Can mutual funds time investor sentiment?

Review of Quantitative Finance and Accounting - This paper examines the ability of mutual fund managers to time aggregate investor sentiment. Our results indicate that mutual fund managers alter...     

Does investor sentiment really matter?

We examine the role sentiment plays and its manifestation in the trading behavior of investors in the U.S. stock market. Our findings support the notion that sentiment-induced buying and selling is an important determinant of stock price variation. While ‘classical’ asset pricing categorizes investors who trade in ways not consistent with mean-variance optimization as ‘irrational,’ we show that this traditional view should not hastily be evoked to characterize sentiment-driven investing. We instead show that sentiment-driven investors can trade against the herd and sell when prices are overinflated as a result of over-bullishness and vice versa. The asset pricing implications of this paper are that sentiment is linked to shifts in risk tolerance and this triggers contrarian-type behavior. In sum, we uncover the following regarding the behavior of sentiment-driven investors; firstly, they are more apt to trade on survey-based indicators rather than market-based indicators. Secondly, they trade on the basis of information extracted from individual, rather than institutional, investor surveys. Thirdly, they respond asymmetrically to shifts in sentiment and trade more aggressively during periods of declining sentiment. Finally, there is asymmetry in the role of sentiment with respect to business conditions whereby such buying and selling is more pronounced during bear markets.     

When does investor sentiment predict stock returns?

We examine the asymmetry in the predictive power of investor sentiment in the cross-section of stock returns across economic expansion and recession states. We test the implication of behavioral theories and evidence that the return predictability of sentiment should be most pronounced in an expansion state when investors' optimism increases. We segregate economic states according to the NBER business cycles and further implement a multivariate Markov-switching model to capture the unobservable dynamics of the changes in the economic regime. The evidence suggests that only in the expansion state does sentiment perform both in-sample and out-of-sample predictive power for the returns of portfolio formed on size, book-to-market equity ratio, dividend yield, earnings-to-price ratio, age, return volatility, asset tangibility, growth opportunities, and 11 widely documented anomalies. In a recession state, however, the predictive power of sentiment is generally insignificant.     

Which sentiment index is more informative to forecast stock market volatility? Evidence from China

In this paper, we investigate the predictive ability of three sentiment indices constructed by social media, newspaper, and Internet media news to forecast the realized volatility (RV) of SSEC from in- and out-of-sample perspectives. Our research is based on the heterogeneous autoregressive (HAR) framework. There are several notable findings. First, the in-sample estimation results suggest that the daily social media and Internet media news sentiment indices have significant impact for stock market volatility, while the sentiment index built by traditional newspaper have no impact. Second, the one-day-ahead out-of-sample forecasting results indicate that the two sentiment indices constructed by social media and Internet media news can considerably improve forecast accuracy. In addition, the model incorporating the positive and negative social media sentiment indices exhibits more superior forecasting performance. Third, we find only the sentiment index built by Internet media news can improve the mid- and long-run volatility predictive accuracy. Fourth, the empirical results based on alternative prediction periods and alternative volatility estimator confirm our conclusions are robust. Finally, we examine the predictability of the monthly sentiment indices and find that the two sentiment indices of social media and Internet media news contain more informative to forecast the monthly RV of SSEC, CSI800, and SZCI, however invalid for CSI300.     

Why does stock-market investor sentiment influence corporate investment?

Review of Quantitative Finance and Accounting - We examine the relationship between corporate investment and investor sentiment at the firm level with the predicted change in investor sentiment....     

How does news sentiment affect the states of Japanese stock return volatility?

This paper examines the impact of public news sentiment on the volatility states of firm-level returns on the Japanese Stock market. We firstly adopt a novel Markov Regime Switching Long Memory GARCH (MRS-LMGARCH), which is employed to estimate the latent volatility states of intraday stock return. By using the RavenPack Dow Jones News Analytics database, we fit discrete choice models to investigate the impact of news sentiment on changes of volatility states of the constituent stocks in the TOPIX Core 30 Index. Our findings suggest that news occurrence and sentiment, especially those of macro-economic news, are a key factor that significantly drives the volatility state of Japanese stock returns. This provides essential information for traders of the Japanese stock market to optimize their trading strategies and risk management plans to combat volatility.     

Can the implied volatility surface move by parallel shifts?

This note explores the analogy between the dynamics of the interest rate term structure and the implied volatility surface of a stock. In particular, we prove an impossibility theorem conjectured by Steve Ross.     

Do sentiment trades explain investor overconfidence around analyst recommendation revisions?

This study examines irrational stock market reactions to analyst recommendation revisions depending on investor sentiment levels prior to analyst report announcements. We construct a firm-specific sentiment indicator by extending Huang et al. (2015, Review of Financial Studies, 28, pp.791–837). Analyst recommendation revisions have more pronounced effects for downgrades, which is attributable to sentiment effects. Domestic investors tend to react less to upgrades (downgrades) news when their prior beliefs are pessimistic (optimistic), implying that they are overconfident. The domestic investors drive sentiment trades, whereas foreign investors are not biased.     

Does the Hurst index matter for option prices under fractional volatility?

This study examines the effect of fractional volatility on option prices. To this end, we develop an approximation method for the pricing of European-style contingent claims when volatility follows a fractional Brownian motion. Through extensive numerical experiments, we confirm that the decrease in the smile amplitude under fractional volatility is much slower than that under the standard stochastic volatility model. We also show that the Hurst index under fractional volatility has a crucial impact on option prices when the maturity is short and speed of mean reversion is slow. On the contrary, the impact of the Hurst index on option prices reduces for long-dated options.     

Does favorable investor sentiment lead to costly decisions to go public?

We investigate the real effects of decisions to undertake an initial public offering of stock in periods of favorable investor sentiment. Specifically, we examine potential effects of favorable investor sentiment on investment expenditures and how effects on investment affect firm operating performance and value as well as the likelihood of survival. We find that firms going public during periods of favorable sentiment, on average, spend substantially more on investments, especially acquisitions, than firms going public in other periods. The effect of favorable investor sentiment on investment is more pronounced for younger firms. We do not find, however, that the higher investment spending in the wake of favorable sentiment leads to worse operating or stock performance. Stock returns around acquisitions announcements are also positive for firms going public in favorable sentiment periods. The preponderance of our findings indicate that decisions to go public in favorable investor sentiment periods do not lead to corporate investment decisions that harm firm performance and value.     

Can exchange rate volatility explain persistence in the forward premium?

The persistence of the forward premium has been cited both as evidence of the failure of the unbiasedness hypothesis and as rationale for the forward premium anomaly. This paper examines the recent proposition that forward premium persistence can be explained solely by the conditional variance of the spot rate. We provide theoretical and empirical evidence to challenge this proposition. Our empirical results are shown to be robust to the presence of structural breaks. A corollary of the results is that the ‘true’ risk premium contains a long memory component. This is non-standard and has implications for the construction of rational expectations models of the foreign exchange market.     

How well do investor sentiment and ensemble learning predict Bitcoin prices?

Investor sentiment is widely recognized as the major determinant of cryptocurrency prices. Although earlier research has revealed the influence of investor sentiment on cryptocurrency prices, it has not yet generated cohesive empirical findings on an important question: How effective is investor sentiment in predicting cryptocurrency prices? To address this gap, we propose a novel prediction model based on the Bitcoin Misery Index, using trading data for cryptocurrency rather than judgments from individuals who are not Bitcoin investors, as well as bagged support vector regression (BSVR), to forecast Bitcoin prices. The empirical analysis is performed for the period between March 2018 and May 2022. The results of this study suggest that the addition of the sentiment index enhances the predictive performance of BSVR significantly. Moreover, the proposed prediction system, enhanced with an automatic feature selection component, outperforms state-of-the-art methods for predicting cryptocurrency for the next 30 days.     

Can salience theory explain investor behaviour? Real-world evidence from the cryptocurrency market

Research on human attention indicates that objects that stand out from their surroundings, i.e., salient objects, attract the attention of our sensory channels and receive undue weighting in the decision-making process. In the financial realm, salience theory predicts that individuals will find assets with salient upsides (downsides) appealing (unappealing). We investigate whether this theory can explain investor behaviour in the cryptocurrency market. Consistent with the theory's predictions, using a sample of 1738 cryptocurrencies, we find that cryptocurrencies that are more (less) attractive to “salient thinkers” earn lower (higher) future returns, which indicates that they tend to be overpriced (underpriced). On average, a one cross-sectional standard-deviation increase in the salience theory value of a cryptocurrency reduces its next-week return by 0.41%. However, the salience effect is confined to the micro-cap segment of the market, and its size is moderated by limits to arbitrage.     

Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices

We address the question whether the evolution of implied volatility can be forecasted by studying a number of European and US implied volatility indices. Both point and interval forecasts are formed by alternative model specifications. The statistical and economic significance of these forecasts is examined. The latter is assessed by trading strategies in the recently inaugurated CBOE volatility futures markets. Predictable patterns are detected from a statistical point of view. However, these are not economically significant since no abnormal profits can be attained. Hence, the hypothesis that the volatility futures markets are efficient cannot be rejected.     

Can happiness predict future volatility in stock markets?

In this paper, we use the Twitter based happiness index as a proxy for investor sentiment in order to examine whether happiness influences future market volatility of country VIX indexes. Our sample includes the major stock markets of the USA, Canada, UK, Germany, France, Netherlands, Switzerland, Japan, China, Hong Kong, India, Brazil, South Korea, and South Africa. Using linear and nonlinear causality tests, we find that Twitter happiness significantly causes the future volatility of the sample countries. The robustness checks show no divergence from our primary findings and provide strong evidence of a nonlinear relationship between investor sentiment and future stock market volatility.     

Can interest rate volatility be extracted from the cross section of bond yields?

Most affine models of the term structure with stochastic volatility predict that the variance of the short rate should play a ‘dual role’ in that it should also equal a linear combination of yields. However, we find that estimation of a standard affine three-factor model results in a variance state variable that, while instrumental in explaining the shape of the yield curve, is essentially unrelated to GARCH estimates of the quadratic variation of the spot rate process or to implied variances from options. We then investigate four-factor affine models. Of the models tested, only the model that exhibits ‘unspanned stochastic volatility’ (USV) generates both realistic short rate volatility estimates and a good cross-sectional fit. Our findings suggest that short rate volatility cannot be extracted from the cross-section of bond prices. In particular, short rate volatility and convexity are only weakly correlated.     

Financial investor sentiment and the boom/bust in oil prices during 2003–2008

From both theoretical and policy perspectives, it is important to understand if financial speculation contributes to the boom/bust in oil prices during 2003–2008. In this paper, we disentangle the effects of financial speculation from those of economic fundamentals by focusing on exogenous changes of financial speculation in oil markets identified by changes in financial investor sentiment. Furthermore, we focus on a quasi-experiment setting, and investigate both sentiment-driven overvaluation of oil prices and subsequent mispricing correction. Our findings suggest that financial speculation might have contributed to the 2003–2008 boom/bust in oil prices.     

VCRIX — A volatility index for crypto-currencies

Public interest, explosive returns, and diversification opportunities gave stimulus to the adoption of traditional financial tools to crypto-currencies. While the CRIX offered the first scientifically-backed proxy to the crypto-market (analogous to S&P 500), measuring the forward-oriented risk in the crypto-currency market posed a challenge of a different kind. Following the intuition of the “fear index” VIX for the American stock market, the VCRIX volatility index was created to capture the investor expectations about the crypto-currency ecosystem. VCRIX is built based on CRIX and offers a forecast based on the Heterogeneous Auto-Regressive (HAR) model. The HAR model was selected as the most suitable out of a horse race of volatility models, with two proxies for implied volatility, namely the 30 days mean annualized volatility and realized volatility. The model was further examined by the simulation of VIX (resulting in a correlation of 78% between the actual VIX and a “VIX” version estimated with the VCRIX technology). Trading strategies confirmed the predictive power of VCRIX and supported the selection of the 30 days means annualized volatility proxy. The best performing trading strategy with the use of VCRIX outperformed the benchmark strategy for 99.8% of the tested period and 164% additional returns. VCRIX provides forecasting functionality and serves as a proxy for the investors’ expectations in the absence of a developed crypto derivatives market. These features provide enhanced decision making capacities for market monitoring, trading strategies, and potentially option pricing.     

Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility

This paper investigates the impacts of News-based Implied Volatility (NVIX) on the long-term volatility of five cryptocurrencies using the GARCH-MIDAS model. We also evaluate the hedging effectiveness of cryptocurrencies against the S&P 500 index after incorporating NVIX. The empirical results show that NVIX has a negative and significant impact on the long-term volatility of five cryptocurrencies. The impact of NVIX remains robust even after controlling for Global Economic Policy Uncertainty (GEPU) and Realized Volatility (RV). The uncertainty derived from investor perception is more important than the uncertainty of economic fundamentals in predicting cryptocurrency volatility. The hedging effectiveness of Bitcoin against the S&P 500 index is improved due to consideration of NVIX. This paper provides new evidence concerning the impacts of uncertainty on the volatility of cryptocurrencies.     

Does foreign investor demand for information affect forecast accuracy? Evidence from the Chinese stock markets

Prior international accounting studies have examined mainly the supply side of analyst research. Specific trading rights and financial reporting systems in China provide a unique opportunity to test both demand and supply factors of analyst information. For shares traded only by less-informed foreign investors, the increased information demand may create incentives for analysts to exert greater efforts than for shares traded by local investors. This study provides evidence that analyst effort (proxied by analyst coverage) and expertise (proxied by consensus cross-analyst forecast variability) affect forecast accuracy more significantly for shares traded by foreign investors than for shares traded by local investors. Additionally, I find that the relation between analyst characteristics and relative forecast accuracy is stronger for shares traded by the foreign investors. The findings are consistent with analysts responding to demands for information by less-informed investors.