| LDR | | 01826nmm uu200433 4500 |
| 001 | | 000000330044 |
| 005 | | 20240805155751 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438090231 |
| 035 | |
▼a (MiAaPQ)AAI10686354 |
| 035 | |
▼a (MiAaPQ)okstate:15556 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 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 |