Abstract
The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. In this paper, the features are selected by various feature selection algorithms, and the parameters of the machine learning-based stock price trend prediction models are set through time-sliding window cross-validation based on 8-year data of Chinese A-share market. Through the analysis of different integrated models, the model performs best when the random forest algorithm is used for both feature selection and stock price trend prediction. Based on the random forest algorithm, a long-short portfolio is constructed to validate the effectiveness of the best model.
Highlights
The ability of investors to make profit depends mainly on their prediction ability, while most investors in the Chinese A-share market are facing investment loss
We focus on the long-term stock price trend prediction in order to construct a better long-term stock selection model
Nakamoria, and Wang state that they have predicted the direction of NIKKEI 225 index, and the results show that the support vector machine (SVM) algorithm can achieve higher accuracy [1]
Summary
The ability of investors to make profit depends mainly on their prediction ability, while most investors in the Chinese A-share market are facing investment loss. One of the main reason is that most of the investors have limited information and ability to predict the stock price trend well. The algorithms for stock trend prediction have been continuously proposed. One is the stock price trend prediction, which is called classification [1]–[5]. From the perspective of forecasting time, it is roughly grouped into two types: the short-term and the long-term trend forecast of stock price [11]. The length of time for stock price trend prediction is highly correlated with the selected features. Indicators such as yesterday’s closing price and 5-day moving average closing price are usually used to
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