A Stock Index Prediction Method and Trading Strategy Based on the Combination of Lasso-Grid Search-Random Forest

Abstract

This paper establishes stock index trading strategy by building stock index predicting model. First of all, this paper mainly reviews the application of machine learning in stock prediction, and constructs the L-GSRF stock index prediction model based on Lasso regression, grid search and random forest algorithm. After the input of textual and numerical data, the L-GSRF stock index prediction model greatly reduces the prediction error and improves the prediction accuracy in the photovoltaic (PV) stock index prediction, comparing with the traditional random forest and support vector machine (SVM) algorithm. In this paper, the trading strategy based on the prediction model has achieved a high annualized return. Finally, this study further clarifies the shortcomings of machine learning methods and future research directions.KeywordsMachine learningLasso regressionGrid searchRandom forestTrading strategy