LDR | | 00000nmm u2200205 4500 |
001 | | 000000330044 |
005 | | 20241023115522 |
008 | | 181129s2017 ||| | | | eng d |
020 | |
▼a 9780438090231 |
035 | |
▼a (MiAaPQ)AAI10686354 |
035 | |
▼a (MiAaPQ)okstate:15556 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
049 | 1 |
▼f DP |
082 | 0 |
▼a 338 |
100 | 1 |
▼a Banga, Jasdeep Singh. |
245 | 10 |
▼a Machine Learning: A Potential Forecasting Tool. |
260 | |
▼a [S.l.] :
▼b Oklahoma State University.,
▼c 2017 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2017 |
300 | |
▼a 69 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A. |
500 | |
▼a Adviser: B. Wade Brorsen. |
502 | 1 |
▼a Thesis (Ph.D.)--Oklahoma State University, 2017. |
520 | |
▼a 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 |
520 | |
▼a 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 |
590 | |
▼a School code: 0664. |
650 | 4 |
▼a Agricultural economics. |
650 | 4 |
▼a Finance. |
650 | 4 |
▼a Economics. |
690 | |
▼a 0503 |
690 | |
▼a 0508 |
690 | |
▼a 0501 |
710 | 20 |
▼a Oklahoma State University.
▼b Agricultural Economics. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 79-11A(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0664 |
791 | |
▼a Ph.D. |
792 | |
▼a 2017 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996750
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자
▼b 관리자 |