dc.citation.conferencePlace |
US |
- |
dc.citation.conferencePlace |
Brooklyn NY USA |
- |
dc.citation.title |
ICAIF'23: 4th ACM International Conference on AI in Finance |
- |
dc.contributor.author |
Kim, Seyoung |
- |
dc.contributor.author |
Hong, Joohwan |
- |
dc.contributor.author |
Lee, Yongjae |
- |
dc.date.accessioned |
2024-01-31T18:06:05Z |
- |
dc.date.available |
2024-01-31T18:06:05Z |
- |
dc.date.created |
2023-12-14 |
- |
dc.date.issued |
2023-11-27 |
- |
dc.description.abstract |
This paper addresses the challenges of risk management in the financial market through a data-driven approach. In investment management, it is important to detect and avoid market anomalies, defined as significant deviations from typical stock price movement patterns. We propose a method that utilizes Generative Adversarial Networks (GANs) to detect anomalous patterns in stock prices. We devise simple investment strategies based on the GANs-based anomaly detection model and validate them on real-world data to demonstrate if the proposed approach can reduce investment risk. Experimental results show that GANs-based anomaly detection can be successfully incorporated into investment strategies to yield superior returns and Sharpe ratio and much reduced volatility and maximum drawdown compared to the S&P500 benchmark. This study shows the potential of data-driven approaches in detecting and managing investment risk. |
- |
dc.identifier.bibliographicCitation |
ICAIF'23: 4th ACM International Conference on AI in Finance |
- |
dc.identifier.doi |
10.1145/3604237.3626892 |
- |
dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/74432 |
- |
dc.publisher |
ACM |
- |
dc.title |
A GANs-Based Approach for Stock Price Anomaly Detection and Investment Risk Management |
- |
dc.type |
Conference Paper |
- |
dc.date.conferenceDate |
2023-11-27 |
- |