dc.citation.startPage |
104285 |
- |
dc.citation.title |
FINANCE RESEARCH LETTERS |
- |
dc.citation.volume |
58 |
- |
dc.contributor.author |
Hwang, Yoontae |
- |
dc.contributor.author |
Park, Junpyo |
- |
dc.contributor.author |
Lee, Yongjae |
- |
dc.contributor.author |
Lim, Dong-Young |
- |
dc.date.accessioned |
2023-12-21T11:40:54Z |
- |
dc.date.available |
2023-12-21T11:40:54Z |
- |
dc.date.created |
2023-08-11 |
- |
dc.date.issued |
2023-12 |
- |
dc.description.abstract |
Since the rise of ML/AI, many researchers and practitioners have been trying to predict future stock price movements. In actual implementations, however, stop-loss is widely adopted to manage risks, which sells an asset if its price goes below a predetermined level. Hence, some buy signals from prediction models could be wasted if stop-loss is triggered. In this study, we propose a stop-loss adjusted labeling scheme to reduce the discrepancy between prediction and decision making. It can be easily incorporated to any ML/AI prediction models. Experimental results on U.S. futures and cryptocurrencies show that this simple tweak significantly reduces risk. |
- |
dc.identifier.bibliographicCitation |
FINANCE RESEARCH LETTERS, v.58, pp.104285 |
- |
dc.identifier.doi |
10.1016/j.frl.2023.104285 |
- |
dc.identifier.issn |
1544-6123 |
- |
dc.identifier.scopusid |
2-s2.0-85167442294 |
- |
dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/65128 |
- |
dc.identifier.wosid |
001060806500001 |
- |
dc.language |
영어 |
- |
dc.publisher |
Elsevier BV |
- |
dc.title |
Stop-loss adjusted labels for machine learning-based trading of risky assets |
- |
dc.type |
Article |
- |
dc.description.isOpenAccess |
FALSE |
- |
dc.type.docType |
Article |
- |
dc.description.journalRegisteredClass |
ssci |
- |
dc.description.journalRegisteredClass |
scopus |
- |