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We explore the impact of investor attention on idiosyncratic risk in the cryptocurrency markets. Taking the Google Trends Index as the measure of investor attention, we find that investor attention can significantly reduce cryptocurrencies' idiosyncratic risks by increasing the liquidity. We further study possible cross-sectional variations of the effect of investor attention on idiosyncratic risk. Evidence shows that the investor attention effect is more pronounced for smaller-cap and younger cryptocurrencies. Moreover, a relatively stable external market environment and rising market state are conducive to the further play of the attention effect. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between news media sentiment and spillover effects in the cryptocurrency market. By employing a time-varying parameter vector autoregressive model, we initially develop measures of spillover specific to individual cryptocurrencies. Subsequently, we employ unique data on cryptocurrency-specific sentiment to assess its impact on these spillover measures using panel fixed effects regression analysis. Our findings indicate that news media sentiment plays a significant role in explaining the spillover dynamics within the cryptocurrency market. Unlike traditional assets, it appears that only positive sentiment affects the spillovers among cryptocurrencies, suggesting an asymmetric effect. Taking into account various characteristics of cryptocurrencies, we find that sentiment's impact on spillover is more pronounced in community-based coins than in those driven by firms. An examination of news content suggests that sentiment pertaining to emotional and risk aspects of cryptocurrencies predominantly influences these spillovers. Additionally, a comparative analysis of sentiment derived from social media and traditional news sources reveals a stronger influence of the former on spillover effects. Through extensive robustness checks, our research consistently affirms the pivotal role of sentiment in driving spillovers among cryptocurrency returns, underlining the importance of sentiment analysis in understanding the dynamics of the cryptocurrency market. [ABSTRACT FROM AUTHOR]

The price and return time series are two distinct features of any financial asset. Hence, examining the evolution of multiscale characteristics of price and returns sequential data in time domain would be helpful in gaining a better understanding of the dynamical evolution mechanism of the financial asset as a complex system. In fact, this is important to understand their respective dynamics and to design their appropriate predictive models. The main purpose of the current work is to investigate the multiscale fractals of price and return high frequency data in Turkish stock market. In this regard, the wavelet leaders computational method is applied to each high frequency data to reveal its multi-fractal behavior. In particular, the method is applied to a large set of Turkish stocks and statistical results are performed to check for (i) presence of multi-fractals in price and return series and (ii) differences between prices and returns in terms of multi-fractals. Our statistical results show strong evidence that high frequency price and return data exhibit multi-fractal dynamics. In addition, they show evidence of distinct fractal characteristics on different scales between price and return series. Furthermore, our statistical results show evidence of differences in local fluctuation characteristics of price and return time series. Therefore, differences in local characteristics are useful to build specific predictive models for each type of data for better modeling and prediction to generate profits. Besides, we found evidence that both long-range correlations and fat-tail distributions contribute to the multifractality in Turkish stocks. This finding can be attributed to the major role played by international investors in increasing the volatility of Turkish stocks. [ABSTRACT FROM AUTHOR]