| Presentation | 2015-03-05 Forcasting Individual stock prices using Deep Learning Kazuya MATSUMOTO, Kouhei SAKURAI, Satoshi YAMANE, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Attempts to predict the stock market are numerous, but practically effective ones have not been announced yet. The reason for this is that, stock market is strongly influenced by the world of trends, include those that simple law does not exist. In this study, we propose Deep Learning, the latest prediction method of individual stock price that is a combination of SNS Big data analysis, such as Twitter to correspond social mood. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Deep Learning / Machine Learning / Big Data / Stock |
| Paper # | MSS2014-95 |
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| Committee | MSS |
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| Conference Date | 2015/2/26(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Mathematical Systems Science and its applications(MSS) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Forcasting Individual stock prices using Deep Learning |
| Sub Title (in English) | |
| Keyword(1) | Deep Learning |
| Keyword(2) | Machine Learning |
| Keyword(3) | Big Data |
| Keyword(4) | Stock |
| 1st Author's Name | Kazuya MATSUMOTO |
| 1st Author's Affiliation | School of Electrical and Computer Engineering College of Science and Engineering Kanazawa University() |
| 2nd Author's Name | Kouhei SAKURAI |
| 2nd Author's Affiliation | School of Electrical and Computer Engineering College of Science and Engineering Kanazawa University |
| 3rd Author's Name | Satoshi YAMANE |
| 3rd Author's Affiliation | School of Electrical and Computer Engineering College of Science and Engineering Kanazawa University |
| Date | 2015-03-05 |
| Paper # | MSS2014-95 |
| Volume (vol) | vol.114 |
| Number (no) | 493 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |