Bayesian regression and Bitcoin

By Devavrat Shah, Kang Zhang. 2014

168 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Statistical Arbitrage in Cryptocurrency Markets (2019, methodology): “One of the ﬁrst works addressing this question is Shah and Zhang (2014).”
Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies (2016, unknown): “In terms of the prediction rates for Bitcoin and other cryptocur-rencies based on the limited resources in online communities, the proposed method paralleled previous studies designed for similar purposes [15, 51].”
Finding attractive technical patterns in cryptocurrency markets (2018, background): “The literature on automatic trading of cryptocurrency is expanding at the moment [12,22].”
Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies (2016, background): “The price dynamics have been a controversial topic, though, keeping various research disciplines busy [51]–[53].”
Predicting the Price of Bitcoin Using Machine Learning (2018, methodology): “[8] implemented a latent source model as developed by [9] to predict the price of Bitcoin noting 89% return in 50 days with a Sharpe ratio of 4.”
Automated Bitcoin Trading via Machine Learning Algorithms (2014, background): “Most recently, Shah and Zhang [1] described their application of Bayesian regression to Bitcoin price prediction, which achieved high profitability.”
Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis (2015, background): “achieved an 89% return on investment over fifty days of buying and selling Bitcoins [8].”