the economic stimulus by the Chinese government during 2008 and 2015 utilizing monetary and real estate policy to stimulate stock market expansion, and identifies a pattern of path dependency in the government's approach to economic challenges. In the context of the fiscal pressures of 2024, the government has again employed an extraordinary level of intervention to catalyze a significant uptick in the stock market. While this has led to a temporary increase in stock market indices, it poses substantial risks. The potential for a stock market bubble, detached from fundamental values, could precipitate a subsequent market collapse. Additionally, the government's market stimulus may be aimed at providing financial support to underperforming state-owned enterprises, which in the long-run will damage China's economic sustainability. There is also a concern that the measures to stimulate the stock market could interlink the bond market and banking sector with the equity, potentially inciting broader systemic risks. Furthermore, the housing market crisis and investors' skepticism towards the extent of China's marketization reforms will also intensify. Overall, we believe that the current round of stock market stimulus is not sustainable and leaves the door open for future risks.

This article examines the pressing challenges facing Industrial Organization (IO) in the contemporary economic landscape, with a particular focus on merger analysis. The rapid pace of technological advancement and emergence of novel business models have strained conventional analytical frameworks. We explore how the rise of digital platforms, data as a competitive asset, and multi-sided markets have exposed gaps in our understanding of market power and consumer welfare. Through an examination of empirical methodologies, including structural models and difference-in-differences approaches, we highlight the limitations of current techniques in accurately predicting merger outcomes. The paper argues for a disciplinary reinvention of IO, drawing upon insights from behavioural economics, network theory, and data science. We posit that an interdisciplinary approach is essential for developing more robust analytical tools capable of navigating the complexities of modern markets and informing effective antitrust policy in an increasingly interconnected global economy.

As is well known, limiting carbon emissions is an important link in mitigating global climate change. Carbon quotas are a widely used policy tool by governments around the world, without increasing the financial burden on the government. To study the impact of carbon quota policies on the clean transformation of the key carbon emitting industry - the power industry, we established a duopoly model and conducted analysis and numerical simulation. Research has found that the effectiveness of carbon quota policies is closely related to the level of competition within the power supply chain, and is also influenced by the market share of clean energy power generation enterprises.Moreover, in some cases, it may have the opposite effect.

We are currently witnessing a strong global transition towards new, more sustainable models of development and consumption. This transition is both activating and highlighting a series of discrepancies between the various actors in agri-food marketing systems, including the institutions that govern regulatory and trade aspects. These discrepancies highlight how, on a global level, the agri-marketing system is unable to provide adequate responses to the new principles of sustainability that are increasingly expected by civil society. This inability is often linked to the phenomena of opportunism, information asymmetries and lock-in effects (resulting from the slowness or inability of some sectors or actors to evolve technologically and culturally). The control of all these factors is often ineffective and often involves high transaction costs . These conditions lead the global market into an ongoing state of failure, creating structural holes, in which new forms of exchange are born, in response to the principles mentioned above. We identify these as nested markets: hybrid market forms that, thanks to new information technologies, create a new form of proximity that transcends the classical concept of physical proximity. This new proximity directly links producers and consumers in relationships based mainly on two substantial values: reciprocity and reputation. Both of these values significantly reduce transaction costs which today play an increasing role in determining the form and repetition of exchanges. This condition is difficult to interpret from classical and neo-classical economic theories. To do so it is necessary to use a multidisciplinary approach. In this article we combine neo-institutional theory and network analysis to interpret phenomena that are increasingly emerging in all areas of the world. Using these two theories, and case studies from Brasil, North America, China and Europe, we try to highlight how the market is not only the place where the price of products and of their quantities are formed. Market is a complex social spaces, where more-or-less stable relationships are formed, based on the values of reciprocity and reputation that determine behaviour and codes of conduct aimed at containing opportunism and encouraging sustainable, autonomous, forms of self-regulation. These relationships are also the driving forces that ensure the ongoing continuation of the exchanges and the diffusion and preservation of knowledge over time and space. These relationships represent the beating heart of nested markets and their dynamic ability to adapt to the continuous changes that occur in the socio-economic and environmental context in which they are rooted. In this way sustainability is built as an expression of the co-production between different actors (producers, consumers, institutions) and natural resources.

Keywords: Forest management; sustainability; FSC; PEFC; research; market share

Renewable energy sources are increasingly crucial for meeting global energy demands in an eco-friendly manner, particularly as the world shifts away from fossil fuels and faces higher volatility of the market prices. Portugal, a country with limited fossil fuel reserves and high energy dependency, has heavily invested in renewables in recent years. However, renewables like sunlight, wind, and water are inherently unsteady due to climate influences, resulting in energy production fluctuations. To mitigate this issue, energy storage systems are being deployed to stabilize renewable energy supply during production lulls. This project aims to assess an energy storage system and define technical specification in order to maximize profitability at the Finerge-owned Alto Douro wind farm. To achieve this, we've conducted an energy demand analysis, followed by simulations to assess system performance in relation to energy use and profit, while varying parameters such as power and efficiency. We've also compared commercial proposals from different battery manufacturers utilizing various technologies. Our findings suggest that high-power, quick charge-discharge lithium-ion batteries are the most suitable solution, given their efficiency and modularity. These attributes are vital for achieving current and future profitability.

Financial bubble prediction has been a significant area of interest in empirical finance, garnering substantial attention in the literature. This study aimed to detect and forecast financial bubbles in the Vietnamese stock market from 2001 to 2021. The PSY procedure, which involves a right-tailed unit root test to identify the existence of financial bubbles, was employed to achieve this goal. Machine learning algorithms were then utilized to predict real-time financial bubble events. The results revealed the presence of financial bubbles in the Vietnamese stock market during 2006-2007 and 2017-2018. Additionally, the empirical evidence supported the superior performance of the Random Forest and Artificial Neural Network algorithms over traditional statistical methods in predicting financial bubbles in the Vietnamese stock market.

Today's unstable labor market increasingly requires flexibility and adaptability to cope with the threat of unemployment. It can cause distress in people and have a more significant negative impact on fragile workers, such as migrants. This study aimed to test whether a Career Counseling intervention designed for Migrants (CCfM) can develop Career Adaptability and, therefore, both Work Self-efficacy (WSe) and Job Search Self-efficacy (JSSe) perceptions. It was conducted in Italy and involved a sample of 233 migrants, who were asked to respond to a questionnaire available in three languages (Italian, French, and English). Data analysis showed that an improvement was demonstrated in all the variables considered, namely career adaptability (including concern, control, confidence, and curiosity), WSe, and JSSe, even though the CCfM was not directly designed to increase the last one. In addition, the development of career adaptability explained the increase in migrants' WSe and JSSe, and the initial level of career adaptability was found to explain the increase in WSe due to the initial positive level of curiosity.

Market air quality is very important to the economic lives of the people which is rarely researched, however, market activities particularly pedestrian traffic releases particulates which is detrimental to the health of the users and stakeholders. Thermo scientific MIE pDR-1500 particulate was used to monitor the quality of air within the market for eight (8) weeks, air pollutant of concern is PM2.5. ten (10) sample points were located in the market which covers ten (10) sample points for pedestrian traffic to represent the entire market environment spectrum. The analysis of PM2.5 measured daily during dry and wet season shows a clear seasonal variation of this particular pollutant as elevated concentration was measured during the dry season than the wet season. The assessment of PM2.5 concentration shows exceedances of the standards stated by WHO and NAAQS during the dry season which ranges from 47.9 μg/m3- 231.88 μg/m3 in the morning and 65.17 μg/m3- 1806.33 μg/m3 in the afternoon. From the findings, pedestrian traffic contributes immensely to air pollution in an open market, with this elevated concentration, prolonged exposure is highly detrimental to health. This study creates awareness to the pedestrians in an open market about air pollution and informs policy changes.

The pandemic caused by SARS-CoV-2 virus obstructed the Chinese economy and has expanded to the rest of the world at a rapid pace affecting at least 215 countries, areas and territories. The advancement of the disease and its economic repercussions is profoundly ambiguous, making it challenging for policymakers to formulate suitable microeconomic and macroeconomic policy responses. The scenarios in this paper illustrate how an outbreak could significantly affect the global economy in the short run. It has been estimated that each additional month of crisis would cost from about 2.5-3% of the global GDP and that the GDP growth would take a blow, reaching about 3-6%, depending on the country. Scenarios also suggest that GDP can drop by more than 10% and even exceed 15% in some countries. Via addressing the economic consequence of COVID-19 in different industries and countries, the paper presents assessments of the likely global economic costs of COVID-19 and the GDP growth of different countries. Economies will be negatively affected because of the high number of jobs at risk. Countries highly dependent on foreign trade are more negatively affected. Given that disease and its economic influence are highly unpredictable in numerous aspects, the global economy at the moment is the most critically threatened in history.

This study examines the effect of market risk on the financial performance of 31 non-financial companies listed on the Casablanca Stock Exchange (CSE) over the period 2000-2016. We utilize three alternative variables to assess financial performance, namely return on assets, return on equity and profit margin. Next, we use the degree of financial leverage, the book-to-market ratio, and the gearing ratio as market risk variables. Besides, we employ the pooled OLS model, the fixed effects model, the random-effects model, the difference GMM and the system GMM models. The results show that market risk indicators have a negative and significant influence on the companies' financial performance. The elasticities are greater following the book-to-market ratio compared to the degree of financial leverage and the gearing ratio, respectively. In most cases, the firm size, the tangibility ratio, and the cash holdings ratio have a positive effect on financial performance, whereas the firms' age, the debt-to-income ratio, stock turnover, and leverage hurt the performance of these non-financial companies. Therefore, decision-makers and managers should mitigate market risk through appropriate strategies of risk management, such as derivatives and insurance techniques.

Forest certification is considered a viable market-based policy instrument to promote forest sustainability. It has an important role of play in meeting the objective of modern forestry development in China, which is to sustain ecological and environmental benefits of forests. To understand differences in attitudes, opinions, and interests in forest certification, this study segmented respondents of a landowner’s survey in Shandong, China based on their level of interest in participating in forest certification under different program requirements. Multivariate cluster analysis revealed three distinct groups: likely-, potential-, and unlikely-landowners. We further examined the heterogeneity of these groups in terms of their demographics, ownership characteristics, management objectives, and perceived benefits and challenges with adopting forest certification. The results suggested the necessity of differentiating landowners in formulating and designing specific motivation-based incentives and tailor outreach efforts and communication strategies to improve their interests in forest certification. Findings are useful and interesting to forest policymakers interested in promoting forest certification among landowners in China and other countries facing similar circumstances.

In this paper, we focus on the impact of financial liberalization on stability of emerging stock market. We identify crises in a group of Latin American (Argentina, Brazil and Chile) and Asian countries (Philippines, Korea, Taiwan and Thailand) during 1975–2005. This paper aims to apply the methodology of CMAX method. Our results indicate that liberalization triggers more unstable stock market in the short run and generate several crises. Still, liberalization seems to generate more stable financial markets in the long run. Financial liberalization does not increase the frequency of crises in emerging countries and at long-term, crises are less several.

Market dynamics is based on classical electrodynamics and uses quantum electrodynamics as a reference model framework. Its construction is always based on Bourbaki structuralism. Market dynamics studies the interaction between price, demand and supply. Quantum electrodynamics studies the interaction between photons, electrons and positrons. Both belong to the category of single charge dynamics and share a symmetry group. Market dynamics introduces market charge and defines demand and supply respectively. By examining the market hesitation phenomenon, spin is introduced as the intrinsic property of demand and supply. Therefore, demand and supply can be defined by Dirac spinors. By introducing the coin cone and valence shell, virtual prices are defined; demand and supply interact by exchanging virtual prices. These interactions can be described by Feynman diagrams. The cognitive field has the function of polarizing decision-making and is described by the magnetic field. Market dynamics believes that the social economy itself is an experiment and every market participant is an observer. We established the non-commutative relationship of market observation and the market version of the uncertainty principle of observation information, and defined the quantum version of the invisible hand. On the basis of classical electrodynamics, the relationship between price field and Maxwell field is discussed, and the concept of gauge potential is introduced. Market fluctuations (including mesoscopic and quantum fluctuations) caused by high-order cognition (including gaming, decision-making and reasoning). The fluctuation phenomena inherent in various markets are the basis for understanding both the cognition of market participants and the phase transition of the market. Six components of the market wave function are defined, namely the global phase factor, global gauge potential and global gauge field strength, as well as the local phase factor, local gauge potential and local gauge field strength. Gauge transformation is a integral part of quantum electrodynamics and market dynamics. The gauge principle is then given to show that global symmetry is a necessary condition for local symmetry. Four operators are introduced, namely the Newton mean operator, the Einstein cone operator, the Schrödinger evolution operator and the Dirac field theory operator. These four operators are applied to the market wave function respectively, presenting four operator phases of the market wave function. The change of operator state is called the phase transition of the market wave function.

Data on antimicrobial resistance in Burkina Faso’s agricultural sector is still limited. The study assessed the occurrence of extended spectrum β-lactamase -producing Escherichia coli (ESBL-Ec) and Klebsiella spp. (ESBL-K) in lettuce, environment, and gardeners’ stools in market gardens in Ouagadougou, Burkina Faso. A total of 356 samples were collected from three vegetable gardens (76 lettuce, 76 soil, 62 manure, 63 irrigation water and 79 human stools). ESBL-selective medium was used for initial selection of ESBL-producing bacteria, and the isolates were further identified using biochemical tests. Antibiotic susceptibility test was performed using the disk diffusion method. The overall prevalence of ESBL-Ec and/or ESBL-K in the samples was 232/356 (65.2%). Of the lettuce samples, the prevalence of ESBL-Ec was 19/76 (25.0%) and ESBL-K 33/76 (43.4%). In the market gardens environment, the prevalence of ESBL-Ec was 32/201 (15.9%) and ESBL-K 124/201 (61.7%). In the gardeners’ stools the prevalence of ESBL-Ec was 42/79 (53.2%) and ESBL-K 24/79 (30.4%). Two ESBL-K isolates were found to produce NDM carbapenemase. Due to the high prevalence of ESBL-producing bacteria, which may also be carbapenemase producers, it is necessary to monitor pathogens in agricultural products with a "One Health" approach to limit and prevent infections in the population.

This study investigates the strategic unity of fintech enterprises, assessing whether their characteristics bear greater resemblance to conventional banking institutions or technological corporations. Through the evaluation of operational functions, target demographics, business frameworks, stakeholder engagements, equity market dynamics, and empirical data, the analysis indicates that fintechs exhibit a closer alignment with technology companies. The inquiry uncovers significant lacunae in the prevailing scholarly discourse, particularly regarding the examination of fintechs’ fundamental attributes and their ramifications for strategic positioning. The results not only imply that fintechs are more akin to technology firms but also underscore the increasing convergence between fintechs and tech enterprises, propelled by the incursion of Big Tech and a heightened focus on sustainability. This implies an evolving financial landscape where traditional categorizations are progressively obscured. Our research is pivotal for comprehending and navigating this emergent terrain, which holds paramount importance for professionals operating within the financial and technological domains.

We use Fourier analysis to access risk in financial products. With it we analyze price changes of e.g. stocks. Via Fourier analysis we scrutinize quantitatively whether the frequency of change is higher than a change in (conserved) company value would allow. If it is the case, it would be a clear indicator of speculation and with it risk. The entire methods or better its application is fairly new. However, there were severe flaws in previous attempts; making the results (not the method) doubtful. We corrected all these mistakes by e.g. using Fourier transformation instead of discrete Fourier analysis. Our analysis is reliable in the entire frequency band, even for frequency of 1/1d or higher if the prices are noted accordingly. For the stocks scrutinized we found that the price of stocks changes disproportionally within one week which clearly indicates speculation. It would be an interesting extension to apply the method to crypto currencies as these currencies have no conserved value which makes normal considerations of volatility difficult.

This paper shows that the empirical distribution of cross-sectional analyst coverage in China's stock markets follows an exponential law in a given month from 2011 to 2020. The findings hold in both the emerging (Shanghai) and the developed market (Hong Kong). Moreover, the unique distribution parameter (i.e., mean) is directly related to the amount of market-wide information. Average analyst coverage exhibits a significant negative predictive power for stock-market uncertainty, highlighting the role of security analysts in diminishing the total uncertainty. The exponential law can be derived from the maximum entropy principle (MEP). When analysts, who are constrained by average ability in generating information (i.e., the first-order moment), strive to maximize the amount of market-wide information, this objective yields the exponential distribution. Contrary to the conventional wisdom that security analysts specialize in the generation of firm-specific information, empirical findings suggest that analysts primarily produce market-wide information for 25 countries. Nevertheless, it remains unclear why cross-sectional analyst coverage reflects market-wide information, this paper provides an entropy-based explanation.

With the deepening of the diversification and openness of financial system, financial vulnerability, as an endogenous attribute of financial system, becomes an important measurement of financial security. Based on network analysis, we introduce network curvature indicator improved by Copula entropy as an innovative metric of financial vulnerability. Compared with previous network curvature analysis method, CE-based curvature proposed in this paper can measure market vulnerability and systematic risk with significant advantages.

The purpose of this research was to examine the act of business schools in the Palestinian universities by their study plans in achieving sustainable development goals (SDGs) (G1, G4, G8, G9, G12, G16, G17), due to their strong relationship with business sector in the local market, in addition to examining whether the graduates of business schools have the required competencies and required skills for the local market. This research is descriptive in nature and a survey questionnaire was designed to collect data from faculty members and employers. The results indicate that business schools in the Palestinian universities have incorporated sustainable development goals in their study plans to a medium-to-high degree, with a higher emphasis on Quality Education. Graduates from these programs are perceived to possess the required competencies and skills by the employers in the local market, with a medium-to-high level of alignment with the SDGs, especially in the area Quality Education. There was no significant difference between the perceptions of university faculty members and employers in the local market regarding the fit of business school graduates' competencies and skills with the local market needs in relation to the SDGs. However, there were significant differences in the areas of accountability and transparency principles, economic and industrial challenges, partnerships for the goals, international standardization, and capacity building in operational expertise. It is recommended that business schools in Palestinian universities continue to prioritize the integration of sustainable development goals in their study plans and align the competencies and skills of their graduates with the needs of the local market. The areas of significant difference between university faculty members and employers in the local market present opportunities for further collaboration and dialogue to better meet the needs of the local market and achieve sustainable development goals.

With the increased liberation of trade and fierce competition in the automotive industry, Proton needs to embark on a strategic reform to survive and remain relevant in the challenging market. This case study aims to explain the effectiveness of the Post Geely acquisition's ten years master plan to enhance Proton's self-reliance and global competitiveness. This case suggests that while the company's ten-year Master Plan is holistic and shows positive results in the early years, there is a need for a new business strategy, given the challenges imposed by climate change's impact on the automotive industry moving forward. The potentials of the Electric Vehicles (EVs) market and the Internet of Things (IoT) to develop the necessary long-term competitive advantage. A market strategy integrating the 4Ps and 4Cs framework suggests a two-dimensional convergence of the market mix, incorporating the organization's market strategy with a more consumer expectations-centric and user experience. Strategic integration of Geely's technological assets in developing Proton's technical know-how can address Proton's challenges and opportunities ahead.

We have implemented quantum modeling mainly based on Bohmian Mechanics to study time series that contain strong coupling between their events. Compared to time series with normal densities, such time series are associated with rare events. Hence, employing Gaussian statistics drastically underestimates the occurrence of their rare events. The central objective of this study is to investigate the effects of rare events in the probability densities of time series from the point of view of quantum measurements. For this purpose, we first model the non-Gaussian behavior of time series using the multifractal random walk (MRW) approach. Then, we examine the role of the key parameter of MRW, λ, which controls the degree of non-Gaussianity, in quantum potentials derived for time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for rare events. Thus, rare events can generate a potential barrier in the high-frequency region of the quantum potential, and the effect of such a barrier becomes prominent when the system transverses it. Finally, as an example of applying quantum potential beyond the microscopic world, we compute quantum potentials for the S&P financial market time series to verify the presence of rare events in the non-Gaussian densities and demonstrate deviation from the Gaussian case.

The use of improved agricultural technologies has been reported as the major strategy to increase agricultural production, increased household incomes and addressing poverty. However, adoption of such improved technologies by smallholder coffee farmers has been slow and this has contributed to the low coffee productivity in the country and the poor performance of the coffee sub sector. The study was meant to examine the effect of institutional factors including extension services, access to market and access to credit on technology adoption among smallholder coffee farmers in Kanungu District, Uganda. The study collected both quantitative and qualitative data from 289 smallholder coffee farmers and 8 key informants respectively in major coffee growing sub counties in Kanungu district. The study revealed that access to extension services and access to market have a positive significant influence on agricultural technology adoption while access to market has no significant influence on agricultural technology adoption. The study concluded that there is a positive significant relationship between institutional factors and technology adoption among smallholder coffee farmers in Kanungu district.

The European Bioeconomy Strategy aims to strengthen and boost biobased sectors, unlocking investments and markets while rapidly deploying local bioeconomies across Europe and improving compliance with environmental and social sustainability goals. Current biomass provision structures and infrastructure might not be able to tap the sustainable potential of forestry-, agricultural residues and biogenic waste envisaged forming the biogenic feedstock base of the Circular Bioeconomy of tomorrow. Therefore, for the present paper, we assess mobilization strategies, their current status, opportunities, and barriers for local low value and heterogenous biomass resources. Based on discussions with bioenergy supply chain experts, we cluster mobilization measures into three assessment levels; the legislative framework, market structures and technological innovation. Scientific literature research on the respective keywords is performed, the European policy landscape mapped, and the results are enriched with anecdotal evidence, especially for recent and running projects and market developments that lack in published track records. We can identify research needs on all three assessment levels. Still, technological development and legislative frameworks are providing support for heterogeneous biomass mobilization. Market creation, however, represents a bottleneck. We provide novel perspectives, how physical- and virtual bio-hubs and crediting stake- and shareholder variety could create added-value based on sustainable primary economic activities and their cascading activities.

The purpose of this study is to examine the driving model of determinant factors that affects the performance of small and micro enterprises empirical evidence from Amhara Region, Ethiopia. The study used primary and secondary data from manufacturing, construction, urban agriculture, trade, and service entrepreneurs found in Bahirdar, Dessie & Gonder Cities using a purposive sampling technique. It also used the descriptive research design with a self-administered survey questionnaire. The Statistical analysis tools, SPSS and Amos, software were applied to analyze the data. Multiple regression model results revealed that access to credit, initial capital, working premises, industry category, market linkage, ICT adoption have a positive relationship and a major impact on the Amhara region's enterprise performance. Hence, the outcome variable, enterprise performance, is highly affected by all predicted variables. As the study finding shows, a lack of access to credit, , market linkage, working premises), initial capital, and information communication Technology problems are critical challenges of SMEs. The Regional Government, TVED Bureau, Micro Finance Institutions, , should take corrective action to provide sufficient credit and long-term loans to protect inflation. Moreover, this study proposed a sustainable cluster management approach that solves their challenges in an integrated way.

Agricultural extension and advisory services in Africa have significant impact on food security as well as economic and social development. Recent moves towards a pluralistic delivery system, including the emergence of private-sector led initiatives in many countries are subject of policy and academic discourse. Our study used an adapted, fit-for-purpose market systems development framework to review available research in extension and advisory services in selected sub-Sahara Africa countries. Using a literature survey methodology, we report evidence of multiple actors in extension delivery, findings that point towards evolution towards mixed delivery as well as objectives. While there are significant uptake of cost-recovery approaches among commercially-oriented farmers, many smallholder farmers still depended on donor-funded services. Our review adds to existing knowledge through incorporating a market systems development framework, which extends the often-used willingness to pay approach, and highlights the need for merger of the public and private-sector objectives to achieve developmental outcomes.

The utility of ICTs for providing market information to rural smallholder farmers is growing rapidly, and access to reliable information and sources is considered crucial for beneficial market interaction. This study explored critical factors contributing to usage of electronic sources for market information search among rural smallholder livestock farmers. Using data collected from 129 respondents through a non-random sampling technique; descriptive and regression analysis was applied to identify key factors responsible for their awareness and use of ICT-based market information sources. Level of education was found to be a driver of awareness of ICT-based sources, and use of these sources was influenced by farmer-specific characteristics such as household size, education, income, membership of cooperatives and herd-size. The key ICT tools used was radio and mobile phones, widely available in the study area. Identified constraints to use of these ICTs include cost and patchy network signals in some areas. Policy interventions to reduce cost of mobile phone services and expansion of base stations; including practical recommendations for improved programming in radio and television offerings, are considered indispensable for greater uptake of e-information sources among smallholder livestock farmers.

Descriptive research design was used to select 200 respondents (i.e., 100 from each study places) following purposive sampling technique on two study areas (Market; Living). Personal observation and interview with retailers were done to triangulate the collected data from the formal survey. The collected data was analyzed using SPSS version 20 software program. Descriptive statistical measurements, Index and multinomial regression model was calculated to assess the relationship between customers point of emphasis during packaged water buying and respondents demographic variables. Majority of the respondent who frequently purchase packaged water generated a monthly income between 5000 and 10000 Birr. Television and radio was ranked first compare to other Media. Living place customers were more health concerned as compared to market place customers. Market place customer’s primarily give emphasis to price of packaged water. Almost all (97%) customers did not have the awareness towards packaged water standards. Only 86(43%) of the total respondents checked the chemical composition, of which 74(85%) of the respondents did not understand it. Customers sex, educational level and health status showed significant relationship with choice of packaged water quality -1.42(p<0.05), price -2.45(P<0.01) and health status -1.80(P<0.05) in market place and living places, respectively.

Increasing market participation among smallholder farmers have a big potential to uplift living standards of poor through increasing production and consumption pattern. Although, smallholder farming made 95% of total crop production in Ethiopia, they are exposed to a marketing bottleneck that hinders benefits from their produce. The objective of this study was analyzing factors determining smallholder Teff farmer decision to participate in output market and level of marketed output.The study used data from 190 respondents from four selected Teff dominant kebeles of Gena-Bossa districts in Dawro Zone, through structured questioner. This investigation was imperative because no adequate research has been done in study area in examining the hindering factors of farmers’ market participation. Moreover, in the prior study, different authors come up with varied outcomes in diverse country and geographic location concerning poor farmers’ market participation decision. The descriptive statistics and Heckman two stage econometric methods were employed to analyze data collected from sampled household. The significance of coefficient of inverse Mill’s ratio ( ) indicates the presence of self-selection bias and the effectiveness of applying Heckman two stage model. The results of study show that the smallholder decision to participate in output market were positively influenced by size of land holding, availability of family labor force, education status of household head, accessibility of credit service and access to market price information. On the other hand, size of family member, sex of house hold head being female and distance to market place discourage probability of Teff farmer market participation decision. Moreover, the second stage estimation reveals that, the education status of house hold head, size of farm land, amount of Teff crop produced, accessibility of market information, the size of family labor force and being member to farm cooperative increase the quantity of marketable output, whereas, large number of family size decline the level of Teff crops marketed. The policy that assist poor farmers in obtaining market skills; create affordable credit service; strengthen community based producer groups and capacitating the females socially and economically in the community believed to minimize the problems encountering small farmers in a way to market their crop

Time irreversibility, i.e. the lack of invariance of the statistical properties of a system under time reversal, is a fundamental property of all systems operating out of equilibrium. Time reversal symmetry is associated with important statistical and physical properties and is related to the predictability of the system generating the time series. Over the past fifteen years, various methods to quantify time irreversibility in time series have been proposed, but these can be computationally expensive. Here we propose a new method, based on permutation entropy, which is essentially parameter-free, temporally local, yields straightforward statistical tests, and has fast convergence properties. We apply this method to the study of financial time series, showing that stocks and indices present a rich irreversibility dynamics. We illustrate the comparative methodological advantages of our method with respect to a recently proposed method based on visibility graphs, and discuss the implications of our results for financial data analysis and interpretation.

2017 was an excellent year in terms of new drugs (chemical entities and biologics) approved by the FDA, with a total of forty-six. In turn, one of the highlights was the number of peptides (six) included in this list. Here, the six peptides are analysed in terms of chemical structure, synthetic strategy used for their production, source, biological target, and mode of action.

The purpose of this paper is to apply China’s economy growth prospects and its potential drivers of future. China's fast rise and its growth model have accelerated important existing structural trends in the global economy and made them decisive characteristics of the world economy. China's role in the world economy over the coming decades, an exercise which would not be possible without an investigation of the prospects for China's continued economic rise. On the one hand, China is a large export market for the United States. A lot of U.S. firms use China as the final destination of assembly in their global supply chain networks. China’s huge holdings of U.S. Treasury securities support the federal government finance its budget failures. However, some analysts contend that China consolidates a number of distortive economic policies such as protectionist industrial policies and an undervalued currency that undermine U.S. economic interests. They warn that efforts by the Chinese government to promote indigenous innovation, often through the use of subsidies and other distortive measures, could negatively affect many leading U.S. industries

Federated unlearning represents a sophisticated evolution in the domain of machine learning, particularly within federated learning frameworks. In financial applications, where data privacy and security are paramount, federated unlearning allows institutions to selectively remove or “unlearn” specific data from trained models without compromising their overall performance and accuracy. This capability is essential for ensuring that models remain adaptable, secure, and compliant with regulatory requirements, while minimizing the need for expensive retraining. In this paper, we explore various financial applications where federated unlearning can have a significant impact, including fraud detection, portfolio management, and credit risk modeling. By allowing targeted removal of outdated, erroneous, or sensitive data, federated unlearning enhances the agility of financial models, enabling institutions to keep pace with the dynamic financial landscape. Using practical numerical examples, we demonstrate how unlearning improves model accuracy and decision-making while maintaining data privacy across distributed systems. This paper underscores the critical role of federated unlearning in addressing the challenges of modern financial institutions, offering insights into its practical applications and future potential.

The new digital infrastructure has become an important basis for the green development of agriculture. This paper selects provincial sample data from 2006 to 2022, constructs an indicator system to measure new digital infrastructure and agricultural green total factor productivity, and explores how new digital infrastructure can promote agricultural green development. The results show that: (1) the new digital infrastructure has a significant role in promoting the green development of agriculture, and this conclusion is still valid after processing endogeneity and heteroscedasticity; (2) Technology and land factors play an intermediary role in the promotion of green agricultural development by the new digital infrastructure, that is, the new digital infrastructure helps to improve the quality and technical level of cultivated land and thus promote green agricultural development; (3) Labor force factors optimize the mediating role of technology factors, that is, the higher the quality of labor force, the stronger the role of the new digital infrastructure in promoting the green development of agriculture through the technological path; (4) The effect of new digital infrastructure on the green development of agriculture is obviously heterogeneous, and the effect is more obvious in northwest China and the regions with lower marketization level, higher environmental regulation level. This study is helpful to clarify the relationship between new digital infrastructure and agricultural green development in China, and provide corresponding policy suggestions for promoting regional agricultural green development.

Liquidity providers for asset-pair pools with constant value distri- bution, such as Balancer pools, experience some impermanent loss whenever there is price divergence in the tokens’ fiat values. In this paper, we model this impermanent loss for geometric mean invariant pools by deriving a function of two parameters. We analyze our function graphically, showing that unevenly distributed pools with large weighting disparities best mitigate the risk of impermanent loss. We conclude by providing a general protocol for liquidity providers to choose a suitable pool based on their risk tolerance and profit goals.

The gut microbiota plays an important role in the physiological activities of the host and affects the formation of important economic traits in agricultural animals. We investigated the effects of cecal microbiota on chicken weights using the Guizhou yellow chicken as a model. We collected experimental cohorts from chickens with high (HC, n=16) and low body weights (LC, n=16) and integrated microbial 16S rRNA gene sequencing and non-targeted serum metabolome data to explore the effect and its metabolic mechanism of cecal microbiota on market weight. The genera Lachnoclostridium, Alistipes, Negativibacillus, Sellimonas and Ruminococcus torques group were enriched in the HC group while Phascolarctobacterium was enriched in the LC group (P<0.05). Metabolomic analysis determined that pantothenic acid, luvangetin (2H-1-benzopyran-6-acrylic acid) and menadione (vitamin K) were significantly higher in HC serum while beclomethasone dipropionate (a glucocorticoid) and chlorophene (2-benzyl-4-chlorophenol) were present at higher levels in the LC group. The microbes enriched in HC that were significantly positively correlated with metabolites including pantothenic acid and menadione and negatively correlated with beclomethasone dipropionate and chlorophene. These results indicated that specific cecal bacteria in Guizhou yellow chickens alter the host metabolism and growth performance. This study provides a reference for revealing the mechanism of cecal microbe actions that affect chicken body weight.

The Metropolitan Economic Freedom Index (MEFI) ranks cities based on their support of free market enterprise. In its current state, MEFI purports to measure three constructs (government spending, taxation, and labor market freedom) with three equally weighted variables for each one, assuming perfect substitutability of variables. This study investigates the statistical consistency of MEFI through Confirmatory Factor Analysis. Multiple models investigate current variable selection by providing a potentially better indicator of labor market freedom, aggregation assumptions by removing the requirements for fixed and equal weights, and statistical consistency by evaluating the fit between the data and models. Results indicate that the current MEFI model is not statistically consistent with the data, that weighting of variables should not be equal, that variable selection should be investigated, and that constructs should be re-imagined. The models investigated provide an initial starting point for redefining MEFI.

Management of Australia’s largest river system, the Murray-Darling Basin (MDB) is controversial, addressing conflicting demands from stakeholders, including irrigators, the agriculture industry, indigenous groups, and the environmental lobby. Oversight is concentrated in a single agency, the MDB Authority, but climate change and the water market have contributed to uncertainty over the Basin’s environmental sustainability. This paper reports on preliminary research in South Australia’s Riverland, one of Australia’s most important wine, citrus, nuts, and stone fruit production areas. It focuses on the Renmark Irrigation Trust (RIT), supplying water to irrigators, primarily horticulturalists. It investigates the chief risks perceived and future-plans in face of concerns over variable water flows and economic uncertainty. Using sequential mixed methods, a questionnaire survey supplied information from a sample of irrigators, covering land use, on-farm environmental actions, risks, decision making, recent and proposed changes to farm management, and attitudes to the MDB Plan and the water market. Focus groups and interviews then enabled key topics to be addressed in greater depth. The RIT’s contribution to river restoration is highlighted, with its plans for additional on-farm water stewardship investigated. There are high levels of uncertainty regarding future viability of smallholdings, including unintended consequences from the water market (e.g., increasing areas under water hungry almonds), the role of water brokers, and environmental viability of the river system linked to climate change. More optimistic voices point to increased diversity of production, resilience of the local horticultural industry, and new entrants in the form of hobby and part-time producers.

The high penetration of intermittence resources in the energy market accelerates significantly the decarbonization process but, on the other hand, the electrical system has to face the problem of unbalances. Renewable Energies Sources (RES) are hard to precisely forecast, and power plants are not able to predict the amount of energy that they can provide far from the real time delivery. In this frame, the intraday market gets a fundamental role allowing agents to adjust their position close to the delivery time. In this work we suggest an agent-based model of intraday market combined with genetics algorithms to understand what the best strategy could be adopted by players in order to optimize the market efficiency in terms of welfare and unsold quantity. In the first part we show the effect on the market prices of different scenarios in which players aim at maximizing their revenues and selling/buying all their volumes. In the second part we show the effect of a particular genetic algorithm on the model, focusing on how agents can adapt their strategy to enhance the market efficiency. Comparative analyses are also performed to investigate how the welfare of the system increases as well as the unsold quantity decrease when genetic algorithm is introduced

Buyer practices have changed as individuals are figuring out how to live with the new truth of COVID-19. Take-out and conveyance orders have expanded, and our customer has added new items to their menu because of new client inclinations. With every one of the continuous changes, the customer had numerous unanswered inquiries, for example, Smartbridge has broad involvement with café innovation development Café TECHNOLOGY CAPABILITIES :Are the most famous items as yet unchanged after COVID? :Which are the most sold item blends now? :What is the acknowledgment of new things? :What are clients purchasing alongside new things? :How have liquor deals changed? The customer previously had reports that followed item deals and operational measurements, notwithstanding, there was a need to get a more profound knowledge into item examination. The customer expected to recognize what items and introductions were being sold all the more frequently, measure the acknowledgment of new items, and figure out what items clients buy together to improve advertising efforts, advancements, and deals. he E-business industry is filling immensely in the Indian market. The modest 4G web bundles in India clearly gives a push to these ventures. Thus, as Covid19 first hit in Quite a while, individuals got terrified to go out from their homes in light of the fact that, in their mind, it's a dread of Covid. They even wonder whether or not to go out to purchase fundamental (FMCG) products. Frenzy purchasing additionally has seen and to stay away from this dread of COVID-19, individuals are offering inclinations to the E-Commerce destinations to purchase fundamental products and a few clients are new which joined to purchase fundamental merchandise during this Pandemic Lockdown period. Numerous clients are moving their purchasing conduct from disconnected retail locations to online stores. This paper examines the customer buying pattern during lockdown.