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The purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period.

The paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto traders’ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrencies’ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005).

Our findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies.

The study provides crucial guidelines for investors’ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the market’s information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior.

This paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.

Is location in the financial center of a country significant determinant of a firm’s dividend policy? The purpose of this paper is to answer this question within the context of an emerging market.

The authors use variety of techniques (OLS regression, panel regression with random effects, TOBIT regression and quintile regression) to document the effect of location on dividend policies of Indian firms during the period between 2001 and 2016.

The results show significantly higher dividend payout ratios for firms headquartered in Mumbai, the main financial center of India. The results are robust for alternate proxy of dividend policy and for different sub-samples. The results also show that these results are more pronounced for firms with better information environment (firms with high analyst coverage).

To the best of the authors’ knowledge, most of the prior research overlooks how a location of firm’s headquarter in the financial center affects its dividend decisions. This paper fills this gap by documenting the relationship between the two in India.