[1H5-OS-8c] OS-8

[1H5-OS-8c-03] Stock Recommendation based on Utility-aware Matrix Factorization

〇Keigo Sakurai¹, Takahiro Ogawa¹, Miki Haseyama¹, Anjyu Anan², Kei Nakagawa^2,3 (1. Hokkaido University, 2. Nomura Asset Management Co,Ltd., 3. Osaka Metropolitan University)

Keywords:stock recommendation, utility, matrix factorization

This paper proposes Utility Efficient Collaborative Filtering (UECF), a stock recommendation method based on utility-aware matrix factorization. Traditional stock recommendation methods, which rely on collaborative filtering and mean-variance optimization, aim to recommend stocks by considering investors' preferences and balancing risk and return. However, these methods face challenges in adequately reflecting investors' risk-return preferences in portfolio recommendations. Moreover, the performance of mean-variance optimization heavily depends on estimated parameters, such as expected returns, which can reduce reliability under uncertainty. Our UECF enables the recommendation of stocks that align with investors' preferences while providing high utility. By incorporating higher-order moments and asymmetric correlation structures, our approach more accurately captures investors' preferences in stock recommendations. Experiments using real-world data demonstrate that UECF achieves high recommendation performance while effectively considering Sharpe ratios and utility in its recommendations.