Version 1
: Received: 29 August 2023 / Approved: 29 August 2023 / Online: 30 August 2023 (03:03:52 CEST)
How to cite:
Chiang, J.K.; Chi, R. Predicting Stock Prices Base on Price/Volume with Deep Learning and System Engineering Aggregate with Dynamic Behaviors and Trading Signals. Preprints2023, 2023081979. https://doi.org/10.20944/preprints202308.1979.v1
Chiang, J.K.; Chi, R. Predicting Stock Prices Base on Price/Volume with Deep Learning and System Engineering Aggregate with Dynamic Behaviors and Trading Signals. Preprints 2023, 2023081979. https://doi.org/10.20944/preprints202308.1979.v1
Chiang, J.K.; Chi, R. Predicting Stock Prices Base on Price/Volume with Deep Learning and System Engineering Aggregate with Dynamic Behaviors and Trading Signals. Preprints2023, 2023081979. https://doi.org/10.20944/preprints202308.1979.v1
APA Style
Chiang, J.K., & Chi, R. (2023). Predicting Stock Prices Base on Price/Volume with Deep Learning and System Engineering Aggregate with Dynamic Behaviors and Trading Signals. Preprints. https://doi.org/10.20944/preprints202308.1979.v1
Chicago/Turabian Style
Chiang, J.K. and Renhe Chi. 2023 "Predicting Stock Prices Base on Price/Volume with Deep Learning and System Engineering Aggregate with Dynamic Behaviors and Trading Signals" Preprints. https://doi.org/10.20944/preprints202308.1979.v1
Abstract
Current stock market forecasting methods encompass fundamental, technical, emotional, and bargaining factors. Predominantly, price prediction hinges on order volume and price, although correlating these two within existing models proves challenging. This study employs Cycle Generative Adversarial Network (Cycle GAN) to unravel the intricate price-volume relationship, combining it with Bollinger Bands for trading signal analysis, overcoming hurdles in short-term forecasting prevalent in numerical analysis and AI. Focusing on TSMC (2330.TW) stock price, the research leverages Cycle GAN in deep learning to master the price-volume nexus, juxtaposed with LSTM and RNN. Historical TSMC closing prices and transaction counts are model inputs, scrutinizing their interconnectedness for predictions. This innovative approach aligns stock price, volume, market value, taxes, and prior changes via system engineering. By intertwining Bollinger Bands with stock price forecasts, trading signals are distilled, factoring in extended index %b for a comprehensive market picture.
Keywords
deep learning; system engineering; stock price forecasting; aggregate dynamic behavior; generative adversarial network
Subject
Business, Economics and Management, Finance
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.
