Investor attention and idiosyncratic risk in cryptocurrency markets

Yao, Shouyu; Kong, Xiaoran; Şensoy, Ahmet; Akyildirim, Erdinc; Cheng, Feiyang

doi:10.1080/1351847x.2021.1989008

Cited by 16 publications

(2 citation statements)

References 80 publications

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“…To improve the reliability of the analysis, we follow prior literature and reconstruct Google Trends data, which can be downloaded for a maximum of 270 days. To create a complete time series of daily data for a longer period, we adopted the overlapping period strategy suggested by Bleher and Dimpfl (2018), which is a common methodology in finance literature (Enoksen et al 2020;Yao et al 2021). We downloaded two sets of 270 days of daily data and used 115 days as an overlapping period to rescale the Google Trends data to obtain one complete time-series data for each cryptocurrency.…”

Section: Robustness Analysismentioning

confidence: 99%

Speculative bubbles and herding in cryptocurrencies

Haykır

Yağlı

2022

Financ Innov

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This study investigates speculative bubbles in the cryptocurrency market and factors affecting bubbles during the COVID-19 pandemic. Our results indicate that each cryptocurrency covered in the study presented bubbles. Moreover, we found that explosive behavior in one currency leads to explosivity in other cryptocurrencies. During the pandemic, herd behavior was evident among investors; however, this diminishes during bubbles, indicating that bubbles are not explained by herd behavior. Regarding cryptocurrency and market-specific factors, we found that Google Trends and volume are positively associated with predicting speculative bubbles in time-series and panel probit regressions. Hence, investors should exercise caution when investing in cryptocurrencies and follow both crypto currency and market-related factors to estimate bubbles. Alternative liquidity, volatility, and Google Trends measures are used for robustness analysis and yield similar results. Overall, our results suggest that bubble behavior is common in the cryptocurrency market, contradicting the efficient market hypothesis.

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Section: Robustness Analysismentioning

confidence: 99%

Speculative bubbles and herding in cryptocurrencies

Haykır

Yağlı

2022

Financ Innov

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“…Recently, many studies have shown that investor attention has a significant impact on the cryptocurrency markets, including returns (Dastgir et al, 2019;Liu & Tsyvinski, 2021;Philippas et al, 2019;Subramaniam & Chakraborty, 2020;Zhang & Wang, 2020), price discovery (Ibikunle et al, 2020), and volatility (Sabah, 2020;Yao et al, 2021a;Zhang & Wang, 2020). However, little research has focused on the relationship between investor attention and cryptocurrency market liquidity as well as the role of investor attention in different market states.…”

Section: Introductionmentioning

confidence: 99%

Investor attention and cryptocurrency market liquidity: a double-edged sword

et al. 2022

Self Cite

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This paper explores the double-edged sword effect of investor attention on market liquidity. Based on the analysis on 597 cryptocurrencies from 2014 to 2020, our findings show that static investor attention improves cryptocurrency market liquidity over the next three months by attracting more investors into the market and stimulating buy and sell transactions. By contrast, abnormal attention persistently and negatively affects the liquidity and leads to excessive net buying pressure in the market and a crowded buyers' market, resulting in a sharp deterioration of liquidity. Moreover, these effects intensify during low global economic policy uncertainty periods and for cryptocurrencies with small market capitalization and low idiosyncratic volatility. Overall, our results have important implications for investors, portfolio managers, and policymakers.

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Time varying risk aversion and its connectedness: evidence from cryptocurrencies

Corbet,

Hou,

et al. 2024

Ann Oper Res

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Changing patterns of risk aversion may follow a non-linear counter-cyclical process. However, the evidence so far has not considered developing cryptocurrency markets. Given some unique features of cryptocurrencies, it is interesting to distinguish how these assets differ from traditional products. This paper investigates the time effects of periodicity on risk aversion for a selection of major cryptocurrencies compared to major financial assets. Significant periodic time-varying patterns are identified when analysing risk aversion. Further, bilateral and bidirectional Granger causalities are identified within cryptocurrencies, as well as between cryptocurrencies and traditional financial assets. Bitcoin is identified as a leading information transmitter of the spillover of risk aversion upon other cryptocurrencies, while estimated risk aversion of traditional financial markets plays a dominant role in the spillover processes upon the cryptocurrency cluster. The latter finding presents further evidence of developing cryptocurrency market maturity. The COVID-19 pandemic is found to have significantly influenced the connectedness of risk aversion among cryptocurrency and traditional financial markets.

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Investor attention and idiosyncratic risk in cryptocurrency markets

Cited by 16 publications

References 80 publications

Speculative bubbles and herding in cryptocurrencies

Speculative bubbles and herding in cryptocurrencies

Investor attention and cryptocurrency market liquidity: a double-edged sword

Time varying risk aversion and its connectedness: evidence from cryptocurrencies

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