Investor attention and cryptocurrency price crash risk: a quantile regression approach

Abstract

Purpose Motivated by the lure of cryptocurrencies for retail investors, whose concentrated holdings are particularly exposed to price crash risk, this paper aims to study the relationship between investor attention and crash risk for a range of cryptocurrencies. Design/methodology/approach This study adopts a quantile regression approach to determine the effect of investor attention on crash risk. Crash risk is measured using the negative coefficient of skewness and down up volatility. Findings This study finds that the connection is concentrated in the tails of the crash risk distribution. Investor attention has a positive relationship with crash risk when crash risk is low (below-median quantiles) and negative when crash risk is high (above-median). The findings are consistent for different measures of crash risk, for alternate internet searches and for a panel of large cryptocurrencies in addition to Bitcoin. This study also notes seasonality in crash risk, with higher crash risk during the June–August period and lower crash risk in the Halloween period that runs from November to April. Originality/value The results provide insights that are not apparent in previous analyses of cryptocurrency price crash risk. The results are particularly important for retail investors, who constitute a large portion of the cryptocurrency market, as they tend to hold concentrated investments and so a price crash of a single asset may have a large bearing on their wealth.

A systematic literature review on the determinants of cryptocurrency pricing

“…Investors’ attention has been argued to be an important determinant of cryptocurrency pricing. Smales (2021) showed that investors’ attention had a positive relationship with the cryptocurrency price. Similarly, others have highlighted that investors’ attention had the potential to improve prediction accuracy for Bitcoin returns.…”

Drivers of the next-minute Bitcoin price using sparse regressions

“…The application of machine learning techniques, including regression models, SVMs and random forests, has been used to analyze historical data and identify patterns or indicators that can signal price movements of cryptocurrencies (Jaquart et al, 2022;Smales, 2022). Additionally, deep learning methods, particularly recurrent neural networks, convolutional SEF 41,2 neural networks (CNNs) and LSTM networks, have gained popularity because of their ability to capture temporal dependencies in sequential data (Lahmiri and Bekiros, 2019;Alonso-Monsalve et al, 2020;Zhong et al, 2023;Oyedele et al, 2023).…”