Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market

Chin Soon Ku (), Jiale Xiong, Yen-Lin Chen (), Shing Dhee Cheah, Hoong Cheng Soong and Lip Yee Por ()
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Chin Soon Ku: Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
Jiale Xiong: Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Yen-Lin Chen: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
Shing Dhee Cheah: Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
Hoong Cheng Soong: Department of Information Systems, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
Lip Yee Por: Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia

Mathematics, 2023, vol. 11, issue 11, 1-20

Abstract: Stock market predictions are a challenging problem due to the dynamic and complex nature of financial data. This study proposes an approach that integrates the domain knowledge of investors with a long-short-term memory (LSTM) algorithm for predicting stock prices. The proposed approach involves collecting data from investors in the form of technical indicators and using them as input for the LSTM model. The model is then trained and tested using a dataset of 100 stocks. The accuracy of the model is evaluated using various metrics, including the average prediction accuracy, average cumulative return, Sharpe ratio, and maximum drawdown. The results are compared to the performance of other strategies, including the random selection of technical indicators. The simulation results demonstrate that the proposed model outperforms the other strategies in terms of accuracy and performance in a 100-stock investment simulation, highlighting the potential of integrating investor domain knowledge with machine learning algorithms for stock price prediction.

Keywords: stock prediction; LSTM algorithm; investor input; trading simulation; investment return (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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