Intraday return predictability in the cryptocurrency markets: Momentum, reversal, or both
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DOI: 10.1016/j.najef.2022.101733
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Cited by:
- Giacomo di Tollo & Joseph Andria & Gianni Filograsso, 2023. "The Predictive Power of Social Media Sentiment: Evidence from Cryptocurrencies and Stock Markets Using NLP and Stochastic ANNs," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
- Shaen Corbet & Les Oxley, 2023. "Investigating the Academic Response to Cryptocurrencies: Insights from Research Diversification as Separated by Journal Ranking," Review of Corporate Finance, now publishers, vol. 3(4), pages 487-528, September.
- Aslam, Faheem & Memon, Bilal Ahmed & Hunjra, Ahmed Imran & Bouri, Elie, 2023. "The dynamics of market efficiency of major cryptocurrencies," Global Finance Journal, Elsevier, vol. 58(C).
- Tan, Xilong & Tao, Yubo, 2023. "Trend-based forecast of cryptocurrency returns," Economic Modelling, Elsevier, vol. 124(C).
- Jiqian Wang & Feng Ma & Elie Bouri & Yangli Guo, 2023. "Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 970-988, July.
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More about this item
Keywords
Intraday return predictability; Cryptocurrency markets; Bitcoin; Momentum; Reversal; Economic value; Market timing strategy;All these keywords.
JEL classification:
- G1 - Financial Economics - - General Financial Markets
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
- Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
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