Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

Kyung-Shik Shin

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Stock Market Forecasting Methods Hydrological Forecasting Using AI Time Series Analysis and Forecasting

Abstract

With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model.

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References

1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 2017Deep learning with long short-term memory networks for financial market predictions 2003Financial time series forecasting using support vector machines 2001Application of support vector machines in financial time series forecasting 2010Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange 2008Surveying stock market forecasting techniques – Part II: Soft computing methods 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index

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2021Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process 2020A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM 2021A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions 2020

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