Mean-Variance Efficient Collaborative Filtering for Stock Recommendation
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909893466783744 |
|---|---|
| author | Chung, Munki Lee, Junhyeong Lee, Yongjae Kim, Woo Chang |
| author_facet | Chung, Munki Lee, Junhyeong Lee, Yongjae Kim, Woo Chang |
| contents | The rise of FinTech has transformed financial services online, yet stock recommender systems have received limited attention. Personalized stock recommendations can significantly impact customer engagement and satisfaction within the industry. However, traditional investment recommendations focus on high-return stocks or highly diversified portfolios, often neglecting user preferences. The former would result in unsuccessful investment because accurately predicting stock prices is almost impossible, whereas the latter would not be accepted by investors because many investors, including both individuals and institutional portfolio managers, who typically hold focused portfolios based on their investment strategies and interests. Collaborative filtering (CF) also may not be directly applicable to stock recommendations, because it is inappropriate to just recommend stocks that users like. The key is to optimally blend user's preference with the portfolio theory. However, no existing model considers both aspects. We propose a simple yet effective model, called mean-variance efficient collaborative filtering (MVECF). Our model is designed to improve the Pareto optimality in a trade-off between the risk and return by systemically handling uncertainties in stock prices. Experiments on real-world data show our model can increase the mean-variance efficiency of recommended portfolios while sacrificing just a small amount of recommendation accuracy. Finally, we further show MVECF is easily applicable to the graph-based ranking model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_06590 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Mean-Variance Efficient Collaborative Filtering for Stock Recommendation Chung, Munki Lee, Junhyeong Lee, Yongjae Kim, Woo Chang Information Retrieval Computational Engineering, Finance, and Science The rise of FinTech has transformed financial services online, yet stock recommender systems have received limited attention. Personalized stock recommendations can significantly impact customer engagement and satisfaction within the industry. However, traditional investment recommendations focus on high-return stocks or highly diversified portfolios, often neglecting user preferences. The former would result in unsuccessful investment because accurately predicting stock prices is almost impossible, whereas the latter would not be accepted by investors because many investors, including both individuals and institutional portfolio managers, who typically hold focused portfolios based on their investment strategies and interests. Collaborative filtering (CF) also may not be directly applicable to stock recommendations, because it is inappropriate to just recommend stocks that users like. The key is to optimally blend user's preference with the portfolio theory. However, no existing model considers both aspects. We propose a simple yet effective model, called mean-variance efficient collaborative filtering (MVECF). Our model is designed to improve the Pareto optimality in a trade-off between the risk and return by systemically handling uncertainties in stock prices. Experiments on real-world data show our model can increase the mean-variance efficiency of recommended portfolios while sacrificing just a small amount of recommendation accuracy. Finally, we further show MVECF is easily applicable to the graph-based ranking model. |
| title | Mean-Variance Efficient Collaborative Filtering for Stock Recommendation |
| topic | Information Retrieval Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2306.06590 |