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Home is Where You Know Your Volatility - Local Investor Sentiment and Stock Market Volatility
Abstract
Using a new variable to measure investor sentiment we show that the sentiment of German and European investors matters for return volatility in local stock markets. A flexible empirical similarity (ES) approach is used to emulate the dynamics of the volatility process by a time-varying parameter that is created via the similarity of realized volatility and investor sentiment. Out-of-sample results show that the ES model produces significantly better volatility forecasts than various benchmark models for DAX and EUROSTOXX. Regarding other international markets no significant difference between the forecasts can be observed.
... The sentiment would cause a deviation of the price from its fundamentals and a non-homogeneous interpretation of the information, consequently increasing assets volatility and generating temporary mispricing (Baker & Wurgler, 2006;Kumari & Mahakud, 2015). In this context, investor sentiment can be understood as the component of assets' price that is not justified by its fundamental (Smales, 2016) and as a systematic risk of assets that are priced by the market (Lee, Jiang, & Indro, 2002;Schneller, Heiden, & Hamid, 2018;Yu & Yuan, 2011). ...
... In this study, asymmetry refers to the reduction, total or partial, of the explanatory capacity of the investors' sentiment about the volatility of the Brazilian stock market. This asymmetry can occur when two factors are considered: changes in the sentiment between optimistic and pessimistic and when specific companies characteristics associated with pricing difficulties are considered (Aydogan, 2017;Baker & Wurgler, 2006, 2007Kumari & Mahakud, 2015;Lee et al., 2002;Piccoli et al., 2018;Smales, 2016;Schneller et al., 2018;Yu & Yuan, 2011). The asymmetric behavior would appear, given that, in periods of optimistic sentiment, there is a higher entry of noise traders and lower volatility, while in periods of pessimistic sentiment, there is greater uncertainty and, consequently, greater volatility (De Long et al., 1990). ...
... The research closer to the aim of the present study were developed in international markets and mainly focus on the analysis of the relationship between sentiment and return on assets, mostly identifying a positive and significant relationship between these variables (Baker & Wrugler, 2006, 2007Garcia, 2013;Neves et al., 2016;Piccoli et al., 2018). However, more recently, the focus has shifted to understand the role of investor sentiment in the volatility return (Aydogan, 2017;Kumari & Mahakud, 2015;Lee et al., 2002;Smales, 2016;Schneller et al., 2018;Yu & Yuan, 2011), which is the purpose of this study. ...
Purpose: The purpose of this study was to analyze the effect of investor sentiment on the volatility of the Brazilian stock market. Specifically, it aimed to identify if the asymmetric behavior of sentiment could be observed in emerging markets, considering companies that have characteristics that are difficult to price.
Originality/value: Unlike most studies on investor sentiment, this study focuses on its impact on the stock market volatility, as well as on the characteristics of companies associated with difficult pricing.
Design/methodology/approach: The volatility of the IBRX100 index was used to represent the Brazilian stock market, and as a proxy for investor sentiment it was selected Miranda's index (2018), based on market data. Data were estimated using the two-stage least squares (MQ2E) technique to address endogeneity problems. Finally, the volatility of companies with difficult-to-price characteristics was segregated to analyze their sensitivity to sentiment.
Findings: The results indicate that sentiment has a negative and significant relationship with the volatility of the Brazilian market, as well as evidences an asymmetrical behavior, being statistically stronger in pessimistic periods. Additional analyzes evidence that the explanatory sentiment capacity is sensitive to companies' characteristics, but only companies with a high book-to-market ratio showed asymmetric behavior, as expected by the literature. The portfolios segmented by size and illi-quidity maintained an asymmetric behavior, but it was the volatility of the large companies and the less illiquid ones that were best explained by sentiment, indicating that the Brazilian market has distinctive characteristics in relation to developed markets.
... Nesse contexto, o sentimento do investidor pode ser entendido como o componente do preço dos ativos, oriundo das expectativas sobre os retornos, que não estão justificados pelos seus fundamentos (Samles, 2016). Além disso, o sentimento do investidor pode ser entendido como um risco sistemático dos ativos que é precificado pelo mercado (Lee et al., 2002;Schneller et al., 2018; Yu & Yuan,2011). ...
... Os estudos que buscam verificar o impacto do sentimento do investidor nos mercados de capitais focam majoritariamente na análise da relação entre o sentimento e o retorno dos ativos (Baker & Wrugler, 2006, 2007Garcia, 2013;Neves et al., 2016;Piccoli et al., 2018;Stambaugh et al., 2012), identificando, em sua maioria, uma relação positiva e significante entre as variáveis. No entanto, mais recentemente, o foco alterou-se para a compreensão do papel do sentimento do investidor na volatilidade dos retornos (Aydogan, 2017;Kumari & Mahakud, 2015;Lee et al., 2002;Samles, 2016;Schneller et al., 2018;Yu & Yuan, 2011), que é o objeto deste estudo. ...
... Ao investigar uma amostra de nove países membros da Organização para a Cooperação e Desenvolvimento Econômico (OECD), Aydogan (2017) identificou um comportamento assimétrico em todos os mercados, apresentando resultados mais significativos quando o sentimento era pessimista. Ainda para o mercado europeu, Schneller et al. (2018) constataram que o sentimento do investidor possui poder explicativo sobre a volatilidade do mercado alemão, bem como para os demais mercados europeus, considerando a volatilidade construída com dados de alta frequência. Kumari e Mahakud (2015) buscaram analisar a influência do sentimento do investidor no mercado acionário indiano e também observaram um poder explicativo assimétrico com maior intensidade em períodos de sentimento pessimista, sugerindo que a assimetria pode ser identificada tanto em mercados mais desenvolvidos quanto em mercados emergentes. ...
– Problema de Pesquisa e Objetivo: o presente estudo teve por objetivo analisar o efeito do sentimento do investidor sobre a volatilidade do mercado acionário brasileiro. Especificamente, buscou-se identificar se o comportamento assimétrico do sentimento pode ser observado em mercados emergentes, bem como considerando empresas com características de difícil precificação.
– Metodologia: Para representar o mercado acionário brasileiro, optou-se por utilizar a volatilidade do índice do IBRX100, tendo em visa que reflete os retornos de uma carteira teórica composta pelas cem ações mais negociadas na Brasil Bolsa Balcão (B3). Como proxy para sentimento do investidor (Sent), fez-se uso do modelo de Miranda (2018) baseado em variáveis de mercado. Assim, os dados foram estimados utilizando a técnica de mínimos quadrados em dois estágios (MQ2E). Por fim, segregou-se a volatilidade das empresas com características de difícil precificação, para analisar sua sensibilidade ao Sent. Utilizar a volatilidade do índice do IBRX100 entre os Para a realização da pesquisa, utilizou-se um indice de sentimento do investido baseado em vriáveis de mercado para a volatilidade do índice do IBRX100 entre Jan 2006 à Dezembro 2017. Para as análises associadas as empresas com características de difícil precificação, foram utilizados portfólios formados pelo tamanho, book-to-market e iliquidez das empresas.
– Análise dos Resultados: Os resultados sugerem que o sentimento do investidor tem uma relação negativa e significativa com a volatilidade do mercado acionário brasileiro, bem como apresenta um comportamento assimétrico, sendo mais forte estatisticamente para períodos pessimistas. Ademais, observou-se que as empresas com alto padrão de crescimento (book-to-market) e que possuem títulos com liquidez reduzida também apresentam maior sensibilidade ao sentimento do investidor do que suas características opostas, ratificando o comportamento assimétrico.
– Conclusão: O sentimento pessimista apresentou maior capacidade explicativa sobre volatilidade, indicando que o impacto assimétrico do sentimento do investidor ocorre em mercados emergentes de forma similar aos mercados mais desenvolvidos. Considerando características associadas à difícil precificação, também identificou-se relações significantes de forma mais pronunciada quando o sentimento era pessimista, indicando haver uma sensibilidade à características das empresas, dado relevante quando se está buscando maior precisão na precificação dos ativos.
– Contribuição / Impacto: Este estudo busca contribuir para a identificação e quantificação dos fatores associados ao mispricing do mercado brasileiro, possibilitando uma melhor alocação de recursos, bem como a elaboração de políticas pelos órgãos reguladores. Assim, os resultados podem ampliar a precisão de suas previsões e a elaboração de estratégias para obtenção de ganhos financeiros, além de ser importantes para reguladores de mercado, que podem elaborar medidas que visem à estabilização do sentimento do investidor, reduzindo a volatilidade e incertezas do mercado.
... They achieved this by examining changes in exchange prices for two currency pairs, EUR-USD and USD-JPY, using weekly Sentix indexes. According to Schneller et al.'s (2018) analysis of the connection between investor emotion and stock return volatility, investor mood can be used to forecast stock return volatility. ...
Employing the R² decomposed linkage methodology, our study endeavors to elucidate interrelations, particularly distinguishing between concurrent and delayed connections. This novel approach is utilized to scrutinize the transmission mechanism of returns between the Investor Sentiment Index (ISI) and VN Index (VNI), as well as the five most frequently exchanged foreign currencies vis-à-vis the Vietnamese Dong, namely USD/VND, EUR/VND, GBP/VND, JPY/VND, and CNY/VND. The investigation spans from January 1st, 2017, to November 25th, 2023. It is discerned that delayed connections exert a more pronounced influence across all instances. Investment sentiment exhibits a relatively constrained impact on shocks, regardless of its role as a transmitter or receiver, with its significance primarily manifesting through lagged relationships. Three distinct time periods showcase the conspicuous net shock receiver effect of investment sentiment: the latter part of 2018, the latter portion of 2019 to early 2020, and the initial half of 2023. In aggregate, the COVID-19 epoch witnesses an escalated significance of investment sentiment. Notably, the net shock transmitter function of investment sentiment predominates solely during intervals encompassing the latter part of 2017 to the early part of 2018 and the latter segment of 2020.
... Baker et al. (2006) adopt the comprehensive investor sentiment index method and find that there is a common change relationship between investor sentiment and stock returns. Schneller et al. (2018) then use the empirical similarity approach to document that the sentiment of European investors significantly affects the volatility of European stock returns. Another interesting study about the factor that may influence investor sentiment and eventually affect the stock return was conducted by Drakos (2010); he finds that terrorist activities affect investor sentiment, terrorism led to a significant reduction in returns on the day of the attack. ...
This paper investigates the effect of investor investment sentiment on individual stock returns in China. We find that investment sentiment is positively associated with stock performance contemporaneously. The Granger Causality test shows that investor sentiment is a driving force of stock price movement but not the other way around. Our constructed VAR model further suggests that the change in investor sentiment in the lagged period does not significantly affect the current stock return. In addition, both the Impulse Response and the Variance Decomposition analysis provide evidence that the stock price will increase right after a positive sentiment shock, and then follow a price drop until an intrinsic value comes up. Overall, the investor sentiment factor has a strong explanatory power to explain the variation in stock return.
... empirical investor sentiment and its effects on the financial markets is a hot topic in the last two decades Wugler, 2006, 2007;Wang, Keswani, and taylor, 2006; škrinjarić and čižmešija, 2019; Su, Cai, and tao, 2020; Schneller, et al., 2018). this is due to the development of the methodology of how to observe and measure specific sentiment and feeling variables. ...
This paper observes the possibilities of exploiting a behavioural anomaly on the stock market. Previous literature confirms the existence of Seasonal Affective Disorder (SAD) in return series on the Zagreb Stock Exchange. However, a comprehensive set of investment guidance based on such findings is lacking in the related literature. That is why after the confirmation of the existence of SAD effects in this research, the focus is on simulation of trading strategies that take such information into account. The results indicate that there exist SAD and fall effects on the Croatian stock market, alongside precipitation and temperature having significant effects on stock returns as well. Based on daily data ranging from Jan-uary 2010 until December 2020 for the Croatian stock market index CROBEX, several strategies are observed and compared via performance measures aimed at beating the market. Even with the inclusion of transaction costs, it is shown and commented on possibilities for speculators aiming to obtain extra profits in certain situations. Simpler strategies are considered in the study. However, they provide a starting point for future strategies that combine different (mostly calendar) anomalies with the SAD anomaly. This is due to showing that SAD-driven investors who aim to apply the contrarian strategies against the herd can obtain profits due to the changing risk-aversion of others over the year.
... 26 Elon Musk's tweet on 7 August 2018 about taking Tesla private led to an increase of the share by 11 %. Short sellers lost approximately 1.3 billion USD.Research on financial markets shows that GSV and TV have an impact on financial markets(Bollen et al., 2011;Da, Engelberg, & Gao, 2011;Dimpfl & Jank, 2011Dimpfl & Kleiman, 2017;Hamid & Heiden, 2015;Kumar & Lee, 2006;Mao, Counts, & Bollen, 2015;Schneller, Heiden, Heiden, & Hamid, 2018). ...
The idea of this thesis is to use new data sources to approximate investor beliefs. It investigates whether the approximation improves the measurement of return and volatility in existing model frameworks. The findings are that differences in implied volatility, Google Search volume and Twitter Volume can be proxy variables for investor beliefs. They have an impact on financial market indicators and on the prediction of future market movements. Comparison of the trading behaviour of individual and institutional investors to predict market movements The first approach is to create a new sentiment index which compares the difference between retail investor behaviour at the Stuttgart Stock Exchange (SSE) and professional investors at the Frankfurt Stock Exchange (FSE). The measure is a comparison between the implied volatility measures for the DAX at the FSE (VDAX and VDAX-NEW) and a newly created implied volatility index (VSSE) for the SSE. The sentiment index is significant in predicting the daily returns on a size-based long-short portfolio over a four-year period. The analysis shows the persistent inconsistence between prices of structured products for retail investors on the SSE and option prices of professional investors on the FSE. The results provide empirical evidence that there are significant persistent behavioural differences between the two investor types which is reflected in persistent mispricing. Measurability of investor beliefs and their impact on financial markets The second approach is to measure individual investor beliefs with Google search volume (GSV) and Twitter volume (TV) to analyse their impact on financial markets. The basis is a daily panel of 29 Dow Jones Industrial average index (DJIA) stocks over a time period of 3.5 years in a panel data set-up. The impact on trading activity measured by turnover, is positive for GSV and TV on the same day and the next day which indicates their predictive power. The impact on realized volatility (RV), indicating the share of noise traders on the market, is only positive and significant for TV. It is significant on the same day and the next day. The impact of GSV is not significant. The results support the idea that GSV and TV capture the beliefs of individual investors. Although they suggest that the impact of TV on financial markets is more important than the impact of GSV. Predictive power of Google and Twitter The third approach is to use GSV and TV as a proxy for investor attention and investor sentiment, to assess their predictive power on the RV of the DJIA. The basis is a time-series set-up with a vector autoregression (VAR) model over a period of 2.5 years. The findings show that GSV and TV granger cause RV, controlling for macroeconomic and financial factors. Again, the effect of TV on RV is more important than the effect of GSV. In-sample, the linear prediction model with GSV and TV outperforms a standard AR (1) process. Out-of-sample the AR (1) process outperforms the standard model with GSV and TV. Clustering for high and low volatility groups, the analysis shows that the effect of GSV and TV on RV changes. Especially in times of high and low RV, GSV and TV seem to contain new information, as they improve the model fit compared to a standard AR (1) process. However, the results are not persistent in- and out-of-sample. This underlines that the results of GSV and TV are not generally persistent but depend on the selected criteria. Overall, the results of this thesis show that investor beliefs have an impact on financial markets. The measures, such as a sentiment index based on implied volatility, GSV and TV are proxy variables for investor beliefs. Future research should further improve the comprehension of investor beliefs to improve causality and economic significance in the long term.
... For example, fund cash flow, turnover, and market sentiment may significantly influence stock returns (Baker et al., 2012;Stambaugh, Yu, & Yuan, 2012). More recently, studies by researchers have turned their attention to the question of the role sentiment plays in market volatility, with Schneller, Heiden, Hamid, and Heiden (2018), for example, finding that the sentiment of German and European investors contributes significantly to the forecasting power for the volatility of returns volatility in local stock markets. ...
A significant body of empirical research has shown that investor sentiment is an essential factor to explain stock price that the classical financial theory cannot explain. This research examines the relationship between stock return and investor sentiment using the Vietnamese stock exchange data. We create a sentiment index using the principal components analysis (PCA). Consistent with the sentiment and stock return literature, the research shows a negative contemporaneous relationship between investor sentiment and market return.
... For the USD/JPY market, both studies found no evidence of investor sentiment in predicting future returns. Papers mentioned above focused on stock returns and there is only one paper by Schneller et al. (2018), in which authors use Sentix data to see if there exists an impact of investors' sentiment on stock returns volatility. This empirical research shows that investor sentiment has the ability to forecast stock returns volatility. ...
Purpose
This paper aims to analyze the effects of investors’ sentiment, return and risk series on one to another of selected exchange rates. The empirical analysis consists of a time-varying inter-dependence between the observed variables, with the focus on spillovers between the variables.
Design/methodology/approach
Monthly data on the index Sentix, exchange rates EUR–USD, EUR–CHF and EUR–JPY are analyzed from February 2003 to December 2019. The applied methodology consists of vector autoregression models (VAR) with Diebold and Yilmaz (2009, 2011) spillover indices.
Findings
The results of the empirical research indicate that using static analysis could result in misleading conclusions, with dynamic analysis indicating that the financial of 2007-2008 and specific negative events increase the spillovers of shock between the observed variables for all three exchange rates. The sources of shocks in the model change over time because of variables changing their positions being net emitters and net receivers of shocks.
Research limitations/implications
The shortfalls of this study include using the monthly data frequency, as this was available for the authors, namely, investors are interested to obtain new information on a weekly and daily basis, not only monthly. However, at the time of writing this research, we could obtain only monthly data.
Practical implications
As the obtained results are in line with previous literature and were found to be robust, there exists the potential to use such analysis in the future when forecasting risk and return series for portfolio management purposes. Thus, a basic comparison was made regarding the investment strategies, which were based on the results from the estimation. It was shown that using information about shock spillovers could result in strategies that can obtain better portfolio value over time compared to basic benchmark strategies.
Originality/value
First, this paper allows for the spillovers of shocks in variables within the VAR models in all directions. Second, a dynamic analysis is included in the study. Third, the mentioned spillover indices are included in the study as well.
... The predictive power of investor sentiment in the return on shares has aroused great interest and a growing volume of literature devoted to this subject; however, the results obtained have not always engendered a consensus around this topic, bearing in mind the different methodologies used to build indices of sentiment as well as the various levels of institutional development of the market. Lee, Shleifer & Thaler (1991), Han (2007) and Schneller, Heiden, Heiden & Hamid (2017) describe the investor sentiment as the expectation of return on investments in the absence of logical grounds, expressed in the aggregate errors of their beliefs. This sentiment is associated with the irrational part of investor expectations not related with logical fundamentals, and linked with the difference between the fundamental value of an asset and the its value to an investor with irrational expectations. ...
This study assesses the impact of investor sentiment on the volatility of the PSI 20 and IBEX 35 from time series data from January 1988 to May 2019. The impact of investor sentiment on market and portfolio selection has aroused great interest in the literature, however the results obtained are not consensual, considering the different methodologies used to build sentiment indices, as well as the various levels of institutional development in the market.
Asymmetric volatility behaviours according to good or bad news were evaluated using the TGARCH model. The results indicate that there is an asymmetric effect of good versus bad news on the volatility of IBEX 35. It was also noted that for Portugal and Spain investor sentiment presents statistical significance with a negative sign, suggesting that market volatility is more sensitive to negative shocks in the conditional variance. In Portugal, contrary to Spain, sentiment has no relevance on return. The study reveals that investor sentiment is a key factor in selecting investment in the market. The relationship that this establishes with volatility, can help to implement policies that allow to minimize future shocks’ impact on return. The study reveals for the first time that investor sentiment is a key factor in selecting investment in the market for Portugal.
... Very recently, realistic out-of-sample forecasting approaches to realized volatility and sentiment data have begun to be employed. For example, Schneller et al. (2018) use sentiment data from a survey of (mainly) German and European investors, and find that investor sentiment can be used to profit from a local information advantage when forecasting realized volatility. ...
The information contained in investor sentiment has up to now hardly been used for portfolio optimization, although theoretical works demonstrate that it should not be neglected and it has already been shown to contain exploitable information on future returns and volatility. Employing the approach of Copula Opinion Pooling, we explore how sentiment information regarding international stock markets can be directly incorporated into the portfolio optimization procedure. We subsequently show that sentiment information can be exploited by a trading strategy that takes into account a medium-term reversal effect of sentiment on returns. This sentiment-based strategy outperforms several benchmark strategies in terms of different performance and downside risk measures. More importantly, the results remain robust to changes in the parameter specification.
This paper investigates the relationship between investors' attention, as measured by Google search queries, and equity implied volatility during the COVID-19 outbreak. Recent studies show that search investors' behavior data is an extremely abundant repository of predictive data, and investor-limited attention increases when the uncertainty level is high. Our study using data from thirteen countries across the globe during the first wave of the COVID-19 pandemic (January–April 2020) examines whether the search “topic and terms” for the pandemic affect market participants’ expectations about future realized volatility. With the panic and uncertainty about COVID-19, our empirical findings show that increased internet searches during the pandemic caused the information to flow into the financial markets at a faster rate and thus resulting in higher implied volatility directly and via the stock return-risk relation. More specifically for the latter, the leverage effect in the VIX becomes stronger as Google search queries intensify. Both the direct and indirect effects on implied volatility, highlight a risk-aversion channel that operates during the pandemic. We also find that these effects are stronger in Europe than in the rest of the world. Moreover, in a panel vector autoregression framework, we show that a positive shock on stock returns may soothe COVID-related Google searches in Europe. Our findings suggest that Google-based attention to COVID-19 leads to elevated risk aversion in stock markets.
This research deals with the effects of investor’s sentiment on the return and risk series of selected exchange rates. The analysis allows for a time-varying inter-dependence between the observed variables to estimate the spillovers between them. The main goal is to determine if the investor’s sentiment affects the return and risk series and if there exists feedback as well. For this purpose, monthly data on the index Sentix and exchange rates EUR-USD, EUR-CHF and EUR-JPY were collected for the period February 2003-December 2019. The applied methodology consists of vector autoregression models (VAR) with Diebold and Yilmaz (2009, 2012) spillover indices. The main findings show that using static analysis could result in misleading conclusions. Furthermore, the dynamic analysis shows that the financial crisis of 2007-2008 and specific negative events increase the spillovers of shock between the observed variables for all three exchange rates. The net receivers and emitters of shocks in the system also change over time. Thus, the dynamic analysis should be taken into consideration in future analysis and pricing models. Finally, the robustness of the results was checked. Thus, results of this study could be used in dynamic portfolio rebalancing over time in order to achieve specific investment goals.
This research deals with an empirical comparison of the forecasting abilities of standard GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) and GM-GARCH (Grey Model) models. Although the standard GARCH models are probably the most popular approach in stock risk modelling and forecasting, there have been many different extensions over the years of those models. One possible direction is the Grey Systems Theory, which focuses on uncertain and grey data. Thus, it is very suitable for stock market applications. Based on daily data for the period 4 January 2017 – 5 May 2020 for the stock market index CROBEX, GARCH and GM-GARCH models are estimated and compared based on out-of- sample forecast capabilities. The results indicate that the GM based model is superior compared to its counterpart. That is why it is advisable to incorporate the models from Grey Systems Theory into the investment strategies which focus on hedging or minimising the portfolio risk
Considering the spatial interaction of stocks, we develop a spatio-temporal dynamic panel model to study the effects of investor sentiment on stock return volatility. We construct a sample using data on A-share stocks from the Shanghai Stock Exchange from January 2012 to December 2018. We find that investor sentiment positively influences stock return volatility. Moreover, this influence exhibits both a temporal cumulative and spatial spillover effect. We also find that this spatio-temporal effect is more sensitive to economic than geographic factors. Our findings demonstrate the presence of a spatio-temporal effect on stock return volatility and emphasize the importance of understanding this effect to avoid risk underestimation. Thus, the proposed model can be of use for both investors and policymakers.
Employing a nonlinear multi-criteria optimization approach, sentiment-based disagreement is incorporated into portfolio optimization as additional risk factor. A multi-criteria trading strategy outperforms several benchmarks regarding various performance measures. Applying the strategy over a long time period including downturns and upswings, disagreement proves itself especially valuable in bear markets as it is an indicator for future volatility.
We test the predictive ability of investor sentiment on the return and volatility at the aggregate market level in the U.S., four largest European countries and three Asia-Pacific countries. We find that in the U.S., France and Italy periods of high consumer confidence levels are followed by low market returns. In Japan both the level and the change in consumer confidence boost the market return in the next month. Further, shifts in sentiment significantly move conditional volatility in most of the countries, and in Italy such impacts lead to an increase in returns by 4.7% in the next month.
Purpose
The purpose of this paper is to investigate the impact of individual investor sentiment on the return process and conditional volatility of three main US market indices (Dow Jones Industrial Average, S&P500 and Nasdaq100). Individual investor sentiment is measured by aggregate money flows in and out of domestically oriented US mutual funds.
Design/methodology/approach
A generalised autoregressive conditional heteroscedasticity (GARCH)‐in‐mean specification is used, where our measure for individual sentiment enters the mean and conditional volatility equation.
Findings
For a sample period of six years (February 1998 until December 2004), we find that sentiment has a significant and asymmetric impact on volatility, increasing it more when sentiment is bearish. Using terminology of De Long et al. , we find evidence for the “hold more” effect, which states that when noise traders hold more of the asset, they also see their returns increase, and the “create space” effect, which states that noise traders are rewarded for the additional risk they generate themselves.
Originality/value
In contrast to existing studies using explicit measures of market sentiment on low sampling frequencies, the use of daily mutual flow data offers a unique picture on investors' portfolio rebalancing and trading behavior. We propose an integrated framework that jointly tests for the effects of mutual fund flows on stock return and conditional volatility.
The paper tests whether individuals have value-relevant information about local stocks (where “local” is defined as being headquartered near where an investor lives). Our methodology uses two types of calendar-time portfolios—one based on holdings and one based on transactions. Portfolios of local holdings do not generate abnormal performance (alphas are zero). When studying transactions, purchases of local stocks significantly underperform sales of local stocks. The underperformance remains when focusing on stocks with potentially high levels of information asymmetries. We conclude that individuals do not help incorporate information into stock prices. Our conclusions directly contradict existing studies.
The paper proposes an additive cascade model of volatility components defined over different time periods. This volatility
cascade leads to a simple AR-type model in the realized volatility with the feature of considering different volatility components
realized over different time horizons and thus termed Heterogeneous Autoregressive model of Realized Volatility (HAR-RV).
In spite of the simplicity of its structure and the absence of true long-memory properties, simulation results show that the
HAR-RV model successfully achieves the purpose of reproducing the main empirical features of financial returns (long memory,
fat tails, and self-similarity) in a very tractable and parsimonious way. Moreover, empirical results show remarkably good
forecasting performance.
This paper examines the importance of diff erent economic sentiments, e.g. consumer moods, for the Central and Eastern European countries (CEECs) during the transition process. We fi rst analyze the importance of economic confi dence with respect to the CEECs’ fi nancial markets. Since the integration of formerly strongly-regulated markets into global markets can also lead to an increase in the dependence of the CEECs’ domestic market performance on global sentiments, we also investigate the relationship between global economic sentiments and domestic income and share prices. Finally, we test whether the impact of global sentiments and stock prices on domestic variables increases proportionally with the degree of integration. We also account for eff ects stemming from global income. For these purposes, we apply a restricted cointegrating VAR (CVAR) framework based upon a restricted autoregressive model which allows us to distinguish between the long-run and the short-run dynamics. For the long run we fi nd evidence supporting relationships between sentiments, income and share prices in the case of the Czech Republic. Our results for the short run suggest that economic sentiments in general are infl uenced by share prices but also off er some predictive power with respect to the latter. What is more, European sentiments play an important role in particular for the CEECs’ share prices and income. The signifi cance of this link increases with economic integration.
This article focuses on some aspects of high-frequency data and their use in volatility forecasting. High-frequency data can be used to construct volatility forecasts. The article reviews two leading approaches to this. One approach is the reduced-form forecast, where the forecast is constructed from a time series model for realized measures, or a simple regression-based approach such as the heterogeneous autoregressive model. The other is based on more traditional discrete-time volatility models that include a modeling of returns. Such models can be generalized to utilize information provided by realized measures. The article also discusses how volatility forecasts, produced by complex volatility models, can benefit from highfrequency data in an indirect manner, through the use of realized measures to facilitate and improve the estimation of complex models.
Recently, Perron has carried out tests of the unit-root hypothesis against the alternative hypothesis of trend stationarity with a break in the trend occurring at the Great Crash of 1929 or at the 1973 oil-price shock. His analysis covers the Nelson-Plosser macroeconomic data series as well as a postwar quarterly real gross national product (GNP) series. His tests reject the unit-root null hypothesis for most of the series. This article takes issue with the assumption used by Perron that the Great Crash and the oil-price shock can be treated as exogenous events. A variation of Perron's test is considered in which the breakpoint is estimated rather than fixed. We argue that this test is more appropriate than Perron's because it circumvents the problem of data-mining. The asymptotic distribution of the estimated breakpoint test statistic is determined. The data series considered by Perron are reanalyzed using this test statistic. The empirical results make use of the asymptotics developed for the test statistic as well as extensive finite-sample corrections obtained by simulation. The effect on the empirical results of fat-tailed and temporally dependent innovations is investigated. In brief, by treating the breakpoint as endogenous, we find that there is less evidence against the unit-root hypothesis than Perron finds for many of the data series but stronger evidence against it for several of the series, including the Nelson-Plosser industrial-production, nominal-GNP, and real-GNP series.
This paper proposes an empirical similarity approach to forecast weekly volatility by using search engine data as a measure of investors attention to the stock market index. Our model is assumption free with respect to the underlying process of investors attention and significantly outperforms conventional time-series models in an out-of-sample forecasting framework. We find that especially in high-volatility market phases prediction accuracy increases together with investor attention. The practical implications for risk management are highlighted in a Value-at-Risk forecasting exercise, where our model produces significantly more accurate forecasts while requiring less capital due to fewer overpredictions.
Applying a geographic lens to mutual fund performance, this study finds that fund managers earn substantial abnormal returns in nearby investments. These returns are particularly strong among funds that are small and old, focus on few holdings, and operate out of remote areas. Furthermore, we find that while the average fund exhibits only a modest bias toward local stocks, certain funds strongly bias their holdings locally and exhibit even greater local performance. Finally, we demonstrate that the extent to which a firm is held by nearby investors is positively related to its future expected return. Our results suggest that investors trade local securities at an informational advantage and point toward a link between such trading and asset prices.
We study how heterogeneous beliefs affect returns and examine whether they are a priced factor in traditional asset pricing
models. To accomplish this task, we suggest new empirical measures based on the disagreement among analysts about expected
earnings (short-term and long-term) and show they are good proxies. We first establish that the heterogeneity of beliefs matters
for asset pricing and then turn our attention to estimating a structural model in which we use the forecasts of financial
analysts to proxy for agents’ beliefs. Finally, we investigate whether the amount of heterogeneity in analysts’ forecasts
can help explain asset pricing puzzles.
We examine the relation between daily sentiment and trading behavior within 20 international markets by exploiting Facebook's Gross National Happiness Index. We find that sentiment has a positive contemporaneous relation to stock returns. Moreover, sentiment on Sunday affects stock returns on Monday, suggesting causality from sentiment to stock markets. We observe that the relation between sentiment and returns reverses the following weeks. We further show that negative sentiments are related to increases in trading volume and return volatility. These results highlight the importance of behavioral factors in stock investing.
In this paper we adapt the empirical similarity (ES) concept for the purpose of combining volatility forecasts originating from different models. Our ES approach is suitable for situations where a decision maker refrains from evaluating success probabilities of forecasting models but prefers to think by analogy. It allows to determine weights of the forecasting combination by quantifying distances between model predictions and corresponding realizations of the process of interest as they are perceived by decision makers. The proposed ES approach is applied for combining models in order to forecast daily volatility of the major stock market indices.
The paper investigates whether risk-neutral skewness has incremental explanatory power for future volatility in the S&P 500 index. While most of previous studies have investigated the usefulness of historical volatility and implied volatility for volatility forecasting, we study the information content of risk-neutral skewness in volatility forecasting model. In particular, we concentrate on Heterogeneous Autoregressive model of Realized Volatility and Implied Volatility (HAR-RV-IV). We find that risk-neutral skewness contains additional information for future volatility, relative to past realized volatilities and implied volatility. Out-of-sample analyses confirm that risk-neutral skewness improves significantly the accuracy of volatility forecasts for future volatility.
This paper tests the performance of individuals' equity investments. We study over 40,000 accounts and 950,000 trades from a large discount broker. Individuals invest heavily in local stocks and put 14% more into these stocks than a market-neutral portfolio would suggest. Using holdings-based calendar-time portfolios, we find the local holdings do not generate positive alphas. Using the transactions data, we find local stocks bought actually underperform local stocks sold (though the underperformance is more severe when considering remote stocks.) We find no support for the folk wisdom that one should "invest in what you know."
We study the accuracy of a wide variety of estimators of asset price variation constructed from high-frequency data (so-called “realized measures”), and compare them with a simple “realized variance” (RV) estimator. In total, we consider almost 400 different estimators, applied to 11 years of data on 31 different financial assets spanning five asset classes, including equities, equity indices, exchange rates and interest rates. We apply data-based ranking methods to the realized measures and to forecasts based on these measures. When 5-minute RV is taken as the benchmark realized measure, we find little evidence that it is outperformed by any of the other measures. When using inference methods that do not require specifying a benchmark, we find some evidence that more sophisticated realized measures significantly outperform 5-minute RV. In forecasting applications, we find that a low frequency “truncated” RV outperforms most other realized measures. Overall, we conclude that it is difficult to significantly beat 5-minute RV.
We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-minute) data, and in the length of the past history included in the forecasts. The MIDAS framework allows us to compare models across all these dimensions in a very tightly parameterized fashion. Using equity return data, we find that daily realized power (involving 5-minute absolute returns) is the best predictor of future volatility (measured by increments in quadratic variation) and outperforms model based on realized volatility (i.e. past increments in quadratic variation). Surprisingly, the direct use of high-frequency (5-minute) data does not improve volatility predictions. Finally, daily lags of one to two months are sucient to capture the persistence in volatility. These findings hold both in- and out-of-sample.
Using data on the investments a large number of individual investors made through a discount broker from 1991 to 1996, we find that households exhibit a strong preference for local investments. We test whether this locality bias stems from information or from simple familiarity. The average household generates an additional annualized return of 3.2% from its local holdings relative to its nonlocal holdings, suggesting that local investors can exploit local knowledge. Excess returns to investing locally are even larger among stocks not in the S&P 500 index (firms for which information asymmetries between local and nonlocal investors may be largest).
We consider a Brownian semimartingale X (the sum of a stochastic integral w.r.t. a Brownian motion and an integral w.r.t. Lebesgue measure), and for each n an increasing sequence T(n, i) of stopping times and a sequence of positive FT(n,i)-measurable variables Δ(n,i) such that S(n,i):=T(n,i)+Δ(n,i)≤ (n,i+1). We are interested in the limiting behavior of processes of the form U tⁿ (g)=√δnΣi:S(n,i)≤ t[g(T(n,i),ξiⁿ-αiⁿ(g)], where δn is a normalizing sequence tending to 0 and ξiⁿ=δ(n,i)-1/2(Xs(n,i)-XT(n,i) and αiⁿ are suitable centering terms and g is some predictable function of (ω,t,x). Under rather weak assumptions on the sequences T(n, i) as n goes to infinity, we prove that these processes converge (stably) in law to the stochastic integral of g w.r.t. a random measure B which is, conditionally on the path of X, a Gaussian random measure. We give some applications to rates of convergence in discrete approximations for the p-variation processes and local times. © The Author, 2017. Published by Oxford University Press. All rights reserved.
We suggest in this article a similarity‐based approach to time‐varying coefficient non‐stationary autoregression. In a given sample, the model can display characteristics consistent with stationary, unit root and explosive behaviour, depending on the similarity between the dependent variable and its past values. We establish consistency of the quasi‐maximum likelihood estimator of the model, with a general norming factor. Asymptotic score‐based hypothesis tests are derived. The model is applied to a data set comprised of dual stocks traded in NASDAQ and the Tokyo Stock Exchange.
A rapidly growing literature has documented,important improvements,in financial return volatility measurement,and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling,procedures. Building on recent theoretical results in Barndorff-Nielsen and Shephard (2004a, 2005) for related bi-power variation measures, the present paper provides a practical and robust framework,for non-parametrically measuring the jump component,in asset return volatility. In an application to the DM/$ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from non-jump movements,in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the non-jump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components,of the total return variation process. Keywords: Continuous-time methods; jumps; quadratic variation; realized volatility; bi-power variation; high-
The link between asset valuations and investor sentiment is the subject of con-siderable debate in the profession. We address this question by examining how survey data on investor sentiment relates to i) long-horizon returns, and ii) asset valuations. If excessive optimism drives prices above intrinsic values, periods of high sentiment should be followed by low returns as market prices revert to fundamental values. We find this to be the case for the overall stock market at horizons of two to three years. The relation is strongest for large-capitalization, low book-to-market (growth) portfolios. We also examine the relation between sentiment levels and deviations from intrinsic value. Using errors from an inde-pendent pricing model, we find sentiment is positively related to valuation errors using a variety of tests. All of our results are robust to the inclusion of other factors that have been shown to forecast stock returns, including past returns., seminar participants at the Federal Reserve Board, the University of North Car-olina, Virginia Tech, the 1999 Western Finance Association meeting (especially the discussant, Bhaskaran Swaminathan), the 1999 Financial Management Association meeting, and the 2000 Batten Young Scholars Conference at William & Mary for their comments and suggestions. We also thank Gurdip Bakshi, Zhiwu Chen, Ken French, and Steve Sharpe for providing data.
We examine the predictive effect of sentiment on the cross-section of stock returns across different economic states. The degree of mispricing and the subsequent price correction can be different between economic expansion and recession because of the limits of arbitrage and short sale constraints. The predictive ability of sentiment is asymmetric between different states of the economy. We implement a multivariate Markov-switching model to characterize the economic states. Conditional on the identified economic states, we use the lagged sentiment proxy to forecast the portfolio returns related to small stocks, non-earning stocks, growth stocks, and non-dividend-paying stocks. We find that only in the expansion state does sentiment performs both in-sample and out-of-sample predictive power on these categories of stocks. When an expansion state has high sentiment, these categories of stocks earn relatively low subsequent returns. The predictive ability of sentiment can not be attributed to time-variation in the market beta driven by investor sentiment.
A large number of parameterizations have been proposed to model conditional variance dynamics in a multivariate framework. However, little is known about the ranking of multivariate volatility models in terms of their forecasting ability. The ranking of multivariate volatility models is inherently problematic because it requires the use of a proxy for the unobservable volatility matrix and this substitution may severely affect the ranking. We address this issue by investigating the properties of the ranking with respect to alternative statistical loss functions used to evaluate model performances. We provide conditions on the functional form of the loss function that ensure the proxy-based ranking to be consistent for the true one - i.e., the ranking that would be obtained if the true variance matrix was observable. We identify a large set of loss functions that yield a consistent ranking. In a simulation study, we sample data from a continuous time multivariate diffusion process and compare the ordering delivered by both consistent and inconsistent loss functions. We further discuss the sensitivity of the ranking to the quality of the proxy and the degree of similarity between models. An application to three foreign exchange rates, where we compare the forecasting performance of 16 multivariate GARCH specifications, is provided.
Using US stock portfolios that are formed on book-to-market equity (B/M), long term reversals, momentum, and size, a long sample period (1965–2007), and the comprehensive sentiment index of Baker and Wurgler (20062.
Baker , M and
Wurgler , J . 2006. Investor sentiment and the cross-section of stock returns. Journal of Finance, 61: 1645–80. [CrossRef], [Web of Science ®]View all references), this article shows that contemporaneous returns of extreme portfolios are significantly related to monthly sentiment changes and tend to be higher during periods of negative sentiment. Stock returns, however, seem to Granger-cause sentiment changes and are more important in predicting sentiment changes than vice versa. In addition, conditional return volatility is significantly affected by lagged volatility rather than sentiment changes.
This paper investigates a unique dataset that enables us to determine the aggregate buy and sell volume of individual investors for a large cross-section of NYSE stocks. We find that individuals trade as if they are contrarians, and that the stocks that individuals buy exhibit positive excess returns in the following month. These patterns are consistent with the idea that risk-averse individuals provide liquidity to meet institutional demand for immediacy. We further examine the relation between individual investor sentiment and short-horizon (weekly) return reversals that have been documented in the literature. Our results reveal that individual investor sentiment predicts future returns, and that the information content of investor sentiment is distinct from that of past returns or past volume. Furthermore, the trading of individuals predicts weekly returns in the post-2000 era for stocks of all sizes, while past return seems to have lost its predictive power for all but small stocks over the same time period. Lastly, we note that there is very little cross-sectional correlation of our individual sentiment measure across the stocks in our sample.
This paper studies the aggregation of investor expectations of stock market return variation and its implications. We motivate theoretically that the market's expected return variance can be decomposed into the average of individuals' expected variance plus the dispersion in individuals' expected mean returns. The former can be seen as risk, while the latter is a measure of uncertainty. We illustrate this result empirically by setting up a unique survey measuring investors' expected returns and volatilities. Our finding is important to the issue of aggregating heterogeneous beliefs at the micro level in relation to pricing in financial markets. For instance, as a result it is almost per definition that individual investors are overconfident in the sense of overly narrow forecast bounds, due to neglecting individual differences of opinion about mean returns. We furthermore show that investors display a risk-return trade-off, whereas the market seems to price uncertainty.
ABSTRACTI provide evidence that geographically proximate analysts are more accurate than other analysts. Stock returns immediately surrounding forecast revisions suggest that local analysts impact prices more than other analysts. These effects are strongest for firms located in small cities and remote areas. Collectively these results suggest that geographically proximate analysts possess an information advantage over other analysts, and that this advantage translates into better performance. The well-documented underwriter affiliation bias in stock recommendations is concentrated among distant affiliated analysts; recommendations by local affiliated analysts are unbiased. This finding reveals a geographic component to the agency problems in the industry.
The article develops a dynamic model that nests the rational expectations (RE) and differences of opinion (DO) approaches
to study how investors use prices to update their valuations. When investors condition on prices (RE), investor disagreement
is related positively to expected returns, return volatility, and market beta, but negatively to return autocorrelation. When
investors do not use prices (DO), these relations are reversed. Tests of these predictions on the cross-section of stocks
using analyst forecast dispersion and volume as proxies for disagreement provide empirical evidence that is consistent with
investors using prices on average.
This paper presents a generalized pre-averaging approach for estimating the integrated volatility, in the presence of noise. This approach also provides consistent estimators of other powers of volatility -- in particular, it gives feasible ways to consistently estimate the asymptotic variance of the estimator of the integrated volatility. We show that our approach, which possesses an intuitive transparency, can generate rate optimal estimators (with convergence rate n-1/4).
Existing empirical evidence is inconclusive as to whether professional investors show more sophisticated behavior than individual investors. Therefore, we study two important groups of professional investors and compare them with laymen by means of a survey covering about 500 investors. We find that some professionals, i.e. institutional investors, behave in a more sophisticated manner than laymen, whereas the less researched investment advisors seem to do even worse. Our survey approach complements available evidence due to its design: it compares professionals with (qualified) interested laymen, it covers six measures of sophisticated behavior, uses several control variables and strictly compares investment decisions in the private domain. Copyright 2010 The Authors. German Economic Review 2010 Verein für Socialpolitik.
This paper introduces the model confidence set (MCS) and applies it to the selection of models. An MCS is a set of models that is constructed so that it will contain the best model with a given level of confidence. The MCS is in this sense analogous to a confidence interval for a parameter. The MCS acknowledges the limitations of the data; uninformative data yield an MCS with many models whereas informative data yield an MCS with only a few models. The MCS procedure does not assume that a particular model is the true model; in fact, the MCS procedure can be used to compare more general objects, beyond the comparison of models. We apply the MCS procedure to two empirical problems. First, we revisit the inflation forecasting problem posed by Stock and Watson (1999) and compute the MCS for their set of inflation forecasts. Second, we compare a number of Taylor rule regressions and determine the MCS of the best in terms of in-sample likelihood criteria.
The diversity of agents in a heterogeneous market makes volatilities of different time resolutions behave differently. A lagged correlation study reveals that statistical volatility defined over a coarse time grid significantly predicts volatility defined over a fine grid. This empirical fact is not explained by conventional theories and models. We propose a new model class that takes into account squared price changes from time intervals of different size. This model is shown to reproduce the same empirical properties that have been found for FX intra-day data: long memory, fat-tailed distribution, and predictability of finely defined volatility by coarsely defined volatility.
This paper studies fractional processes that may be perturbed by weakly dependent time series. The model for a perturbed fractional process has a components framework in which there may be components of both long and short memory. All commonly used estimates of the long memory parameter (such as log periodogram (LP) regression) may be used in a components model where the data are affected by weakly dependent perturbations, but these estimates can suffer from serious downward bias. To circumvent this problem, the present paper proposes a new procedure that allows for the possible presence of additive perturbations in the data. The new estimator resembles the LP regression estimator but involves an additional (nonlinear) term in the regression that takes account of possible perturbation effects in the data. Under some smoothness assumptions at the origin, the bias of the new estimator is shown to disappear at a faster rate than that of the LP estimator, while its asymptotic variance is inflated only by a multiplicative constant. In consequence, the optimal rate of convergence to zero of the asymptotic MSE of the new estimator is faster than that of the LP estimator. Some simulation results demonstrate the viability and the bias-reducing feature of the new estimator relative to the LP estimator in finite samples. A test for the presence of perturbations in the data is given.
We examine the informational role of geographically proximate institutions in stock markets. We find that both the level of and change in local institutional ownership predict future stock returns, particularly for firms with high information asymmetry; in contrast, such predictive abilities are relatively weak for nonlocal institutional ownership. The local advantage is especially evident for local investment advisors, high local ownership institutions, and high local turnover institutions. We also find that the stocks that local institutional investors hold (trade) earn higher excess returns around future earnings announcements than those that nonlocal institutional investors hold (trade).
Recent advances in financial econometrics have led to the development of new estimators of asset price variability using frequently-sampled price data, known as “realised volatility estimators” or simply “realised measures”. These estimators rely on a variety of different assumptions and take many different functional forms. Motivated by the empirical success of combination forecasts, this paper presents a novel approach for combining individual realised measures to form new estimators of price variability. In an application to high frequency IBM price data over the period 1996–2008, we consider 32 different realised measures from 8 distinct classes of estimators. We find that a simple equally-weighted average of these estimators cannot generally be out-performed, in terms of accuracy, by any individual estimator. Moreover, we find that none of the individual estimators encompasses the information in all other estimators, providing further support for the use of combination realised measures.
Previous papers that test whether sentiment is useful for predicting volatility ignore whether lagged returns information might also be useful for this purpose. By doing so, these papers potentially overestimate the role of sentiment in predicting volatility. In this paper we test whether sentiment is useful for volatility forecasting purposes. We find that most of our sentiment measures are caused by returns and volatility rather than vice versa. In addition, we find that lagged returns cause volatility. All sentiment variables have extremely limited forecasting power once returns are included as a forecasting variable.
The use of a conditionally unbiased, but imperfect, volatility proxy can lead to undesirable outcomes in standard methods for comparing conditional variance forecasts. We motivate our study with analytical results on the distortions caused by some widely used loss functions, when used with standard volatility proxies such as squared returns, the intra-daily range or realised volatility. We then derive necessary and sufficient conditions on the functional form of the loss function for the ranking of competing volatility forecasts to be robust to the presence of noise in the volatility proxy, and derive some useful special cases of this class of “robust” loss functions. The methods are illustrated with an application to the volatility of returns on IBM over the period 1993 to 2003.
Using a new data set on investor sentiment, we show that institutional and individual sentiment seem to proxy for smart money and noise trader risk, respectively. First, using bias-adjusted long-horizon regressions, we show that institutional sentiment forecasts stock market returns at intermediate horizons correctly, whereas individuals consistently get the direction wrong. Second, even the simplest possible trading strategies based on investor sentiment show clear tendencies toward being profitable after controlling for systematic risk. Finally, IV regressions show that institutional investors take into account expected individual sentiment when forming their expectations, in a way that is consistent with the view that individual investors can be a proxy for noise trader risk. However, there is evidence of structural change.
This paper examines whether analysts resident in a country make more precise earnings forecasts for firms in that country than non-resident analysts. Using a sample of 32 countries, we find an economically and statistically significant local analyst advantage even after controlling for firm and analyst characteristics. The local advantage is high in countries where earnings are smoothed more, less information is disclosed by firms, and firm idiosyncratic information explains a smaller fraction of stock returns. It is negatively related to whether a firm has foreign assets and to market participation by foreign investors and by institutions, and positively related to holdings by insiders. The extent to which U.S. investors underweight a country's stocks is positively related to that country's local analyst advantage.
This study shows the influence of investor sentiment on the market's mean–variance tradeoff. We find that the stock market's expected excess return is positively related to the market's conditional variance in low-sentiment periods but unrelated to variance in high-sentiment periods. These findings are consistent with sentiment traders who, during the high-sentiment periods, undermine an otherwise positive mean–variance tradeoff. We also find that the negative correlation between returns and contemporaneous volatility innovations is much stronger in the low-sentiment periods. The latter result is consistent with the stronger positive ex ante relation during such periods.
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.