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Learning and Predictability via Technical Analysis: Evidence from Bitcoin and Stocks with Enhanced Fundamentals

Last Updated on 10 February, 2024 by Rejaul Karim

In the realm of asset predictability for “hard-to-value” fundamentals, such as Bitcoin and emerging industry stocks, the study “Learning and Predictability via Technical Analysis: Evidence from Bitcoin and Stocks with Hard-to-Value Fundamentals” by Andrew L. Detzel, Hong Liu, Jack Strauss, Guofu Zhou, and Yingzi Zhu etches a fascinating framework.

Delving into the interplay of rational learning and return predictability through technical analysis, the study unveils compelling evidence that ratios of prices to their moving averages serve as robust predictors of daily Bitcoin returns, both in- and out-of-sample contexts.

What’s more, trading strategies anchored in these ratios demonstrate economically significant gains, yielding alpha and Sharpe ratio enhancements relative to traditional buy-and-hold positions.

The breadth of these findings extends to small-cap, young-firm, and low-analyst-coverage stocks, as well as the past era of NASDAQ stocks, shedding light on the nuanced dynamics of asset predictability in markets with “hard-to-value” fundamentals.

Abstract Of Paper

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.

Original paper – Download PDF

Here you can download the PDF and original paper of Learning and Predictability via Technical Analysis: Evidence from Bitcoin and Stocks with Hard-to-Value Fundamentals.

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Author

Andrew L. Detzel
Baylor University – Hankamer School 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

Yingzi Zhu
Tsinghua University – School of Economics & Management

Conclusion

In summary, “Learning and Predictability via Technical Analysis: Evidence from Bitcoin and Stocks with Hard-to-Value Fundamentals” unravels a compelling paradigm of asset predictability in the domain of “hard-to-value” fundamentals.

The equilibrium model proposed in the study sheds light on the intricate mechanisms through which rational learning engenders return predictability via technical analysis.

Notably, the documented forecasting prowess of ratios of prices to their moving averages in daily Bitcoin returns, both in- and out-of-sample scenarios, underscores their robust predictive capacity. Moreover, the study underscores the economically significant alpha and Sharpe ratio gains derived from trading strategies anchored in these ratios, relative to conventional buy-and-hold investments.

The extension of these findings to encompass small-cap, young-firm, and low-analyst-coverage stocks, as well as the era of NASDAQ stocks during the dotcom era, underscores the far-reaching implications of asset predictability in markets with “hard-to-value” fundamentals.

Related Reading:

Blockchain Characteristics and the Cross-Section of Cryptocurrency Returns

Technical Analysis and Cryptocurrencies

FAQ

Q1: What is the main focus of the research paper “Learning and Predictability via Technical Analysis: Evidence from Bitcoin and Stocks with Hard-to-Value Fundamentals”?

A1: The main focus of the research paper is to investigate what predicts returns on assets with “hard-to-value” fundamentals, such as Bitcoin and stocks in new industries. The study explores the interplay of rational learning and return predictability through technical analysis.

Q2: What evidence does the study provide regarding return predictability in the context of Bitcoin?

A2: The study provides compelling evidence that ratios of prices to their moving averages serve as robust predictors of daily Bitcoin returns. The forecasting power of these ratios is documented in both in-sample and out-of-sample contexts.

Q3: What are the findings regarding trading strategies based on the ratios of prices to moving averages in Bitcoin returns?

A3: Trading strategies based on the ratios of prices to their moving averages in Bitcoin returns generate economically significant alpha and Sharpe ratio gains relative to a traditional buy-and-hold position. The study suggests that these strategies are effective in enhancing returns and risk-adjusted performance.

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