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Machine Learning: A Potential Forecasting Tool
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Machine Learning: A Potential Forecasting Tool. |
| 개인저자 | Banga, Jasdeep Singh. |
| 단체저자명 | Oklahoma State University. Agricultural Economics. |
| 발행사항 | [S.l.]: Oklahoma State University., 2017. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
| 형태사항 | 69 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11A(E). Dissertation Abstract International |
| ISBN | 9780438090231 |
| 학위논문주기 | Thesis (Ph.D.)--Oklahoma State University, 2017. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.
Adviser: B. Wade Brorsen. |
| 요약 | Technical analysis involves predicting asset price movements from analysis of historical prices. Many studies have been conducted to determine the profitability of technical analysis. A composite prediction is considered here by using the buy an |
| 요약 | None of the individual indicators or machine learning models generate significant profit in single day forecasts. In twenty-day forecasts, only random forest and pipeline models are profitable. Neural networks and statistical models both failed |
| 일반주제명 | Agricultural economics. Finance. Economics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
