Research On Quantitative Investment Strategy Based On LSTM Neural Network Stock Price Prediction

With the unprecedented development of the world economy,the capital market has also been developed and improved,and securities investment has become a part of people's life,and the stock market,as a barometer of economy and finance,is highly favored by investors.Quantitative investment,as an investment method that has emerged in recent years,has gradually entered investors' vision with the improvement of computer technology and theoretical framework.In addition,the number and scale of quantitative private equity funds have also developed unprecedentedly in recent years,and quantitative products have been innovated and yielded good returns,especially index-enhanced products with CSI 500 index as the benchmark,indicating that investors prefer to invest in growth small and mid-cap stocks.In this context,an in-depth study of quantitative investment strategies is of great significance to the market and investors.In order to enrich the content of quantitative investment strategy and verify the effectiveness of the strategy,this paper constructs a quantitative investment strategy based on the component stocks of China Securities 500 index.There are two main parts of the strategy: a multi-factor scoring stock selection model and a long and short-term memory(LSTM)neural network prediction model are used to screen the stocks in the stock pool,and the output results are finally back-tested and evaluated.Specifically,firstly,the stock candidate pool is initially screened by factor validity analysis and correlation analysis,and the fundamentally valid factors and technically valid factors are identified;secondly,the valid factors are then downscaled using principal component analysis to form the final factor pool;next,based on the final fundamental factors,monthly multi-factor scoring stock selection is performed to screen stocks with excellent fundamental performance;then Then,based on the final technical factors,the LSTM model is applied to the stocks screened in the previous section to make weekly forecasts;finally,the stocks with excellent fundamental performance and predicted to rise in the future are traded,and the market value of the positions are counted daily and a trading record is formed,and based on the trade backtesting results,the relevant evaluation indexes are calculated and the strategy is evaluated accordingly.Based on the above research,this paper uses data from 2011 to 2018 for valid factor testing and screening,and data from 2019 to 2020 for trade backtesting to verify the effect of the strategy.The backtesting results show that the strategy constructed in this paper can steadily obtain excess returns,and can act as a buffer when the market is down and thicken returns when the market is up.The performance of CSI 500 index in the past two years is already very good,based on this multi-factor scoring stock selection does obtain a considerable return,and after adding stock price prediction to the stock selection,the strategy returns have increased to some extent.Overall,all evaluation indicators are relatively good,which fully illustrates that the quantitative investment strategy constructed in this paper is a good strategy with certain investment reference value,and also verifies the incomplete validity of the market.This paper inherently combines fundamental analysis and technical analysis to capture short-term market trading signals,cushion downside risk and thicken strategy portfolio returns while considering enterprise value.Under the premise that the market is not fully effective,the rapid computer computing and big data analysis can effectively capture the market trading signals,which is one of the reasons why quantitative investment is popular and rapidly developing.This paper is based on quantitative investment theory,combined with deep learning algorithms to build quantitative investment strategies,enriching quantitative investment strategy research,and providing investors with investment ideas in the context of rapid development of quantitative investment.