Intelligent Optimization Based Multi-Factor Deep Learning Stock Selection Model and Quantitative Trading Strategy
Study on multi-factor deep learning stock selection model using GRU and Cuckoo Search for quantitative trading.
What it examines
This paper develops a multi-factor stock selection model using intelligent optimization algorithms and deep learning, specifically a GRU neural network optimized by the Cuckoo Search algorithm. It aims to enhance quantitative investment strategies by integrating financial, technical, and public opinion indicators.
What it concludes
The research offers a robust tool for quantitative investment, showing potential for practical application in stock market trading. Future work could explore deeper mining of social media data and further optimization of neural network parameters.
Evidence objects
The research offers a robust tool for quantitative investment, showing potential for practical application in stock market trading. Future work could explore deeper mining of social media data and further optimization of neural network parameters.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
With the rapid development of financial research theory and artificial intelligence technology, quantitative investment has gradually entered people’s attention. Compared with traditional investment, the advantage of quantitative investment lies in quantification and refinement. In quantitative investment technology, quantitative stock selection is the foundation. Without good stock selection ability, the effect of quantitative investment will be greatly reduced. Therefore, this paper builds an effective multi-factor stock selection model based on intelligent optimization algorithms and deep learning and proposes corresponding trading strategies based on this. First of all, this paper selects 26 effective factors of financial indicators, technical indicators and public opinion to construct the factor database. Secondly, a Gated Recurrent Unit (GRU) neural network based on the Cuckoo Search (CS) optimization algorithm is used to build a stock selection model. Finally, a quantitative investment strategy is designed, and the proposed multi-factor deep learning stock selection model based on intelligent optimization is applied to practice to test its effectiveness. The results show that the quantitative trading strategy based on this model achieved a Sharpe ratio of 127.08%, an annualized rate of return of 40.66%, an excess return of 13.13% and a maximum drawdown rate of −17.38% during the back test period. Compared with other benchmark models, the proposed stock selection model achieved better back test performance.
Source row: 1115 · abstract type: unknown