The short of it: Investor sentiment and anomalies

This study explores the role of investor sentiment in a broad set of anomalies in cross-sectional stock returns. We consider a setting in which the presence of market-wide sentiment is combined with the argument that overpricing should be more prevalent than underpricing, due to short-sale impediments. Long-short strategies that exploit the anomalies exhibit profits consistent with this setting. First, each anomaly is stronger (its long-short strategy is more profitable) following high levels of sentiment. Second, the short leg of each strategy is more profitable following high sentiment. Finally, sentiment exhibits no relation to returns on the long legs of the strategies.     

Stock-specific sentiment and return predictability

This paper quantifies the impact of stock-specific news sentiment on future financial returns. Daily predictive regressions yield significant t-statistics for 7% at most of our sample of more than 1000 large stocks listed in the USA. While a few assets do run through pockets of predictability, the evidence suggests that the feedback effect is stronger in the reverse direction: returns are more likely to drive future sentiment than the other way around.     

Applications of a multivariate Hawkes process to joint modeling of sentiment and market return events

To investigate the complex interactions between market events and investor sentiment, we employ a multivariate Hawkes process to evaluate dynamic effects among four types of distinct events: positive returns, negative returns, positive sentiment, and negative sentiment. Using both intraday S&P 500 return data and Thomson Reuters News sentiment data from 2008 to 2014, we find: (a) self-excitation is strong for all four types of events at 15 min time scale; (b) there is a significant mutual-excitation between positive returns and positive sentiment and negative returns and negative sentiment; (c) decay of return events is almost twice as fast as sentiment events, which means market prices move faster than investor sentiment changes; (d) positive sentiment shocks tend to generate negative price jumps; and (e) the cross-excitation between positive and negative sentiments is stronger than their self-excitation. These findings provide further understanding of investor sentiment and its intricate interactions with market returns.     

Investor sentiment and economic forces

Economic theory suggests that pervasive factors should be priced in the cross-section of stock returns. However, our evidence shows that portfolios with higher risk exposure do not earn higher returns. More importantly, our evidence shows a striking two-regime pattern for all 10 macro-related factors: high-risk portfolios earn significantly higher returns than low-risk portfolios following low-sentiment periods, whereas the exact opposite occurs following high-sentiment periods. These findings are consistent with a setting in which market-wide sentiment is combined with short-sale impediments and sentiment-driven investors undermine the traditional risk-return tradeoff, especially during high-sentiment periods.     

Four centuries of return predictability

We combine annual stock market data for the most important equity markets of the last four centuries: the Netherlands and UK (1629–1812), UK (1813–1870), and US (1871–2015). We show that dividend yields are stationary and consistently forecast returns. The documented predictability holds for annual and multi-annual horizons and works both in- and out-of-sample, providing strong evidence that expected returns in stock markets are time-varying. In part, this variation is related to the business cycle, with expected returns increasing in recessions. We also find that, except for the period after 1945, dividend yields predict dividend growth rates.     

Belief-based momentum indicator and stock market return predictability

Li et al. (2022) propose a new momentum indicator that combines past returns and consistent belief information, and show that the indicator positively predicts cross-sectional stock returns. Based on the momentum indicator of Li et al. (2022), we further develop a conditional past return (CPR) indicator that additionally adds the direction information for the investors' consistent belief. We examine the effectiveness of CPR as a predictor for stock market returns. Our evidence shows that CPR significantly and positively predicts future one-month market returns. And CPR provides unique predictive information that is not related to the other popular predictors. The abundant out-of-sample evidence further supports CPR’s predictive ability. Additionally, we detect the asymmetric role of CPR in predicting market returns and find that much of the predictive ability of CPR is attributed to the interaction between the positive past returns and the positive consistent belief.     

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.     

Investor sentiment,SEO market timing,and stock price performance

This article details an investigation of the impact of investor sentiment on the probability of firms conducting seasoned equity offerings (SEOs) and on stock price performance around and subsequent to SEOs. The results show that investor sentiment has a positive impact on SEO probability and that this impact is stronger for small and young firms. Furthermore, firms conducting SEOs during high sentiment periods experience less severe short-run price drops around the issuance yet more severe post-issue long-run underperformance, compared with firms conducting SEOs during low sentiment periods. These effects of investor sentiment on stock price performance are stronger for small, young, and high market-to-book ratio firms.     

European business cycles and stock return predictability

This paper finds that the European leading economic indicator, a prime business cycle indicator for the European economies published by the OECD, can strongly predict European stock returns and generate utility gains. Importantly, the predictive power of the European indicator is above and beyond that contained in the country-specific leading indicator. Furthermore, we find that the predictive power of the European indicator is stable.     

Investor attention,index performance,and return predictability

We investigate a link between the performance of several security indexes in broad investment categories and investor attention as measured by Google search probability. We find that there is a significant short-term change in index returns following an increase in attention. Conversely, a shock to returns leads to a long-term change in attention. Given this evidence, we hypothesize that a change in index return or the sign of its return in the past can indicate the nature of the information that investors are paying attention to. Therefore, past returns should determine the impact of attention on the future returns and volatility. Indeed, we find significant interaction effects between lagged returns and attention. This result suggests that attention can alter predictability of index returns. Specifically, we demonstrate that increased investor attention diminishes return predictability and, therefore, improves market efficiency.     

Dividend growth and return predictability: A long-run re-examination of conventional wisdom

We re-examine dividend growth and return predictability evidence using 165 years of data from the Brussels Stock Exchange. The conventional wisdom holds that time-varying dividend yield is predominately explained by changes in expected returns and that expected dividend growth is only weakly forecastable. However, we find robust dividend growth predictability evidence in every time period. A lack of dividend smoothing is the most important reason for the disconnect with previous evidence. Furthermore, we find return predictability in the post-World War II period when we adjust the dividend yields for changing index composition, business cycle variation and structural breaks. This is explained by a simultaneous increase in equity duration, induced by an increasing importance of growth stocks.     

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.     

Returns,volatility and investor sentiment: Evidence from European stock markets

In this study, we show that patterns in returns behave as if investors, influenced by their level of optimism, selected stocks according to their volatility. Our goal is to confirm the contribution of behavioral finance while showing that investor sentiment can be profitably used by practitioners. We incorporate volatility in the relationship between investor sentiment and future returns, this is the main originality of our approach. Our methodology consists in comparing returns, volatility and higher-order moments of portfolios managed with investor sentiment against those obtained either with passive (buy and hold) portfolio management or with a minimum variance portfolio. Portfolios managed with investor sentiment have better returns and involve less risk under certain conditions.     

Out‐of‐sample stock return predictability in emerging markets

This article builds on the widely debated issue of stock return predictability by applying a broad range of predictor variables and comprehensively considering the in‐sample and out‐of‐sample stock return predictability of ten advanced emerging markets. It compares forecasts from models with a single predictor variable, multiple predictor variables and a combination forecast approach. The results confirm the findings of Welch and Goyal (2008) for US data that only a limited number of individual predictor variables are able to deliver significant out‐of‐sample forecasts. However, a combination forecast approach provides statistically and economically significant out‐of‐sample forecast results.     

A study of cross-industry return predictability in the Chinese stock market

We investigate cross-industry return predictability for the Shanghai and Shenzhen stock exchanges, by constructing 6- and 26- industry portfolios. The dominance of retail investors in these markets, in conjunction with the gradual diffusion of information hypothesis provide the theoretical background that allows us to employ machine learning methods to test for cross-industry predictability. We find that Oil, Telecommunications and Finance industry portfolio returns are significant predictors of other industries. Our out-of-sample forecasting exercise shows that the OLS post-LASSO estimation outperforms a variety of benchmarks and a long–short trading strategy generates an average annual excess return of 13%.     

The dual contributions of information instruments in return models: magnitude and direction predictability

By partitioning asset return prediction errors, we show explicitly the dual role of magnitude and sign prediction of return instruments. We demonstrate analytically that sign prediction directly affects heteroskedasticity in asset returns; increases in precision attenuate the heteroskedasticity. Our findings with monthly asset returns are consistent with earlier evidence and indicate that our proposed analytical model captures the sign predictive component of returns. Our results are supportive of a nonlinear return generating model that can be thought of as the product of a model, perhaps linear, for forecasting return signs and a model for forecasting return magnitudes.     

Investor sentiment and calendar anomaly effects: A case study of the impact of Ramadan on Islamic Middle Eastern markets

The holy month of Ramadan is usually a time of celebration and renewal in Muslim countries. This paper examines whether or not this is reflected in positive calendar anomalies effects in Islamic Middle Eastern stock markets during the period 1992-2007. Strong evidence is found of significant and positive calendar effects in respect to the whole period of Ramadan in most countries and it is argued that this can be attributed to the generally positive investor mood, or sentiment. Although Ramadan is a time of celebration for Muslims it also can be a time of uncertainty and this appears to result in the impact of the festival not being uniformly positive throughout Ramadan. It is found that market returns in the first and last days of Ramadan show high levels of statistically significant year-on-year variation. It is argued that this can possibly be attributed to synchronization-related herding effects amplifying the impact of the mood swings associated with this period. The paper also finds that although the overall Ramadan effect is both positive and statistically significant for most countries, the associated gains were only large enough to outweigh transactions costs and provide the basis of a profitable trading strategy in one market.     

Treasury return predictability and investor sentiment

We document that the Treasury market investor sentiment (TSENT) of institutional investors is a powerful predictor of bond risk premia. Specifically, TSENT positively predicts Treasury bond excess returns in and out of sample. The forecasting gains of TSENT are incremental to those in conventional bond return predictors: Fama–Bliss forward spreads, Cochrane–Piazzesi forward rate factor, and Ludvigson–Ng macro factor, as well as equity market sentiment proxies such as the investor sentiment index and the partial least squares sentiment index. Asset allocation analysis indicates the forecasting power of TSENT is economically valuable to investors. Finally, we show that the time-series bond risk premia predictability associated with TSENT relates to its predictive power for macroeconomic performance, such as payroll employment, unemployment rate, and industrial production.     

Disagreement and return predictability of stock portfolios

This paper provides evidence that portfolio disagreement measured bottom-up from individual-stock analyst forecast dispersions has a number of asset pricing implications. For the market portfolio, market disagreement mean-reverts and is negatively related to ex post expected market return. Contemporaneously, an increase in market disagreement manifests as a drop in discount rate. For book-to-market sorted portfolios, the value premium is stronger among high disagreement stocks. The underperformance by high disagreement stocks is stronger among growth stocks. Growth stocks are more sensitive to variations in disagreement relative to value stocks. These findings are consistent with asset pricing theory incorporating belief dispersion.     

Loan loss provisions and return predictability: A dynamic perspective

This paper examines the impact of loan loss provisions (LLPs) on return predictability during 1994–2017. We find that on average, LLPs are negatively associated with one year ahead stock returns. This effect is particularly significant during the global financial crisis but much weaker during the Basel II and III periods. Consistent with these findings, a long–short trading strategy based on LLPs generates positive abnormal returns during the Basel II and III periods but negative abnormal returns during the financial crisis. Cross-sectional tests show that this effect is more pronounced among banks with greater information asymmetry. Decomposition of LLPs suggests that these findings are driven mainly by nondiscretionary LLPs. Overall, our results suggest that the relationship between LLPs and future stock returns is not linear but contingent on bank regulations and macroeconomic conditions.