評価関数の可視化による株価予測モデルの汎用性評価

抄録

When predicting stock prices with a complex model using machine learning or artificialintelligence, overfitting sometimes occurs, and the prediction accuracy expected in actual operationcannot be obtained. In such a model, the cost function is presumed to be steep and multi-modal, while in amodel that maintains stable prediction results, the cost function is considered to be gradual andsingle-peaked. In this study, we first compared the performance of several stock price prediction models,and then visualized the cost function for each model using t-SNE. As a result, the model using Lassoregression, which had the highest performance, showed a gradual unimodal cost function, while the linearregression, which had relatively low performance, showed a steep and multi-modal shape.Visualizing the cost function using t-SNE can be an important index for evaluating the stability andversatility of a stock price prediction model.