Investor attention and cryptocurrency performance
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DOI: 10.1016/j.frl.2020.101702
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Citations
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Cited by:
- Kamal, Javed Bin & Hassan, M. Kabir, 2022. "Asymmetric connectedness between cryptocurrency environment attention index and green assets," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
- Vidal-Tomás, David, 2022.
"The new crypto niche: NFTs, play-to-earn, and metaverse tokens,"
Finance Research Letters, Elsevier, vol. 47(PB).
- Vidal-Tomás, David, 2022. "The new crypto niche: NFTs, play-to-earn, and metaverse tokens," MPRA Paper 112361, University Library of Munich, Germany.
- Vidal-Tomás, David, 2022. "The new crypto niche: NFTs, play-to-earn, and metaverse tokens," MPRA Paper 111351, University Library of Munich, Germany.
- Vidal-Tomás, David, 2022. "The new crypto niche: NFTs, play-to-earn, and metaverse tokens," MPRA Paper 113033, University Library of Munich, Germany.
- Maher Abida & Emna Mnif, 2023. "Investor Attention in Cryptocurrency Markets: Examining the Effects of Vaccination and COVID-19 Spread through a Wavelet Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 13(5), pages 43-51, September.
- Walker, Clive B., 2024. "Going mainstream: Cryptocurrency narratives in newspapers," International Review of Financial Analysis, Elsevier, vol. 94(C).
- Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predictability of crypto returns: The impact of trading behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
- Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
- Smales, L.A., 2022. "Investor attention in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 79(C).
- Nguyen, Khanh Quoc & Nguyen, Thanh Huong & Do, Bao Linh, 2023. "Narrative attention and related cryptocurrency returns," Finance Research Letters, Elsevier, vol. 56(C).
- Bouteska, Ahmed & Mefteh-Wali, Salma & Dang, Trung, 2022. "Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
- Arun Narayanasamy & Humnath Panta & Rohit Agarwal, 2023. "Relations among Bitcoin Futures, Bitcoin Spot, Investor Attention, and Sentiment," JRFM, MDPI, vol. 16(11), pages 1-24, November.
- Caferra, Rocco, 2022. "Sentiment spillover and price dynamics: Information flow in the cryptocurrency and stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
- Rasoul Amirzadeh & Asef Nazari & Dhananjay Thiruvady & Mong Shan Ee, 2023. "Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach," Papers 2303.16148, arXiv.org.
- Florentina Șoiman & Jean-Guillaume Dumas & Sonia Jimenez-Garces, 2022. "The return of (I)DeFiX [Le rendement de (I)DeFiX]," Working Papers hal-03625891, HAL.
- Tan, Xilong & Tao, Yubo, 2023. "Trend-based forecast of cryptocurrency returns," Economic Modelling, Elsevier, vol. 124(C).
- Zribi, Wissal & Boufateh, Talel & Guesmi, Khaled, 2023. "Climate uncertainty effects on bitcoin ecological footprint through cryptocurrency environmental attention," Finance Research Letters, Elsevier, vol. 58(PD).
- Koch, Sophia & Dimpfl, Thomas, 2023. "Attention and retail investor herding in cryptocurrency markets," Finance Research Letters, Elsevier, vol. 51(C).
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- Wang, Jying-Nan & Liu, Hung-Chun & Hsu, Yuan-Teng, 2024. "A U-shaped relationship between the crypto fear-greed index and the price synchronicity of cryptocurrencies," Finance Research Letters, Elsevier, vol. 59(C).
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More about this item
Keywords
Investor attention; Cryptocurrency returns; Google search probability; Vector autoregression;All these keywords.
JEL classification:
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
Statistics
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