Application of DeepForest-CQP multi-factor model in quantitative stock selection

Abstract

The stock market is a chaotic system, and stock forecasting has been the research focus. This paper proposes a multi-factor model based on DeepForest-CQP to make it more applicable to the stock domain. A t-test is used for selecting factors, and orthogonalization and heteroskedasticity tests are performed for the combined factors, which are particularly important in stock forecasting. DeepForest-CQP was combined with the multi-factor model to construct a stock selection model that can achieve higher returns. The obtained multi-factor quantitative stock selection model is used to study stock selection strategies, and simulated trading is used to evaluate the multi-factor model and stock selection strategies and compare them with various machine learning multi-factor models. The experimental results show that the DeepForest-CQP-based multi-factor stock selection model achieves significant performance advantages in all backtesting metrics.