Application of LSTM and portfolio optimization in Chinese stock market
DOI:
https://doi.org/10.54691/bcpbm.v30i.2450Keywords:
Chinese stock market, Machine learning, deep Learning, LSTM, Portfolio Optimization.Abstract
The purpose of this paper is to examine the application of LSTM and mean variance portfolio optimization in Chinese stock market. 20 stocks are selected from CSI 300 components, we collect their High, Low, Open, Adjust Close and trade volume from June 16th 2020 to June 16th 2022. Then we use LSTM model to forecast the stock price. The forecast results are used to construct 2 portfolios. One portfolio maximize Sharpe ratio, the other portfolio minimize variance. From April 6th to June 16th 2022, the Maximize Sharpe Ratio portfolio outperformed CSI 300 index, the Minimize Variance Portfolio did not beat the market but the return was very close to CSI. Therefore, the combination of LSTM and Mean Variance Portfolio Optimization theory is effective in Chinese stock market.
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Lim, Thian. (2013). Has Stock Market Efficiency Improved? Evidence from China. Journal of Finance & Economics. 1. 1-9.
Dao-ping Wang, Yu-ning Jia. Investor Sentiment and Excess Volatility of Chinese Stock Markets. Finance Forum, 2019, 24(7):14.
Prasad, Akhilesh & Seetharaman, Arumugam. (2021). Importance of Machine Learning in Making Investment Decision in Stock Market. Vikalpa: The Journal for Decision Makers. 46. 025609092110599.
Akhtar, Md & Zamani, Abu & Khan, Shakir & Shatat, Abdallah & Dilshad, Sara & Samdani, Faizan. (2022). Stock market prediction based on statistical data using machine learning algorithms. Journal of King Saud University - Science. 34. 101940.
Agrawal, Manish, Asif Ullah Khan, and Piyush Kumar Shukla. "Stock price prediction using technical indicators: a predictive model using optimal deep learning." Learning 6.2 (2019): 7.
Y. S. Abu-Mostafa and A. F. Atiya, “Introduction to financial forecasting,” Applied Intelligence, vol. 6, no. 3, pp. 205–213, 1996.
Li S, Li W, Cook C, et al. Independently recurrent neural network (indrnn): Building a longer and deeper rnn[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 5457-5466.
Preeti, R. Bala and R. P. Singh, "Financial and Non-Stationary Time Series Forecasting using LSTM Recurrent Neural Network for Short and Long Horizon," 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2019, pp. 1-7
Vora, Sakshi & Shaikh, Rayees & Bhanushali, Kartik & Patil, Prof. (2022). Stock Price Prediction using LSTM. Indian Journal of Artificial Intelligence and Neural Networking. 2. 1-5.
Rather, Akhter. (2021). LSTM-based Deep Learning Model for Stock Prediction and Predictive Optimization Model. EURO Journal on Decision Processes. 9. 100001.
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