Bayesian regression and Bitcoin
Author(s)Shah, Devavrat; Zhang, Kang
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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  and Bresler, Chen and Shah  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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Proceedings of the 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
Institute of Electrical and Electronics Engineers (IEEE)
Shah, Devavrat, and Kang Zhang. “Bayesian Regression and Bitcoin.” 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton) (September 2014).