Stock market volatility, excess returns, and the role of investor sentiment

Using the Investors' Intelligence sentiment index, we employ a generalized autoregressive conditional heteroscedasticity-in-mean specification to test the impact of noise trader risk on both the formation of conditional volatility and expected return as suggested by De Long et al. [Journal of Political Economy 98 (1990) 703]. Our empirical results show that sentiment is a systematic risk that is priced. Excess returns are contemporaneously positively correlated with shifts in sentiment. Moreover, the magnitude of bullish (bearish) changes in sentiment leads to downward (upward) revisions in volatility and higher (lower) future excess returns.     

News-based sentiment and bitcoin volatility

In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In doing so, I applied three different range-based volatility estimates along with two different sentiments, namely psychological sentiments and financial sentiments, incorporating four various sentiment dictionaries. By analyzing 17,490 news coverages by 91 major English-language newspapers listed in the LexisNexis database from around the globe from January 2012 until August 2021, I found news media sentiments to play a significant role in Bitcoin volatility. Following the heterogeneous autoregressive model for realized volatility (HAR-RV)—which uses the heterogeneous market idea to create a simple additive volatility model at different scales to learn which factor is influencing the time series—along with news sentiments as explanatory variables, showed a better fit and higher forecasting accuracy. Furthermore, I also found that psychological sentiments have medium-term and financial sentiments have long-term effects on Bitcoin volatility. Moreover, the National Research Council Emotion Lexicon showed the main emotional drivers of Bitcoin volatility to be anticipation and trust.     

Market sentiment in commodity futures returns

We identify a strong presence of sentiment exposure in commodity futures returns. Sentiment is able to provide additional explanatory power for comovement among commodity futures beyond the macro- and equity-related sources. Commodity futures with low open interest growth, high volatilities, low momentum, or low futures basis are more sensitive to change in sentiment. Similar to Baker and Wurgler (2006), we construct a market sentiment index by Partial Least Squares regressions (PLS) with non-return based stock market proxies, in particular higher moments of the option implied return distribution. Moreover, our sentiment index can be built on a daily basis.     

News sentiment and stock market volatility

Review of Quantitative Finance and Accounting - This study investigates the effect of news sentiment on stock market volatility using the Generalized Autoregressive Conditional Heteroskedasticity...     

Expected stock returns and volatility

This paper examines the relation between stock returns and stock market volatility. We find evidence that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns. There is also evidence that unexpected stock market returns are negatively related to the unexpected change in the volatility of stock returns. This negative relation provides indirect evidence of a positive relation between expected risk premiums and volatility.     

Dispersion in news sentiment and corporate bond returns

We construct a news sentiment index at the firm level by using textual analysis of news articles and find that dispersion in news sentiment is a significant predictor of corporate bond returns. Bonds of firms with high dispersion in news sentiment have a highly significant average return of 7.38 percent. A portfolio that longs bonds with high dispersion in news sentiments and shorts bonds with low dispersion earns an average biweekly return of 8.53 percent. This finding is in line with an argument that dispersion in news sentiment is a proxy for future cash flow uncertainty.     

Institutional investor sentiment and aggregate stock returns

This paper examines the equity market return predictability of institutional investor sentiment, in comparison to individual investor sentiment. Our findings suggest that institutional traders are informed and that their sentiment helps to tilt stock prices towards the intrinsic value. This is because the sentiment of institutions encompasses news regarding expectations on future cash flows of underlying firms that impounds itself into future price expectations. In this study, we add to the large number of studies that investigate the role and implications of investor sentiment, which has long been viewed as a pure behavioural phenomenon, on market efficiency and price discovery.     

Cross-section of option returns and volatility

We study the cross-section of stock option returns by sorting stocks on the difference between historical realized volatility and at-the-money implied volatility. We find that a zero-cost trading strategy that is long (short) in the portfolio with a large positive (negative) difference between these two volatility measures produces an economically and statistically significant average monthly return. The results are robust to different market conditions, to stock risks-characteristics, to various industry groupings, to option liquidity characteristics, and are not explained by usual risk factor models.     

Foreign exchange volatility and stock returns

This paper explores whether foreign exchange volatility is a priced factor in the US stock market. Our investigation is motivated by a number of empirical as well as theoretical considerations. Empirically, Menkhoff et al. (2012) find that foreign exchange volatility is a pervasive factor across a variety of test assets. Theoretically, Shapiro (1974), Dumas (1978), and Levi (1990) imply that foreign exchange volatility can influence firms’ cash flow volatility therefore the discount rate. In terms of empirical implementation, we employ the cross-sectional regression methodology of Fama and MacBeth (1973) as well as the time-series regression approach of Fama and French (1996). For robustness, we also use the mimicking portfolio approach of Fama and French (1993). We find that foreign exchange volatility has no power to explain either the time-series or the cross-section of stock returns, which calls for more research on foreign exchange risk. Bartov et al. (1996) and Adrian and Rosenberg (2008) suggest an alternative and maybe promising direction.     

Implied volatility and future portfolio returns

Prior studies find that the CBOE volatility index (VIX) predicts returns on stock market indices, suggesting implied volatilities measured by VIX are a risk factor affecting security returns or an indicator of market inefficiency. We extend prior work in three important ways. First, we investigate the relationship between future returns and current implied volatility levels and innovations. Second, we examine portfolios sorted on book-to-market equity, size, and beta. Third, we control for the four Fama and French [Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.] and Carhart [Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance, 52, 57–82.] factors. We find that VIX-related variables have strong predictive ability.     

Testing excess returns on event days: Log returns vs. dollar returns

The results of academic and practitioners’ event studies are often translated from excess log returns into excess dollar returns. The prior literature argues for a difference between the statistical significance of excess log returns and that of excess dollar returns. In contrast, we show analytically and using simulations that specifying event study hypotheses in terms of excess dollar returns is equivalent to specifying them in terms of excess log returns. The prior literature’s result was due to a bias in the estimator of expected excess dollar returns, an incorrect assumption that it is approximately normally distributed, and a misapplication of the delta method.     

Grey Relational Analysis and Neural Network Forecasting of REIT returns

This study employs the Grey Relational Analysis (GRA) and Artificial Neural Network (ANN) to measure the impact of key elements on the forecasting performance of real estate investment trust (REIT) returns. To manage risks from a real estate price bubble, the findings of GRA suggest that the REIT is best influenced by industrial production index, lending rate, dividend yield, stock index and its own lagged performance. Consequently, this paper adjusts the parameters from GRA and inserts the key elements into the fitted ANN model by comparing the learning effect of the Back-propagation Neural Network (BPN). This study found that the ranking provided by the GRA is significant in correcting prediction errors using the learning outcome of the BPN. The neural network model proved to minimize error function and was able to adjust weighted values in order to enhance prediction accuracy.     

Changes in equity returns and volatility across different Australian industries following the recent terrorist attacks

We investigate the impact of five recent terrorist attacks on equities listed on the Australian Stock Exchange. Following the Global Industry Classification Standard, we analyse how these events affect the different sectors in Australia. Using parametric and non-parametric tests, we investigate the relationship between stock returns for equities listed in these sectors and terrorist attacks. We report significant short term negative abnormal returns around the September 11 attacks and to a lesser extent, the Madrid and London bombings. Our evidence shows a weak positive equity response to the Bali bombing, and no response from the Mumbai attack in the Australian market. We also document negative industry abnormal returns as high as 37.30% on the day in the Utilities sector. Our findings show that systematic risk of certain sectors increased after the events of September 11 but remained unchanged for the other attacks.     

The impacts of futures trading on volatility and volatility asymmetry of Bitcoin returns

The impact of Bitcoin futures introduction on the underlying Bitcoin volatility has been a controversial topic. Conflicting results had been obtained from different sample periods and methodologies. To address this debate, this study examines the impact of futures trading on volatility and volatility asymmetry of Bitcoin returns in the short and long run. Using exponential GARCH models, we introduce a dummy in the variance equation to capture the changes in the volatility after the introduction of Bitcoin futures. We find that after the introduction, spot return volatility decreases in the short run, but increases in the long run. Besides, in the short run, there exists an inverse leverage effect before and after the introduction; in the long run, the inverse leverage effect before the introduction changes to a usual level effect after the introduction. Finally, we examine whether greater futures trading activity, proxied by trading volume and open interest, is associated with greater Bitcoin volatility. To do so, we decompose each proxy into expected and unexpected components and document that, in the long run, Bitcoin volatility covaries positively with unexpected futures trading volume, but negatively with unexpected futures open interest.     

Managerial sentiment,consumer confidence and sector returns

This paper investigates the relationship between managerial sentiment and sector returns. Using UK monthly data from January 1985 to December 2014 and a sample of consumer and business confidence indicators provided by the European Commission, we provide novel evidence on how managerial and consumer sentiment indicators affect stock returns. We find no support for consumer confidence as a predictor of stock returns. However, managerial sentiment shows a significant impact on aggregate market and sector return indices. Furthermore, we find that parameter estimates for sector groupings are not consistent, implying that the sentiment-return relationship differs across sectors. We also find parameters are sensitive to industry characteristics. Importantly, the overall sentiment-return relationship is dominated by sentiment associated with manufacturing firms.     

Investor sentiment and stock returns: Global evidence

We assess the impact of investor sentiment on future stock returns in 50 global stock markets. Using the consumer confidence index (CCI) as the sentiment proxy, we document a negative relationship between investor sentiment and future stock returns at the global level. While the separation between developed and emerging markets does not disrupt the negative pattern, investor sentiment has a more instant impact in emerging markets, but a more enduring impact in developed markets. Individual stock markets reveal heterogeneity in the sentiment-return relationship. This heterogeneity can be explained by cross-market differences in culture and institutions, along with intelligence and education, to varying degrees influenced by the extent of individual investor market participation.     

Investor sentiment and stock returns: Wenchuan Earthquake

This paper exploits a natural experiment (the Wenchuan Earthquake in China) to study the effects of investor sentiment on stock returns. We find that during the 12 months following the earthquake, stock returns are significantly lower for firms headquartered nearer the epicenter than for firms further away. Further analyses indicate that this pattern of stock returns does not exist before or long after the earthquake, and cannot be explained by actual economic losses or a change in systematic risk. Overall, our evidence is consistent with the interaction of local bias and investor sentiment affecting stock returns.     

Investor sentiment and stock volatility: New evidence

This study investigates the predictability of sentiment measure on stock realized volatility. We propose a new investor sentiment index (NISI) based on the partial least squares method. This sentiment index outperforms many existing sentiment indicators in three aspects. First, in-sample result shows that the NISI has greater predictive power relative to the others. Most sentiment indicators show predictability in the non-crisis period only while the NISI is also effective in the crisis period. Furthermore, the NISI exhibits more prominent superiority in longer horizons forecasting. Second, further analysis indicates that the NISI has robust predictability before and after the Chinese stock market turbulence periods while the others not. Importantly, the NISI is still effective significantly after considering leverage effect while most of the others not. Finally, out-of-sample analysis demonstrates that the NISI is more powerful than other sentiment measures. This result is reproducible in different robustness checks.     

Macroeconomic announcements and asymmetric volatility in bond returns

This study analyses the impact of macroeconomic news announcements on the conditional volatility of bond returns. Using daily returns on the 1, 3, 5 and 10 year US Treasury bonds, we find that announcement shocks have a strong impact on the dynamics of bond market volatility. Our results provide empirical evidence that the bond market incorporates the implications of macroeconomic announcement news faster than other information. Moreover, after distinguishing between types of macroeconomic announcements, releases of the employment situation and producer price index are especially influential at the intermediate and long end of the yield curve, while monetary policy seem to affect short-term bond volatility.