Mihova, V.; Georgiev, I.; Raeva, E.; Georgiev, S.; Pavlov, V. Validation of Stock Price Prediction Models in the Conditions of Financial Crisis. Mathematics2024, 12, 33.
Mihova, V.; Georgiev, I.; Raeva, E.; Georgiev, S.; Pavlov, V. Validation of Stock Price Prediction Models in the Conditions of Financial Crisis. Mathematics 2024, 12, 33.
Mihova, V.; Georgiev, I.; Raeva, E.; Georgiev, S.; Pavlov, V. Validation of Stock Price Prediction Models in the Conditions of Financial Crisis. Mathematics2024, 12, 33.
Mihova, V.; Georgiev, I.; Raeva, E.; Georgiev, S.; Pavlov, V. Validation of Stock Price Prediction Models in the Conditions of Financial Crisis. Mathematics 2024, 12, 33.
Abstract
The distribution laws of various natural and anthropogenic processes in the world around us are stochastic in nature. The development of mathematics and, in particular, of stochastic modelling allows us to study regularities in such processes. In practice, stochastic modelling finds a huge number of applications in various fields, including finance and economics. In this work, some particular applications of stochastic processes in finance are examined in the conditions of financial crisis. More specifically, Autoregressive Integrated Moving Average (ARIMA) models and Modified Ordinary Differential Equations (ODE) models, which have been previously developed by the authors to predict assets’ prices of four Bulgarian companies, are validated on a time period during the crisis. Estimated rates of return are calculated from the models for one period ahead. The errors are estimated and the models are compared. The predicted return values with each of the two approaches are used to derive optimal risk portfolios based on the Markowitz Model. The resulting portfolios are compared in terms of distribution (weights of the stocks), risk and rate of return.
Keywords
stochastic processes; time series; ARIMA; ODE; price forecasting
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
