Optimizing the moving average trade rule for cryptocurrencies: implications of band size and transaction costs
Author:
Affiliation:
1. Finance Area, The Busch School of Business at The Catholic University of America, Washington, DC, USA
2. Department of Economics, Goddard School of Business & Economics at Weber State University, Ogden, UT, USA
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/13504851.2024.2394211
Reference11 articles.
1. Profitability of technical trading rules among cryptocurrencies with privacy function
2. Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets
3. Using genetic algorithms to find technical trading rules1Helpful comments were made by Adam Dunsby, Lawrence Fisher, Steven Kimbrough, Paul Kleindorfer, Michele Kreisler, James Laing, Josef Lakonishok, George Mailath, and seminar participants at Institutional Investor, J.P. Morgan, the NBER Asset Pricing Program, Ohio State University, Purdue University, the Santa Fe Institute, Rutgers University, Stanford University, University of California, Berkeley, University of Michigan, University of Pennsylvania, University of Utah, Washington University (St. Louis), and the 1995 AFA Meetings in Washington, D.C. We are particularly grateful to Kenneth R. French (the referee), and G. William Schwert (the editor) for their suggestions. Financial support from the National Science Foundation is gratefully acknowledged by the first author and from the Academy of Finland by the second and from the Geewax-Terker Program in Financial Instruments by both. Correspondence should be addressed to Franklin Allen, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104-6367.1
4. Cryptocurrency, Decentralized Finance, and the Evolution of Money: A Transaction Costs Approach
5. The effectiveness of technical trading rules in cryptocurrency markets
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