Accounting for investor sentiment in news and disclosures

This study aims to develop two sentiment indices sourced from news stories and corporate disclosures of the firms in the National Stock Exchange NIFTY 50 Index by extracting sentiment polarity. Subsequently, the two indices would be compared for the predictive accuracy of the stock market and stock returns during the post-digitization period 2011–2018. Based on the findings this paper suggests various options for financial strategy.

The news- and disclosure-based sentiment indices are developed using sentiment polarity extracted from qualitative content from news and corporate disclosures, respectively, using qualitative analysis tool “N-Vivo.” The indices developed are compared for stock market predictability using quantitative regression techniques. Thus, the study is conducted using both qualitative data and tools and quantitative techniques.

This study shows that the investor is more magnetized to news than towards corporate disclosures though disclosures contain both qualitative as well as quantitative information on the fundamentals of a firm. This study is extended to sectoral indices, and the results show that specific sectoral news impacts sectoral indices intensely over market news. It is found that the market discounts information in disclosures prior to its release. As disclosures in quarterly statements are delayed information input, firms can use voluntary disclosures to reduce the communication gap with investors by using the internet. Managers would do so only when the stock price is undervalued and tend to ignore the market and the shareholder in other cases. Otherwise, disclosure sentiment attracts only long horizon traders.

Finance managers need to improve disclosure dependence on investors by innovative disclosure methodologies irrespective of the ruling market price. In this context, future studies on investor sentiment would be interesting as they need to capture man–machine interactions reflected in market sentiment showing the interplay of human biases with machine-driven decisions. The findings would be useful in developing the financial strategy for protecting firm value.

This study is unique in providing a comparative analysis of sentiment extracted from news and corporate disclosures for explaining the stock market direction and stock returns and contributes to the behavioral finance literature.