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Home is Where You Know Your Volatility - Local Investor Sentiment and Stock Market Volatility

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  • Versicherungskammer Bayern
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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.

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... 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. ...
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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. ...
Conference Paper
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– 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.
... 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. ...
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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. ...
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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. ...
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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).
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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.
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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.
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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.
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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.
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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).
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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We study asset pricing in economies featuring both risk and uncertainty. In our empirical analysis, we measure risk via return volatility and uncertainty via the degree of disagreement of professional forecasters, attributing different weights to each forecaster. We empirically model the typical risk-return trade-off and augment these models with our measure of uncertainty. We find stronger empirical evidence for an uncertainty-return trade-off than for the traditional risk-return trade-off. Finally, we investigate the performance of a two-factor model with risk and uncertainty in the cross section.