An overview of investor sentiment: Identifying themes, trends, and future direction through bibliometric analysis

The impact of news on individual investor decision is explicit as investors need to update, adapt and forecast returns with constraints of time, uncertainty and resources to be successful. The aim is to understand and review the influence of news on individual investor’s decision making in stock markets and identify the impact of different type of news on individual investor’s decision making in stock markets, assess the behavioral reaction and investment decisions made by investors before and after there is news item, identify the linking effect on behavioral theories and biases, develop a generalized decision making conceptual model to understand the impact of news on investor’s reaction, decision and its linkages along with the behavioral bias. Theoretical basis/methodology for processing of news by investors is assumed to be based on Broadbent’s filter theory (1958) and due to cognitive informational inefficiency of investors it assesses the attention and the investor’s reaction of overreaction and underreaction, which do not comply with efficient market hypothesis theory. The reasons for its noncompliance are found by relating it with behavioral theories. The results explain how investor screens with filters and give attention to news only when it affects their portfolio or investment objective and strategies. It is concluded that investor’s decision making depends on degree of information penetration, information content, information influence, specific internal factors and generic external and on investors prevailing at that given circumstances. This gives us the solution to comprehend the investor’s reaction, decision and unresolved reversals, short- and long-term overreaction.

The biggest challenge facing countries, including India, is creating and managing an LCR (low carbon resilient) economy, which balances the need for high growth rates and is environmentally sustainable. The green bond market provides investors the means to help change the economy into an LCR economy. The study was undertaken to understand the key drivers and the factors influencing the individual retail investor’s decision to invest in green bonds. A survey instrument was designed and administered through the snowball sampling technique to 125 Indian respondents of various age groups who were eligible to invest in the Indian bond market. SPSS software was used to conduct a descriptive analysis followed by regression and conjoint analyses. The study results suggest that the Environmental, Social, and Governance (ESG) rating and credit rating of the green bond issuers are the key factors that influence an individual’s investment decision. The findings also highlight that incentives such as tax exemptions and awareness of green bonds also affect an investor’s decision. This research stands out as one of the first attempts to understand the Indian retail investors’ perception of a green bond.

Behavioral components of Kahneman and Tversky’s (1979) prospect theory (PT) were applied to derive an adjusted Capital Asset Pricing Model (CAPM) in the estimation of merger and acquisition-intensive firms’ expected returns. The premise was that the CAPM – rooted in expected utility theory – is violated by the behavioral biases identified in prospect theory. Kahneman and Tversky’s prospect theory (1979) has demonstrated that weaknesses abound in the viability of classical utility theory predictions. For mergers and acquisitions, firms appear to be isolated from and immune to human error, yet decisions which involve the undertaking of capital-intensive projects are delegated to senior management. These individuals are prone to cognitive biases and personalized risk appetites that may (and often do) compromize attitudes and behavior when it comes to pricing risky ventures. Having established that beta estimates using linear regression are inferior, the CAPM was implemented utilizing beta estimates obtained from the Kalman filter. The results obtained were assessed for their long-term market price predictive accuracy. The authors test the reliability of the CAPM as a predictor of price, observe the rationality of human behavior in capital markets, and attempt to model premiums to adjust CAPM returns to a level that more appropriately accounts for firm specific risk. The researchers show that market participants behave irrationally when assessing M&A firms’ specific risk. Logistic regression coupled with the development of a risk premium was implemented to correct the original Kalman filter returns and was tested for improvements in predictive power.