G-LASSO/G-SCAD/G-MCP penalized trinomial logit dynamic models predict up trends, sideways trends and down trends for stock returns

Abstract

Correctly predicting up trends, sideways trends and down trends for stock returns is vitally important in financial market. Group penalized trinomial logit dynamic models can not only provide three-class label information and three-class probability estimations, but also enhance three-class prediction performance by shrinking group coefficients to bypass multi-collinearity and over-fitting. In this paper we propose G-LASSO/G-SCAD/G-MCP penalized trinomial logit dynamic models with 24 technical indicators to predict up trends, sideways trends and down trends for stock returns, develop group coordinate descent algorithm (GCD) to complete group selection and group estimation simultaneously, establish the relative optimal Bayes classifier to identify class labels, introduce three-class confusion matrix and hypervolume under the ROC manifold (HUM) to assess the three-class prediction performance to 15 methods. Experiment results show that G-LASSO/G-SCAD/G-MCP penalized trinomial logit dynamic models predict better than LASSO/SCAD/MCP penalized trinomial logit dynamic models, 6 deep learning models and 3 machine learning models in terms of Accuracy and HUM. In particular, the highest prediction Accuracy (the average Accuracy) from G-LASSO penalized method outperforms 3-Layer Long Short-Term Memory (LSTM) and Random Forest (RF) for 4.76% and 10.29% (8.16% and 11.78%), respectively. Moreover, compared with LASSO/SCAD/MCP penalized methods, G-LASSO/G-SCAD/G-MCP penalized methods enhance 5.88% Accuracy and only require 27.29% Predicted Time. The proposed G-LASSO/G-SCAD/G-MCP penalized trinomial logit dynamic models can not only be extended as more general three-class prediction method by replacing 24 technical indicators by some factors influencing three-class response variable, but also be directly extended as more efficient multi-category prediction method by replacing three-class response variable by multi-category response variable.