Disagreement and asset prices

How do differences of opinion affect asset prices? Do investors earn a risk premium when disagreement arises in the market? Despite their fundamental importance, these questions are among the most controversial issues in finance. In this paper, we use a novel data set that allows us to directly measure the level of disagreement among Wall Street mortgage dealers about prepayment speeds. We examine how disagreement evolves over time and study its effects on expected returns, return volatility, and trading volume in the mortgage-backed security market. We find that increased disagreement is associated with higher expected returns, higher return volatility, and larger trading volume. These results imply that there is a positive risk premium for disagreement in asset prices. We also show that volatility in and of itself does not lead to higher trading volume. Instead, only when disagreement arises in the market is higher uncertainty associated with more trading. Finally, we are able to distinguish empirically between two competing hypotheses regarding how information in markets gets incorporated into asset prices. We find that sophisticated investors appear to update their beliefs through a rational expectations mechanism when disagreement arises.     

Order book characteristics and the volume–volatility relation: Empirical evidence from a limit order market

Using unique data, we address the issue of price formation in a limit order market. A standard volume–volatility relation is documented with the number of trades acting as the important component of volume. The main contribution of the paper is to identify strong evidence that volume, volatility, and the volume–volatility relation are negatively related to the order book slope. These results are robust to the inclusion of several liquidity measures. A significant empirical relationship between the order book slope and the coefficient of variation in earnings forecasts by financial analysts suggests that the slope is proxying for disagreement among investors. Hence, our results support models where investor heterogeneity intensifies the volume–volatility relation.     

Disagreement and the informativeness of stock returns: The case of acquisition announcements

We examine whether disagreement between managers and investors, in the context of mergers and acquisitions, affects the information contained in bidder returns. We test the disagreement hypothesis, which posits that disagreement causes investors to be less certain about their revaluation of acquiring firms, making bidder returns less informative. Consistent with this hypothesis, we find an inverse relation between bidder returns, which proxy for the degree of disagreement, and the change in the bidders' implied volatility. Also consistent with the hypothesis, we find that the significant inverse relation between bidder returns and the change in implied volatility holds only for cases of negative bidder returns. We test for alternative explanations of this relation, but continue to find robust support for the disagreement hypothesis. Finally, the relation between bidder returns and the likelihood of deal completion is stronger when announcement returns are more informative, suggesting managers “listen to the market” more when the market response is more informative.     

Expected Idiosyncratic Volatility Measures and Expected Returns

We find that idiosyncratic volatility forecasts using information available to traders at the time of the forecast are not related to expected returns. The positive relation documented in a number of other papers only exists when forward‐looking information is incorporated into the volatility estimate. That positive relation is driven by the realized idiosyncratic volatility component that cannot be forecasted by investors. Our findings are robust to several different empirical tests, volatility forecasting models and time periods.     

基于小波CCC-GARCH模型的融资融券交易与证券市场波动率关系研究

以2010年4月2日～2016年7月21日的中国证券市场交易数据为样本,运用小波CCC-GARCH模型,考量融资融券交易对证券市场波动率的影响.结果表明,融资交易行为对证券市场收益率和成交量的波动均有较显著的影响,而融券交易对市场波动率的影响不显著.同时,融资交易行为对市场的影响主要由高频信号所驱动,投资者短期非理性行为或噪音交易对市场波动的影响较大.为促进市场的健康发展,应均衡融资融券业务的发展,培养理性机构投资者,加强投资者风险警示加快融资融券数据库的建设.     

机构投资者影响我国股价波动的实证研究

采用 TGARCH 模型对机构投资者与我国股指波动的关系进行研究,实证结果表明：无论是否考虑宏观经济因素对股票市场的影响,机构投资者对我国股票市场波动均产生正向影响。进一步用面板数据模型对机构投资者与上市公司股价波动的关系进行研究,发现机构投资者在不同宏观经济环境下也均未起到稳定上市公司股价波动的作用。     

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.     

Disagreement and equilibrium option trading volume

Using a complete market equilibrium model, we present results concerning the effect disagreement has on equilibrium option trading volume and positioning. We find that if agents agree on volatility, total option volume is independent of wealth distribution and average optimism. We also find option volume increasing in drift disagreement and decreasing in risk aversion and volatility. Pessimists are shown to write most options. With volatility disagreement, the results are less clear; however, we show agents with high volatility beliefs write deep out of the money options and buy close to the money options. Numerical comparative statics are also performed. This revised version was published online in June 2006 with corrections to the Cover Date.     

A Theory of Trading Volume

A theory of trading volume is developed based on assumptions that market agents frequently revise their demand prices and randomly encounter potential trading partners. The model describes two distinct ways informational events affect trading volume. One is consistent with conjectures made by empirical researchers that investor disagreement leads to increased trading. But the observation of abnormal trading volume does not necessarily imply disagreement, and volume can increase even if investors interpret the information identically, if they also have had divergent prior expectations. Simulation tests support the model and are used to contrast the random-pairing environment with costless market clearing. Volume is lower in the costly market, and volume increases caused by an informational event persist after the event period. This is consistent with existing empirical evidence and suggests that markets do not immediately clear all orders or that investors have demands to recontract.     

股市波动对基金管理费报酬的影响研究——基于中介效应的分析

利用中介效应分析方法检验了理论假设:股市波动对基金管理费报酬有积极的影响,基金业绩在股市波动对基金管理费报酬的影响中存在正的中介效应,即基金有能力在股市波动中发掘机会来提高业绩,从而提高管理费报酬。实证结果表明,在没有增加基金业绩情况下,股市波动对基金管理费报酬具有积极影响,此结论从股市波动的视角证明了基金管理费激励机制并不是激励相容的。根据实证结果,可以从基金类型多样化、扩大管理费率范围和加强投资者教育等三个方面完善基金管理费激励机制。     

Do institutional investors destabilize stock prices? evidence from an emerging market

In this paper, we provide empirical evidence on the impact of institutional investors on stock market returns dynamics in Poland. The Polish pension system reform in 1999 and the associated increase in institutional ownership due to the investment activities of pension funds are used as a unique institutional characteristic. We find robust empirical evidence that the increase of institutional ownership has changed the autocorrelation and volatility structure of aggregate stock returns. However, the findings do not support the hypothesis that institutional investors have destabilized stock prices. The results are interpretable in favor of a stabilizing effect on index stock returns induced by institutional trading.     

International capital asset pricing: Evidence from options

We study the risk dynamics and pricing in international economies through a joint analysis of the time-series returns and option prices on three equity indexes underlying three economies: the S&P 500 Index of the United States, the FTSE 100 Index of the United Kingdom, and the Nikkei-225 Stock Average of Japan. We develop an international capital asset pricing model, under which the return on each equity index is decomposed into two orthogonal jump-diffusion components: a global component and a country-specific component. We apply separate stochastic time changes to the two components so that stochastic volatility can come from both global and country-specific risks. For each economy, we assign separate market prices for the two return risk components and the two volatility risk components. Under this specification, we obtain tractable option pricing solutions. Model estimation reveals several interesting insights. First, global and country-specific return and volatility risks show different dynamics. Global return movements contain a larger discontinuous component, and global return volatility is more persistent than the country-specific counterparts. Second, investors charge positive prices for global return risk and negative prices for volatility risk, suggesting that investors are willing to pay positive premiums to hedge against downside global return movements and upside volatility movements. Third, the three economies contain different risk profiles and also price risks differently. Japan contains the largest idiosyncratic risk component and smallest global risk component. Investors in the Japanese market also price more heavily against future volatility increases than against future market downfalls.     

Chinese institutional investors’ sentiment

We use daily survey data on Chinese institutional investors’ forecasts to measure investors’ sentiment. Our empirical model uncovers that share prices and investor sentiment do not have a long-run relation; however, in the short-run, the mood of investors follows a positive-feedback process. Hence, institutional investors are optimistic when previous market returns were positive. Contrarily, negative returns trigger a decline in sentiment, which reacts more sensitively to negative than positive returns. Investor sentiment does not predict future market movements—but a drop in confidence increases market volatility and destabilizes exchanges. EGARCH models reveal asymmetric responses in the volatility of investor sentiment; however, Granger causality tests reject volatility-spillovers between returns and sentiment.     

Why Does Return Predictability Concentrate in Bad Times?

We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions polarize. Disagreement thus spikes in bad times, causing returns to react to past news. This phenomenon creates a positive relation between disagreement and future returns. It also generates time‐series momentum, which strengthens in bad times, increases with disagreement, and crashes after sharp market rebounds. We provide empirical support for these new predictions.     

Institutional investors and stock returns volatility: Empirical evidence from a natural experiment

In this paper, we provide empirical evidence on the impact of institutional investors on stock market returns dynamics. The Polish pension system reform in 1999 and the associated increase in institutional ownership due to the investment activities of pension funds are used as a unique institutional characteristic. Performing a Markov-switching-GARCH analysis we find empirical evidence that the increase of institutional ownership has temporarily changed the volatility structure of aggregate stock returns. The results are interpretable in favor of a stabilizing effect on index stock returns induced by institutional investors.     

Crowd wisdom and internet searches: What happens when investors search for stocks?

Search engines and social media have become popular among investors as tools for finding and sharing information. The investor social media gathers a large amount of investor-generated content (IGC), which reflects the crowd wisdom of investors, while search engines help investors increase their chances of finding them. In this study, we integrate investor search behavior data from the Baidu Index and investor crowd wisdom data from Eastmoney Guba to assemble a unique data set at the daily level. We then describe and quantify crowd wisdom from investor-generated content (IGC) using three dimensions (IGC average sentiment, IGC sentiment volatility, and IGC increased volume) to investigate the impact of crowd wisdom in the relationship between investors' Internet searches and next-day stock returns. In our empirical analysis, we find that IGC average sentiment strengthens the relationship between investors' Internet searches and next-day stock returns, while IGC sentiment volatility and IGC increased volume have negative effects. These moderating effects are also moderated by institutional investor attention, search terminal preference, and content reading volume. These findings help to explain the value and impact of crowd wisdom when investors search for stock information through the Internet.     

Crude oil market and stock markets during the COVID-19 pandemic: Evidence from the US,Japan, and Germany

We analyzed the return and volatility spillover between the COVID-19 pandemic in 2020, the crude oil market, and the stock market by employing two empirical methods for connectedness: the time-domain approach developed by Diebold and Yilmaz (2012) and the method based on frequency dynamics developed by Barunik and Krehlik (2018). We find that the return spillover mainly occurs in the short term; however, the volatility spillover mainly occurs in the long term. From the moving window analysis results, the impact of COVID-19 created an unprecedented level of risk, such as plummeting oil prices and triggering the US stock market circuit breaker four times, which caused investors to suffer heavy losses in a short period. Furthermore, the impact of COVID-19 on the volatility of the oil and stock markets exceeds that caused by the 2008 global financial crisis, and continues to have an effect. The impact of the COVID-19 pandemic on financial markets is uncertain in both the short and long terms. Our research provides some urgent and prominent insights to help investors and policymakers avoid the risks in the crude oil and stock markets because of the COVID-19 pandemic and reestablish economic development policy strategies.     

Belief Dispersion in the Stock Market

We develop a dynamic model of belief dispersion with a continuum of investors differing in beliefs. The model is tractable and qualitatively matches many of the empirical regularities in a stock price and its mean return, volatility, and trading volume. We find that the stock price is convex in cash‐flow news and increases in belief dispersion, while its mean return decreases when the view on the stock is optimistic, and vice versa when pessimistic. Moreover, belief dispersion leads to higher stock volatility and trading volume. We demonstrate that otherwise identical two‐investor heterogeneous‐beliefs economies do not necessarily generate our main results.     

The Role of Media for Inflation Forecast Disagreement of Households and Professional Forecasters

This paper investigates the effects of media coverage about consumer price inflation on inflation forecast disagreement of German households and professional forecasters. We adopt a Bayesian learning model in which media coverage of inflation affects forecast disagreement by influencing information sets as well as predictor choice. Our empirical results show that disagreement of households depends on the heterogeneity of story content and on the reporting intensity, especially of news on rising inflation. Disagreement of professional forecasters does not depend on media coverage. With respect to the influence of macroeconomic variables, we provide evidence that disagreement of professional forecasters primarily depends on the inflation rate and on inflation volatility. The response of households to inflation is much less pronounced.     

The Size and Incidence of the Losses from Noise Trading

Recent empirical research has identified a significant amount of volatility in stock prices that cannot easily be explained by changes in fundamentals; one interpretation is that asset prices respond not only to news but also to irrational “noise trading.” We assess the welfare effects and incidence of such noice trading using an overlapping-generations model that gives investors short horizons. We find that the additional risk generated by noise trading can reduce the capital stock and consumption of the economy, and we show that part of that cost may be borne by rational investors. We conclude that the welfare costs of noise trading may be large if the magnitude of noise in aggregate stock prices is as large as suggested by some of the recent empirical litrature on the excess volatility of the market.