Evaluation of feature selection performance for identification of best effective technical indicators on stock market price prediction

Due to the influence of many factors, including technical indicators on stock market prediction, feature selection is important to choose the best indicators. One of the feature selection methods that consider the performance of models during feature selection is the wrapper feature selection method. The aim of this research is to identify a combination of the best stock market indicators through feature selection to predict the stock market price with the least error. In order to evaluate the impact of wrapper feature selection techniques on stock market prediction, in this paper SFS and SBS with 10 estimators and 123 technical indicators have been examined on the last 13 years of Apple Company. Also, by the proposed method, the data created by the 3-day time window were converted to the appropriate input for regression methods. Based on the results observed: (1) Each wrapper feature selection method has different results with different machine learning methods, and each method is more correlated with a specific set of technical indicators of the stock market. (2) Ridge and LR estimates alone, and with two methods of the wrapper feature selection, namely SFS and SBS; They had the best results with all assessment criteria for market forecast. (3)The Ridge and LR method with all the R2, MSE, RMSE, MAE and MAPE have the best stock market prediction results. Also, the MLP Regression Method, along with the Sequential Forwards Selection and the MSE, had the best performance. SVR regression, along with the SFS and the MSE, has improved greatly compared to the SVR regression with all indicators. (4) It was also observed that different features are selected by different ML methods with different evaluation parameters. (5) Most ML methods have used the Squeeze_pro, Percentage Price Oscillator, Thermo, Decay, Archer On-Balance Volume, Bollinger Bands, Squeeze and Ichimoku indicator.