025-12

第25回研究会

評価関数の可視化による株価予測モデルの汎用性評価†

著者†

坂下好希(東京大学), 瀬之口潤輔(東京工科大学)

概要†

When predicting stock prices with a complex model using machine learning or artificial intelligence, overfitting sometimes occurs, and the prediction accuracy expected in actual operation cannot be obtained. In such a model, the cost function is presumed to be steep and multi-modal, while in a model that maintains stable prediction results, the cost function is considered to be gradual and single-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 Lasso regression, which had the highest performance, showed a gradual unimodal cost function, while the linear regression, 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 and versatility of a stock price prediction model.

キーワード†

t-SNE, cost function, visualization, versatility evaluation, wavelet, reducing dimension

論文†

12_SIG-FIN-25.pdf