Investor sentiment, disagreement, and the breadth return relationship

We study the cross-sectional breadth–return relation by assuming that investors subject to market sentiment hold a biased belief in the aggregate. With a dynamic multiasset model, we predict that the breadth–return relationship can be either positive or negative depending on the relative strength of two offsetting forces—disagreement and sentiment. We find evidence consistent with our predictions. The breadth–return relationship is positive when the sentiment effect is small. However, the relationship becomes negative when (i) the time-series variation of market-wide sentiment is high and (ii) the cross-sectional dispersion of firm-specific exposure to market-wide sentiment variation is large. Our unified framework reconciles a few seemingly inconsistent empirical studies in this literature and explains puzzling cross-sectional return patterns observed during the Internet bubble and the subprime crisis periods. This paper was accepted by Brad Barber, finance. </jats:p

An Extended SISa Model for Sentiment Contagion

One of the main differences between sentiment and infectious diseases is that the former one has two opposite infectious states: positive (optimistic) and negative (pessimistic), while the latter one has not. In this paper, based on the SISa model, we consider this issue and propose a new model of sentiment contagion called the SOSa-SPSa model. The results of both numerical and agent-based simulations show that our model could explain the process of sentiment contagion better than that of Hill et al. (2010). Further analysis shows that both the numbers of optimistic and pessimistic individuals will increase with the probability of spontaneity or contagion and decrease with the probability of recovery. Potential applications of this model in financial market have also been discussed

COVID-19 pandemic and capital markets: the role of government responses

This paper analyzes the moderation effect of government responses on the impact of the COVID-19 pandemic, proxied by the daily growth in COVID-19 cases and deaths, on the capital market, i.e., the S&P 500 firm’s daily returns. Using the Oxford COVID-19 Government Response Tracker, we monitor 16 daily indicators for government actions across the fields of containment and closure, economic support, and health for 180 countries in the period from January 1, 2020 to March 15, 2021. We find that government responses mitigate the negative stock market impact and that investors’ sentiment is sensitive to a firm’s country-specific revenue exposure to COVID-19. Our findings indicate that the mitigation effect is stronger for firms that are highly exposed to COVID-19 on the sales side. In more detail, containment and closure policies and economic support mitigate negative stock market impacts, while health system policies support further declines. For firms with high revenue exposure to COVID-19, the mitigation effect is stronger for government economic support and health system initiatives. Containment and closure policies do not mitigate stock price declines due to growing COVID-19 case numbers. Our results hold even after estimating the spread of the pandemic with an epidemiological standard model, namely, the susceptible-infectious-recovered model

A Reexamination of Time-Varying Stock Return Predictability

Master's thesis Business Administration BE501 - University of Agder 2019In predicting stock market returns, academic research has had its primary focus onmacroeconomic variables, and less attention has been paid towards technical indicators.The evidence of the stock return predictability is either absent or weak, and there arecases of contradicting evidence in the literature whether stock returns even are predictable.Over the last ten years, several papers find evidence that stock return predictability existsduring the bad economic states. These papers have used different approaches, whereasmost of them have been using NBER chronology of expansions and recessions, or investorsentiment index, to define good and bad economic times. Based on our knowledge, therehas been limited research regarding the use of bull and bear markets to determine thesemarket states. This thesis reexamines and extends previous studies on the time-varyingstock return predictability. Our research is similar to Huang et al. (2014), as we measurethe performance of different predictors by conducting a Newey-West-statistics derivedfrom one-state and two-state predictive regression for the in-sample forecast. However,our thesis is extended by using four different definitions of market states to examinewhether there is significant evidence of stock return predictability. Result of this thesispresents a mixed performance across the different macroeconomic variables and technicalindicators. Most of the predictors perform better in bull and bear markets compared toexpansions and recessions, and investor sentiment index. We have tried to compare ourresults to previous studies, but each study applied a combination of different datasets,approaches, and methodologies, and therefore, it would be impractical to compare thefindings

Dangerous Infectious Diseases: Bad news for Main Street, Good News for Wall Street?

We examine whether investor mood, driven by World Health Organization (WHO) alerts and media news on dangerous infectious diseases, is priced in pharmaceutical companies' stocks in the United States. We argue that disease-related news (DRNs) should not trigger rational trading. We find that DRNs have a positive and significant sentiment effect among investors (on Wall Street). The effect is stronger (weaker) for small (large) companies, who are less (more) likely to engage in the development of new vaccines. A potential negative investor climate (on Main Street) – induced by disease-related fear – does not alter the positive sentiment effect