Bayesian regression and Bitcoin
In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency. Bayesian regression refers to utilizing empirical data as proxy to perform Bayesian inference. We utilize Bayesian regressio...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2016
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| Online Access: | http://hdl.handle.net/1721.1/101044 https://orcid.org/0000-0003-0737-3259 |
| _version_ | 1826198498508275712 |
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| author | Shah, Devavrat Zhang, Kang |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Shah, Devavrat Zhang, Kang |
| author_sort | Shah, Devavrat |
| collection | MIT |
| description | In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency. Bayesian regression refers to utilizing empirical data as proxy to perform Bayesian inference. We utilize Bayesian regression for the so-called “latent source model”. The Bayesian regression for “latent source model” was introduced and discussed by Chen, Nikolov and Shah [1] and Bresler, Chen and Shah [2] for the purpose of binary classification. They established theoretical as well as empirical efficacy of the method for the setting of binary classification. In this paper, instead we utilize it for predicting real-valued quantity, the price of Bitcoin. Based on this price prediction method, we devise a simple strategy for trading Bitcoin. The strategy is able to nearly double the investment in less than 60 day period when run against real data trace. |
| first_indexed | 2024-09-23T11:05:54Z |
| format | Article |
| id | mit-1721.1/101044 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:05:54Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1010442022-10-01T01:12:44Z Bayesian regression and Bitcoin Shah, Devavrat Zhang, Kang Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Shah, Devavrat Zhang, Kang In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency. Bayesian regression refers to utilizing empirical data as proxy to perform Bayesian inference. We utilize Bayesian regression for the so-called “latent source model”. The Bayesian regression for “latent source model” was introduced and discussed by Chen, Nikolov and Shah [1] and Bresler, Chen and Shah [2] for the purpose of binary classification. They established theoretical as well as empirical efficacy of the method for the setting of binary classification. In this paper, instead we utilize it for predicting real-valued quantity, the price of Bitcoin. Based on this price prediction method, we devise a simple strategy for trading Bitcoin. The strategy is able to nearly double the investment in less than 60 day period when run against real data trace. National Science Foundation (U.S.) (Grant CMMI-1335155) National Science Foundation (U.S.) (Grant CNS-1161964) United States. Army Research Office. Multidisciplinary University Research Initiative (Award W911NF-11-1-0036) 2016-02-02T00:22:06Z 2016-02-02T00:22:06Z 2014-09 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-8009-3 http://hdl.handle.net/1721.1/101044 Shah, Devavrat, and Kang Zhang. “Bayesian Regression and Bitcoin.” 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton) (September 2014). https://orcid.org/0000-0003-0737-3259 en_US http://dx.doi.org/10.1109/ALLERTON.2014.7028484 Proceedings of the 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Shah, Devavrat Zhang, Kang Bayesian regression and Bitcoin |
| title | Bayesian regression and Bitcoin |
| title_full | Bayesian regression and Bitcoin |
| title_fullStr | Bayesian regression and Bitcoin |
| title_full_unstemmed | Bayesian regression and Bitcoin |
| title_short | Bayesian regression and Bitcoin |
| title_sort | bayesian regression and bitcoin |
| url | http://hdl.handle.net/1721.1/101044 https://orcid.org/0000-0003-0737-3259 |
| work_keys_str_mv | AT shahdevavrat bayesianregressionandbitcoin AT zhangkang bayesianregressionandbitcoin |