High Sentiment Research Articles

Sentiment, Implied Volatility Slope, and Risk-Neutral Skewness

Empirical studies show that sentiment, implied volatility slope and risk-neutral skewness all predict future stock returns. High sentiment and steeper implied volatility curve typically predict negative future returns. However, evidence of return predictability from risk-neutral skewness are mixed. This paper proposes a theoretical model that explains and reconciles these empirical findings. We show that up to a sentiment threshold, implied volatility slope becomes steeper when sentiment is higher. The threshold is large enough to contain most empirical situations. The relationship between risk-neutral skewness and sentiment (or implied volatility slope) can be either positive or negative depending on market conditions. Our model helps explain the difference between implied volatility curves of index options and stock options. It generates implied volatility curves that are consistent to empirical data.

Bitcoin and Sentiment

On the surface, cryptocurrencies share important features in common with high sentiment beta stocks. Baker and Wurgler (2007) identify high sentiment betas with small startup firms that have great growth potential. This paper investigates the degree to which, during the period July 18, 2010 to February 26, 2018, the return to bitcoin displayed the characteristics of a high sentiment beta stock. Using a sentiment-dependent factor model, the analysis indicates that in large measure, bitcoin returns resembled returns to high sentiment beta stocks.

Investor sentiment and timely loss recognition

PurposeThe purpose of this paper is to investigate the impact of investor sentiment on timely loss recognition by examining a sample of firms for the period 1988-2015.Design/methodology/approachThe authors use the accruals-based model of Ball and Shivakumar (2005) and a sentiment measure in their primary analysis. Supporting analyses include an extension of Simpson (2013) using an abnormal accruals analysis with subsamples of firms with bad news, the use of a Khan and Watts (2009) quarter firm-level measure of conservatism and an investigation of the monitoring role played by financial analysts.FindingsThe study finds that managers strategically report more losses in high sentiment periods than in low sentiment periods. This loss timing behavior results in an average 37.8 per cent increase in the acceleration of loss recognition. This study additionally finds a negative correlation between investor sentiment and abnormal accruals when managers are reporting bad news, and that a greater number of financial analysts following a firm curtails managers’ acceleration of loss recognition in high sentiment periods.Originality/valueThis study contributes to the corporate disclosure literature by showing that managers strategically recognize losses, and such behavior is more prevalent in high sentiment periods. Managers take advantage of prevailing investor sentiment to accelerate losses in high sentiment periods to mitigate market penalties from reporting bad news.

Differences in options investors’ expectations and the cross-section of stock returns

We provide strong evidence that the dispersion of individual stock options trading volume across moneynesses (IDISP) contains valuable information about future stock returns. Stocks with high IDISP consistently underperform those with low IDISP by more than 1% per month. In line with the idea that IDISP reflects dispersion in investors’ beliefs, we find that the negative IDISP-return relationship is particularly pronounced around earnings announcements, in high sentiment periods and among stocks that exhibit relatively high short-selling impediments. Moreover, the IDISP effect is highly persistent and robustly distinct from the effects of a large array of previously documented cross-sectional return predictors.

Does Investor Sentiment Predict the Near‐Term Returns of the Chinese Stock Market?

AbstractRecent evidence on the relationship between investor sentiment and subsequent monthly market returns in China shows that investor sentiment is a reliable momentum predictor since an increase (decrease) in investor sentiment leads to higher (lower) future returns. However, we suggest that momentum predictability of investor sentiment originates from the boom and bust period of 2006–2008 (the bubble period hereafter). The bubble period is characterized by several months of sustained optimism followed by several months of sustained pessimism, with the market consequently earning high (low) returns following high (low) sentiment months. Therefore, we find a strong positive association between investor sentiment and subsequent market returns during the bubble period. However, investor sentiment has a negligible impact on subsequent monthly market returns once we exclude the bubble period.

Positive Feedback Trading and Investor Sentiment

ABSTRACTThis article examines how investor sentiment affects positive feedback trading behavior. By analyzing the daily closing total return of CSI 300 index and its individual returns of stocks, we find that relatively high or low sentiment induces active positive feedback trading. With a specific indicator of sentiment, we explain the microstructure setting of the relationship between positive feedback trading and sentiment. We adopt the classical feedback model from Sentana and Wadhwani (1992) to measure positive feedback trading behavior. By adding sentiment factor to the model, we successfully explain how sentiment influences the behavior of both feedback traders and rational investors. The empirical findings suggest that positive feedback traders are more likely to trade when the prices of most securities move forward together. When the sentiment of feedback traders is at an intermediate level, the feedback trading behavior is insignificant.

The short-run performance of equity issues in South Africa: Bad timing or a last resort?

Understanding the stock market’s reaction to secondary equity offerings (SEOs) is vital for managers who are commonly tasked with deciding on how to finance their firm’s operations. This study investigated the short-run performance of firms conducting equity issuance on the Johannesburg Stock Exchange (JSE) over the period 1998–2015 by exploring both rational and behavioural models in predicting SEO behaviour. Event-study analysis reveals that the market generally reacts negatively to the announcement of SEOs with a statistically significant average two-day cumulative abnormal return of -2.6%. We also found that the probability of a firm conducting a SEO is significantly negatively related to the number of years listed and the future share return. Although it would make sense that more corporate activity takes place during periods of high investor sentiment, there is no significant evidence that firms conducting SEOs are attempting to time the market.

Fund Management Skill and Noise Trading

In this article, the authors show that institutional investors’ skill matters most during high sentiment periods when market signals are noisy. The results reveal that the fund managers with the highest (lowest) skill add (lose) $7.71 ($5.64) million of value during high sentiment periods, compared with a $3.74 million gain realized by the average manager during the entire sample period. When the market sentiment is low, highly skilled fund managers incur a value loss of only $0.18 million, much smaller than the $30.32 million loss realized by their less-skilled counterparts.

Investor sentiment and price discovery: Evidence from the pricing dynamics between the futures and spot markets

Abstract This study examines the role of investor sentiment in the pricing dynamics between the spot and futures markets. The empirical evidence suggests that investor sentiment has a positive impact on price volatility and the bid–ask spread on both the spot and futures markets, which induces higher arbitrage risk and trading costs during high sentiment periods. Consequently, during high sentiment periods, informed traders become less willing to leverage their information advantages on the futures market, which diminishes the futures markets’ leading informational role and contributions to price discovery. Our findings provide support for the theory of limits to arbitrage.

The 52-week high, momentum, and investor sentiment

This paper examines the link between the profitability of the 52-week high momentum strategy and investor sentiment. We hypothesize that investors' investment decisions are subject to behavioral biases when the level of investor sentiment is high, resulting in higher profits for the 52-week high momentum following high-sentiment periods. Our empirical results confirm this prediction. In addition, we find that the significant profit of the 52-week high momentum following high-sentiment periods persists up to five years. Further investigations show that the strong persistence of the 52-week high winners (losers) is concentrated in stocks with higher (lower) earnings surprises, especially during periods following high sentiment. Overall, our results provide supportive evidence for the anchoring biases in explaining the 52-week high momentum, especially when the role of investor sentiment is taken into account.

Housing market sentiment and intervention effectiveness: Evidence from China

This paper studies the interaction between housing market sentiment and government interventions. With a unique micro-level data from China, we construct a housing market sentiment index by applying the techniques in the finance literature. This index is significantly correlated with the confidence indexes from official sources. We find that housing market returns increase with contemporaneous sentiment, and high sentiment is followed by low returns. Tightening policies cannot reduce optimism, and high sentiment negatively impacts the effectiveness of tightening policies. This negative impact is especially significant in the zones where housing prices are sensitive to increasing sentiment.

Implied Volatility Spreads and Future Options Returns Around Information Events and Conditions

While numerous prior studies report that call–put implied volatility spreads positively predict future stock returns, recent literature shows that the predictive relation is negative for future call option returns. We investigate whether and, if so, how the predictive relation for options returns is influenced by various information events and conditions. In addition to confirming an opposite predictive relation for both call and put returns, we show that the predictive relation is stronger during periods of earnings announcement and/or high sentiment. In addition, we find that investors learn from informed trading and revise their predictability bias by examining the impacts of information asymmetry, stock liquidity, and options liquidity on the predictive relationships.

The differential impact of investor sentiment on the value relevance of book value versus earnings

This study investigates the impact of investor sentiment on the allocation of investor attention across different information sources. Specifically, I examine the differential role of investor sentiment on the value relevance of book value versus earnings. I predict and find that the value relevance of book value is higher during low sentiment relative to high sentiment periods, and conversely that the value relevance of earnings is higher during high sentiment relative to low sentiment periods. Additional analyses suggest that this sentiment effect is more pronounced for book value components that are closely related to abandonment value, and for earnings components that have strong indication of future earnings. This study also provides empirical evidence for the 'limited attention' theory suggested by psychology research by showing that investor skepticism leads to their placing greater emphasis on financial statement information.

Idiosyncratic Risk, Stock Returns and Investor Sentiment

A growing number of studies show that idiosyncratic risk is positively related to stock returns. However, the results of such works are not consistent with each other. Since the weighting function of prospect theory is not linear, this implies that when investors make decisions under uncertainty they use a dual-classification process. This paper thus argues that applying a more appropriate research method could help to clarify the relationship between idiosyncratic risk and stock returns. This paper applies a Panel Smooth Transition Regression model to conduct an empirical study. The results show that idiosyncratic risk is positively related to stock returns, as is investor sentiment. For a given idiosyncratic risk, retail investors with low sentiment require lower stock returns than investors with high sentiment.

Maxing Out in China: Optimism or Attention?

Bali, Cakici, and Whitelaw (2011) document a MAX premium in the U.S. where stocks with the highest maximum daily returns (MAX) underperform stocks with the lowest MAX in the subsequent month. However, the source of this MAX premium is contentious. Fong and Toh (2014) find that the MAX premium exclusively follows high sentiment periods suggesting that it is driven by investor optimism during high sentiment periods. In contrast Cheon and Lee (2017) find that the MAX premium is stronger following low sentiment periods suggesting that it is driven by the attention-grabbing characteristic of high MAX stocks in low sentiment periods. We present evidence from China consistent with the MAX premium being driven by investor optimism during high sentiment periods.

Are Disagreements Agreeable? Evidence from Information Aggregation

Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares disagreement index by aggregating information across individual disagreement measures and shows that this index significantly predicts market returns both in- and out-of-sample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship.

How diverse is your team? Investigating gender and nationality diversity in GitHub teams

BackgroundBuilding an effective team of developers is a complex task faced by both software companies and open source communities. The problem of forming a “dream”team involves many variables, including consideration of human factors and it is not adilemma solvable in a mathematical way. Empirical studies might provide interesting insights to explain which factors need to be taken into account in building a team of developers and which levers act to optimise productivity among developers.AimIn this paper, we present the results of an empirical study aimed at investigating the link between team diversity (i.e., gender, nationality) and productivity (issue fixing time).MethodWe consider issues solved from the GHTorrent dataset inferring gender and nationality of each team’s members. We also evaluate the politeness of all comments involved in issue resolution.ResultsResults show that higher gender diversity is linked with a lower team average issue fixing time (higher productivity), that nationality diversity is linked with lower team politeness and that gender diversity is linked with higher sentiment.

Bonds, Stocks, and Sources of Mispricing

This paper shows that distressed stocks and bonds are overpriced during high sentiment periods. The correction of overpricing leads to a range of anomalous cross-sectional patterns in stock and bond returns. Including bonds as additional test assets allows us to develop testable restrictions about overpricing rationales related to lottery-type preferences, shareholders' ability to extract value during bankruptcy, and market sentiment. It also reinforces the notion that anomaly payoffs are unexplained by co-movement with risk factors. The evidence suggests that anomalies are attributable to sentiment-driven investors' (both retail and institutional) excessive optimism about the likelihood and consequences of financial distress.

Investor Sentiment Dynamics, the Cross-Section of Stock Returns and the MAX Effect

Recent evidence shows that investor sentiment is a contrarian predictor of stock returns with speculative stocks earning lower (higher) future returns than safe stocks following high (low) sentiment states. We extend this argument by conditioning expected stock returns on sentiment dynamics and show that the mispricing of speculative and safe stocks worsens with sentiment continuations but is corrected with sentiment transitions, consistent with the view that the mispricing of these stocks is sentiment-driven. We show that the unconditional contrarian return predictability of sentiment, at least in the short-run, is due to the returns of stocks in sentiment transitions. Results show that ex post, sentiment is a momentum predictor if subsequent sentiment continues; and a contrarian predictor if subsequent sentiment transitions. We show that the MAX effect can either be positive or negative contingent on sentiment dynamics. The absence of a negative MAX effect following Low sentiment states suggested by prior studies is due to the completely offsetting negative MAX effect when sentiment continues in a Low state and the positive MAX effect when sentiment transitions from a High to a Low state.

Differences in Expectations and the Cross Section of Stock Returns

We provide strong evidence that differences in expectations (DiE) among options investors, measured from the dispersion of equity options trading volume across moneyness levels, contain valuable information about future stock returns. Stocks with high DiE consistently underperform on average those with low DiE by more than 1% per month, with the negative DiE-return relationship strongest when short-sale constraints are high and among stocks that exhibit relatively high arbitrage risk. Moreover, we document that the DiE effect is highly persistent, pronounced in high sentiment periods, and is robustly distinct from a large array of previously documented cross-sectional return predictors.