Evaluation and Analysis of an LSTM and GRU Based Stock Investment Strategy

DOI

10.2991/978-94-6463-052-7_179How to use a DOI?

Keywords

component; Stock Selection; Price Prediction; Investment Strategy; Machine Learning

Abstract

Confronted with an extremely complicated and volatile external environment, it is such a tremendous challenge for researchers and investors to predict the stock market prices. To address the challenge, this paper proposes three steps for stock investment. The stock selection is based on a special ratio which is forward PE divided by trailing PE. This ratio can better evaluate the growth of individual stocks. The research found that stocks that have low PE ratios show strong growth in the price prediction part. Two deep learning-based stock market prediction models are proposed to predict the tendency. LSTM and GRU models are separately adopted to predict future trends of stock prices based on the price history. The experimental results show that the GRU model can improve prediction accuracy and reduce time delay, compared to the consequences of the LSTM model. After determining the scope of investment, to reduce the risk of investment in the stock market, get a higher or more stable rate of return, and achieve a good investment, this study calculated the correlation between these stocks’ changes and then optimize the asset allocation. Monte Carlo model and SLSQP model are used to get the correlation between stocks and both of them to give the respective optimal portfolio. From the latter’s results, the diversity of portfolios decreases with the optimization of asset allocation.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zili Lin
AU  - Fangyuan Tian
AU  - Weiqian Zhang
PY  - 2022
DA  - 2022/12/27
TI  - Evaluation and Analysis of an LSTM and GRU Based Stock Investment Strategy
BT  - Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022)
PB  - Atlantis Press
SP  - 1615
EP  - 1626
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-052-7_179
DO  - 10.2991/978-94-6463-052-7_179
ID  - Lin2022
ER  -