Learning and Predictability via Technical Analysis: Evidence from Bitcoin and Stocks with Hard-to-Value Fundamentals

51 Pages Posted: 13 Feb 2018 Last revised: 31 Jan 2020

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 - Daniels College of Business

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 29, 2020

Abstract

What predicts returns on assets with "hard-to-value" fundamentals, such as Bitcoin and stocks in new industries? We propose an equilibrium model that shows how rational learning enables return predictability through technical analysis. We document that ratios of prices to their moving averages forecast daily Bitcoin returns in- and out-of sample. Trading strategies based on these ratios generate an economically significant alpha and Sharpe ratio gains relative to a buy-and-hold position. Similar results hold for small-cap, young-firm, and low-analyst-coverage stocks as well as NASDAQ stocks during the dotcom era.

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, Learning and Predictability via Technical Analysis: Evidence from Bitcoin and Stocks with Hard-to-Value Fundamentals (January 29, 2020). 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 - Daniels College of Business ( email )

2101 S. University Blvd.
Denver, CO 80208
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)

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
1,548
Abstract Views
5,033
rank
12,305
PlumX Metrics