Bitcoin: Learning and Predictability via Technical Analysis

51 Pages Posted: 13 Feb 2018 Last revised: 19 Mar 2019

See all articles by Andrew L. Detzel

Andrew L. Detzel

University of Denver - Daniels College of Business

Hong Liu

Washington University in St. Louis - Olin Business School; Fudan University - China Institute of Economics and Finance

Jack Strauss

University of Denver - Reiman School of Finance; University of Denver

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School; China Academy of Financial Research (CAFR)

Yingzi Zhu

Tsinghua University - School of Economics & Management

Date Written: January 9, 2019

Abstract

We document that 1- to 20-week moving averages (MAs) of daily prices predict Bitcoin returns in- and out-of-sample. Trading strategies based on MAs generate substantial alpha, utility and Sharpe ratios gains, and significantly reduce the severity of drawdowns relative to a buy-and-hold position in Bitcoin. We explain these facts with a novel equilibrium model that demonstrates, with uncertainty about growth in fundamentals, rational learning by investors with different priors yields predictability of returns by MAs. We further validate our model by showing the MA strategies are profitable for tech stocks during the dotcom era when fundamentals were hard to interpret.

Keywords: Bitcoin, Cryptocurrency, Technical Analysis

JEL Classification: G11, G12, G14

Suggested Citation

Detzel, Andrew L. and Liu, Hong and Strauss, Jack and Zhou, Guofu and Zhu, Yingzi, Bitcoin: Learning and Predictability via Technical Analysis (January 9, 2019). Paris December 2018 Finance Meeting EUROFIDAI - AFFI. Available at SSRN: https://ssrn.com/abstract=3115846 or http://dx.doi.org/10.2139/ssrn.3115846

Andrew L. Detzel

University of Denver - Daniels College of Business ( email )

2101 S. University Blvd
Denver, CO 80208
United States

HOME PAGE: http://portfolio.du.edu/adetzel

Hong Liu

Washington University in St. Louis - Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-5883 (Phone)

Fudan University - China Institute of Economics and Finance ( email )

China

Jack Strauss

University of Denver - Reiman School of Finance ( email )

2101 S. University Blvd
Denver, CO COLORADO 80126
United States
314 602 7265 (Phone)

University of Denver ( email )

2201 S. Gaylord St
Denver, CO 80208-2685
United States

Guofu Zhou (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

China Academy of Financial Research (CAFR)

Shanghai Advanced Institute of Finance
Shanghai P.R.China, 200030
China

Yingzi Zhu

Tsinghua University - School of Economics & Management ( email )

Beijing, 100084
China
+86-10-62786041 (Phone)

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