Common Risk Factors in Cryptocurrency

  • Yukun Liu, Aleh Tsyvinski and Xi Wu
  • Economic Modelling, 2020
  • A version of this paper can be found here
  • Want to read our summaries of academic finance papers? Check out our Academic Research Insight category

What are the Research Questions?

Cryptocurrency investing is a widely debated topic and one can find plenty of debates on Twitter discussing the fed, fiat currencies, and inflation. Regardless of where you fall on the crypto spectrum, we try and focus on research-centric takes on various investment themes whenever possible.

The authors of this study research the cross-section of cryptocurrency returns and ask the following:

  • Are there common risk factors that explain crypto returns?

What are the Academic Insights?

By studying all of the coins (data comes from Coinmarketcap.com) with market capitalizations above one million dollars and their returns from the beginning of 2014 ( there were a total of 109) to the end of 2018 (the total grew to 1,583) and 25 potential common factors, the authors find:

  1. YES, many of the known characteristics in the equity market also form successful long-short trading strategies in the cross-section of cryptocurrencies. In particular, three factors – cryptocurrency market, size, and momentum – capture most of the cross-sectional expected returns.

The authors perform a number of robustness tests to confirm the result.

Why does it matter?

This paper establishes a set of stylized facts on the cross-section of cryptocurrencies that can be used to assess and develop theoretical models.

The Most Important Chart from the Paper:

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

Abstract

We find that three factors—cryptocurrency market, size, and momentum—capture the cross-sectional expected cryptocurrency returns. We consider a comprehensive list of price- and market-related return predictors in the stock market and construct their cryptocurrency counterparts. Ten cryptocurrency characteristics form successful long-short strategies that generate sizable and statistically significant excess returns, and we show that all of these strategies are accounted for by the cryptocurrency three-factor model. Lastly, we examine potential underlying mechanisms of the cryptocurrency size and momentum effects.

Elisabetta Basilico, PhD, CFA
Dr. Elisabetta Basilico is a seasoned investment professional with an expertise in "turning academic insights into investment strategies." Research is her life's work and by combing her scientific grounding in quantitative investment management with a pragmatic approach to business challenges, she’s helped several institutional investors achieve stable returns from their global wealth portfolios. Her expertise spans from asset allocation to active quantitative investment strategies. Holder of the Charter Financial Analyst since 2007 and a PhD from the University of St. Gallen in Switzerland, she has experience in teaching and research at various international universities and co-author of articles published in peer-reviewed journals. She and co-author Tommi Johnsen published a book on research-backed investment ideas, titled Smarte(er) Investing. How Academic Insights Propel the Savvy Investor. You can find additional information at Academic Insights on Investing.

Important Disclosures

For informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice. Certain information is deemed to be reliable, but its accuracy and completeness cannot be guaranteed. Third party information may become outdated or otherwise superseded without notice.  Neither the Securities and Exchange Commission (SEC) nor any other federal or state agency has approved, determined the accuracy, or confirmed the adequacy of this article.

The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).

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