経営課題にAIを!ビジネスインフォマティクス研究会 (第15回)
JSAI Special Interest Group on Business Informatics (SIG-BI #15)

2
Versatility Evaluation of Stock Prediction Model by Visualization of Cost Function
1

23Koki Sakashita1, Yuki Nakai2, and Junsuke Senoguchi3
1
1

Graduate School of Economics, The University of Tokyo
2

2

School of Advanced Science and Engineering, Waseda University
3
3School of Computer Science, Tokyo University of Technology

Abstract: 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 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 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 andversatility of a stock price prediction model.

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JSAI Special Interest Group on Business Informatics (SIG-BI #15)

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JSAI Special Interest Group on Business Informatics (SIG-BI #15)

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JSAI Special Interest Group on Business Informatics (SIG-BI #15)

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JSAI Special Interest Group on Business Informatics (SIG-BI #15)

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