This research presents evidence for the existence of differences in asset beta risk in the liquidity cross-section of assets due to correlated trading. It is argued that due to differences in liquidity or cost, most trading activity is concentrated on the subset of liquid assets. In the presence of systematic wealth shocks this leads to an increase in beta risk for the liquid asset class beyond their true level of risk from the underlying dividend process with regard to the market risk factor. Vice-versa, the risk of illiquid assets becomes understated. Moreover it is argued that a reduction of trading cost in the cross-section will reduce such differences and lead to a convergence of risk factor estimates towards the true value of underlying risk. Empirical evidence using data surrounding the tick-reduction event at the New York Stock Exchange is supporting this conjecture. It is found that beta estimates for liquid assets exceed their illiquid peers, while the difference in beta between the groups is significantly reduced after the exogenous trading cost reduction due to the tick-change event.
{"title":"Betas and Liquidity: Differences in Systematic Price Risk Due to Asymmetric Asset Liquidity and Correlated Funding Shocks","authors":"Roland Umlauft","doi":"10.2139/ssrn.2134945","DOIUrl":"https://doi.org/10.2139/ssrn.2134945","url":null,"abstract":"This research presents evidence for the existence of differences in asset beta risk in the liquidity cross-section of assets due to correlated trading. It is argued that due to differences in liquidity or cost, most trading activity is concentrated on the subset of liquid assets. In the presence of systematic wealth shocks this leads to an increase in beta risk for the liquid asset class beyond their true level of risk from the underlying dividend process with regard to the market risk factor. Vice-versa, the risk of illiquid assets becomes understated. Moreover it is argued that a reduction of trading cost in the cross-section will reduce such differences and lead to a convergence of risk factor estimates towards the true value of underlying risk. Empirical evidence using data surrounding the tick-reduction event at the New York Stock Exchange is supporting this conjecture. It is found that beta estimates for liquid assets exceed their illiquid peers, while the difference in beta between the groups is significantly reduced after the exogenous trading cost reduction due to the tick-change event.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128684104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I investigate the economic importance of correlation in mutual fund flows for funds with overlapping portfolio positions. I illustrate theoretically that systematically correlated trading patterns between funds have a negative impact on asset prices and should influence portfolio choice. Theoretically, I show that the expected return from an asset is conditional on the contemporaneous trading pattern of the asset holder, once trading needs are not i.i.d. Finally, I derive a theoretical upper bound of optimal flow correlation and make the conjecture that an optimal equilibrium portfolio outcome exists for any combination of pairwise fund flow correlations. Empirically, I construct a measure of portfolio adjusted flow correlation and find that co-movement in flows can significantly deteriorate fund performance in the long-run, by about 1.4% annually, measured adjusted for style between peer funds with high and low correlation. Finally, I find that around one third of US mutual funds holds non-optimal portfolios as far as dynamic liquidity from correlated trading patterns is concerned.
{"title":"The Cost of Funding Flow Correlation","authors":"Roland Umlauft","doi":"10.2139/ssrn.2138957","DOIUrl":"https://doi.org/10.2139/ssrn.2138957","url":null,"abstract":"I investigate the economic importance of correlation in mutual fund flows for funds with overlapping portfolio positions. I illustrate theoretically that systematically correlated trading patterns between funds have a negative impact on asset prices and should influence portfolio choice. Theoretically, I show that the expected return from an asset is conditional on the contemporaneous trading pattern of the asset holder, once trading needs are not i.i.d. Finally, I derive a theoretical upper bound of optimal flow correlation and make the conjecture that an optimal equilibrium portfolio outcome exists for any combination of pairwise fund flow correlations. Empirically, I construct a measure of portfolio adjusted flow correlation and find that co-movement in flows can significantly deteriorate fund performance in the long-run, by about 1.4% annually, measured adjusted for style between peer funds with high and low correlation. Finally, I find that around one third of US mutual funds holds non-optimal portfolios as far as dynamic liquidity from correlated trading patterns is concerned.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129120863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines the motivations for seasoned equity offering and the decomposition strategy that breaks the book-to-market ratio into misvaluation and growth components. In logit-based tests, we find strong support for the misvaluation explanation, which predict that firms issue when equities are overvalued. However, the growth component runs counter to conventional wisdom as a proxy for investment opportunities and obscures a more complicated relationship between the accounting for operating and financing activities (leverage). Given a low book-to-value ratio, two groups of firms are both likely to conduct an SEO: one with low operating growth and positive leverage, whereas another group with high operating growth and negative leverage. Apart from market timing, the former is also motivated from a demand for liquidity whereas the latter is consistent with an investment-based explanation. Finally, we document evidence that issuers with low growth opportunities and/or high overvaluation are more likely to issue combined or pure secondary shares rather than primary shares.
{"title":"New Evidence on the Timing, Investment and Liquidity Motivations for Public Equity Offers","authors":"David T. L. Siu, R. Faff","doi":"10.2139/ssrn.2028693","DOIUrl":"https://doi.org/10.2139/ssrn.2028693","url":null,"abstract":"This study examines the motivations for seasoned equity offering and the decomposition strategy that breaks the book-to-market ratio into misvaluation and growth components. In logit-based tests, we find strong support for the misvaluation explanation, which predict that firms issue when equities are overvalued. However, the growth component runs counter to conventional wisdom as a proxy for investment opportunities and obscures a more complicated relationship between the accounting for operating and financing activities (leverage). Given a low book-to-value ratio, two groups of firms are both likely to conduct an SEO: one with low operating growth and positive leverage, whereas another group with high operating growth and negative leverage. Apart from market timing, the former is also motivated from a demand for liquidity whereas the latter is consistent with an investment-based explanation. Finally, we document evidence that issuers with low growth opportunities and/or high overvaluation are more likely to issue combined or pure secondary shares rather than primary shares.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114978735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Out-of-sample performance of continuous time models for equity returns is crucial in practical applications such as computing risk measures like value at risk, determine optimal portfolios or pricing derivatives. For all these applications investors need to model the return distribution of an underlying at some point in time in the future given current information. In this paper we analyze the out-of-sample performance of exponentially affine and non-affine continuous time stochastic volatility models with jumps in returns and volatility. Our analysis evaluates the density forecasts implied by the models. In a first step, we find in general that the good in-sample fits reported in the related literature do not carry over to the out-of-sample performance. In particular the left tail of the distribution poses a considerable challenge to the proposed models. In a second step, we analyze the models by using a rolling window approach. We find that using estimation periods that include high market stress events improve forecasting power considerably. In a third step, we apply parameters estimated on the sub period including the financial crisis (period with highest market stress) to all other forecasting sub periods. This approach further increases overall forecasting power and results in an outperformance of affine compared to non-affine models and an outperformance of jump models.
{"title":"Out-of-Sample Performance of Jump-Diffusion Models for Equity Indices: What the Financial Crisis Was Good For","authors":"R. Frey, Paulo Rodrigues, Norman J. Seeger","doi":"10.2139/ssrn.2022909","DOIUrl":"https://doi.org/10.2139/ssrn.2022909","url":null,"abstract":"Out-of-sample performance of continuous time models for equity returns is crucial in practical applications such as computing risk measures like value at risk, determine optimal portfolios or pricing derivatives. For all these applications investors need to model the return distribution of an underlying at some point in time in the future given current information. In this paper we analyze the out-of-sample performance of exponentially affine and non-affine continuous time stochastic volatility models with jumps in returns and volatility. Our analysis evaluates the density forecasts implied by the models. In a first step, we find in general that the good in-sample fits reported in the related literature do not carry over to the out-of-sample performance. In particular the left tail of the distribution poses a considerable challenge to the proposed models. In a second step, we analyze the models by using a rolling window approach. We find that using estimation periods that include high market stress events improve forecasting power considerably. In a third step, we apply parameters estimated on the sub period including the financial crisis (period with highest market stress) to all other forecasting sub periods. This approach further increases overall forecasting power and results in an outperformance of affine compared to non-affine models and an outperformance of jump models.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124199241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Erik Haugom, Steinar Veka, Gudbrand Lien, Sjur Westgaard
In this article we examine the properties of estimates of realized volatility at various intra-daily sampling frequencies for Brent Crude oil futures traded at the IntercontinentalExchange (ICE). The estimates of realized volatility are subsequently modeled and forecasted to predict day-ahead Value-at-Risk. We suggest a new method for evaluating the whole distribution of the variance forecasts by examining a simple PP-plot. Our results show that the distribution of ICE Brent Crude oil futures returns standardized with predicted volatility for the next trading day is very close to Gaussian, which significantly simplifies the Value-at-Risk estimation. Finally, our results suggest that the ideal choice of sampling frequency is between one and ten minutes for this commodity.
{"title":"Estimating and Evaluating Value-at-Risk forecasts based on Realized Variance: Empirical Evidence from ICE Brent Crude Oil Futures","authors":"Erik Haugom, Steinar Veka, Gudbrand Lien, Sjur Westgaard","doi":"10.2139/ssrn.2138734","DOIUrl":"https://doi.org/10.2139/ssrn.2138734","url":null,"abstract":"In this article we examine the properties of estimates of realized volatility at various intra-daily sampling frequencies for Brent Crude oil futures traded at the IntercontinentalExchange (ICE). The estimates of realized volatility are subsequently modeled and forecasted to predict day-ahead Value-at-Risk. We suggest a new method for evaluating the whole distribution of the variance forecasts by examining a simple PP-plot. Our results show that the distribution of ICE Brent Crude oil futures returns standardized with predicted volatility for the next trading day is very close to Gaussian, which significantly simplifies the Value-at-Risk estimation. Finally, our results suggest that the ideal choice of sampling frequency is between one and ten minutes for this commodity.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127281058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An approach to approximate the multivariate distribution of time-aggregated stock returns in the GARCH context is developed here. The approach yields a one time-step simulation procedure as opposed to a multiple time-step simulation required in such a context. For this purpose, the exact moment formulas for the time-aggregated return under a QGARCH process are combined with multivariate non-normal simulation procedures using as inputs, the first four moments and correlation structure of the unknown target distribution. Estimation and simulation results are presented for a portfolio of 30 stocks from the Dow Jones Industrial Average index. The results reveal that the proposed simulation method can generate random numbers with moments and correlations agreeing with the targets. Using value at risk computations for different horizons and probabilities, we show that the percentiles of portfolios return distributions computed with the proposed approach provide good approximations of benchmark values obtained from a multi-step simulation.
{"title":"Approximating the Multivariate Distribution of Time-Aggregated Stock Returns Under GARCH","authors":"Jean-Guy Simonato","doi":"10.2139/ssrn.2068994","DOIUrl":"https://doi.org/10.2139/ssrn.2068994","url":null,"abstract":"An approach to approximate the multivariate distribution of time-aggregated stock returns in the GARCH context is developed here. The approach yields a one time-step simulation procedure as opposed to a multiple time-step simulation required in such a context. For this purpose, the exact moment formulas for the time-aggregated return under a QGARCH process are combined with multivariate non-normal simulation procedures using as inputs, the first four moments and correlation structure of the unknown target distribution. Estimation and simulation results are presented for a portfolio of 30 stocks from the Dow Jones Industrial Average index. The results reveal that the proposed simulation method can generate random numbers with moments and correlations agreeing with the targets. Using value at risk computations for different horizons and probabilities, we show that the percentiles of portfolios return distributions computed with the proposed approach provide good approximations of benchmark values obtained from a multi-step simulation.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126479920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I present evidence that a moving average (MA) trading strategy dominates buying and holding the underlying asset in a mean-variance sense using monthly returns of value-weighted and equal-weighted US REIT indexes over the period January 1980 until December 2010. The abnormal returns are largely insensitive to the four Carhart factors and produce economically and statistically significant alphas of between 10 and 15% per year after transaction costs. This performance is robust to different lags of the MA and in subperiods while investor sentiment, liquidity risks, business cycles, up and down markets, and the default spread cannot fully account for its performance. The MA strategy works just as well with randomly generated returns and bootstrapped returns. The substantial market timing ability of the MA strategy appears to be the main driver of the abnormal returns. The returns to the MA strategy resemble the returns of an imperfect at-the-money protective put strategy relative to the underlying portfolio. The lagged signal to switch has substantial predictive power over the subsequent return of the REIT index. The MA strategy avoids the sharp downturn at the beginning of 2008 and substantially outperforms the cumulative returns of the buy-and-hold strategy using all of the 20 REIT indexes. The results from applying the MA strategy with 274 individual REITs largely corroborate the findings for the REIT indexes.
{"title":"The Market Timing Power of Moving Averages: Evidence from US REITs and REIT Indexes","authors":"Paskalis Glabadanidis","doi":"10.2139/ssrn.2055017","DOIUrl":"https://doi.org/10.2139/ssrn.2055017","url":null,"abstract":"I present evidence that a moving average (MA) trading strategy dominates buying and holding the underlying asset in a mean-variance sense using monthly returns of value-weighted and equal-weighted US REIT indexes over the period January 1980 until December 2010. The abnormal returns are largely insensitive to the four Carhart factors and produce economically and statistically significant alphas of between 10 and 15% per year after transaction costs. This performance is robust to different lags of the MA and in subperiods while investor sentiment, liquidity risks, business cycles, up and down markets, and the default spread cannot fully account for its performance. The MA strategy works just as well with randomly generated returns and bootstrapped returns. The substantial market timing ability of the MA strategy appears to be the main driver of the abnormal returns. The returns to the MA strategy resemble the returns of an imperfect at-the-money protective put strategy relative to the underlying portfolio. The lagged signal to switch has substantial predictive power over the subsequent return of the REIT index. The MA strategy avoids the sharp downturn at the beginning of 2008 and substantially outperforms the cumulative returns of the buy-and-hold strategy using all of the 20 REIT indexes. The results from applying the MA strategy with 274 individual REITs largely corroborate the findings for the REIT indexes.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129198541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we solve the problem of solution of stochastic volatility models in which the volatility diffusion can be solved by a one dimensional Fokker-planck equation. We use one dimensional transition probabilities for the evolution of PDE of variance. We also find dynamics of evolution of expected value of any path dependent function of stochastic volatility variable along the PDE grid. Using this technique, we find the conditional expected values of moments of log of terminal asset price along every node of one dimensional forward Kolmogorov PDE. We use the conditional distribution of moments of above path integrals along the variance grid and use Edgeworth expansions to calculate the density of log of asset price. Main result of the paper gives dynamics of evolution of conditional expected value of a path dependent function of volatility (or any other SDE) at any node on the PDE grid using just one dimensional PDE if we can describe its one step conditional evolution between different nodes of the PDE.
{"title":"Solution of Stochastic Volatility Models Using Variance Transition Probabilities and Path Integrals","authors":"A. Amin","doi":"10.2139/ssrn.2149231","DOIUrl":"https://doi.org/10.2139/ssrn.2149231","url":null,"abstract":"In this paper, we solve the problem of solution of stochastic volatility models in which the volatility diffusion can be solved by a one dimensional Fokker-planck equation. We use one dimensional transition probabilities for the evolution of PDE of variance. We also find dynamics of evolution of expected value of any path dependent function of stochastic volatility variable along the PDE grid. Using this technique, we find the conditional expected values of moments of log of terminal asset price along every node of one dimensional forward Kolmogorov PDE. We use the conditional distribution of moments of above path integrals along the variance grid and use Edgeworth expansions to calculate the density of log of asset price. Main result of the paper gives dynamics of evolution of conditional expected value of a path dependent function of volatility (or any other SDE) at any node on the PDE grid using just one dimensional PDE if we can describe its one step conditional evolution between different nodes of the PDE.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130986167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The divergence of opinion model originally proposed by Miller (1977) has recently received a great deal of attention. Focusing on the unique offering process of Japanese seasoned equity offerings (SEOs), we are able to directly test the Miller model. A comparable analysis cannot be performed on U.S. SEOs. We find our proxy for divergence of opinion is negatively related to stock returns on both the announcement day and the issue day. This implies that the demand curve for the issuing firm’s common stock steepens as the dispersion of opinion for a stock widens. We find that issue size is also related to stock returns on both dates. The relation is stronger for stocks with higher dispersions of opinion. This finding is consistent with Miller’s prediction. We also show that short sales constraints cause market underreaction. Further, we show that manipulative short selling is concentrated around the price determination day. However, our results regarding opinion divergence are free from the manipulative short selling effect.
{"title":"Does Divergence of Opinion Affect Stock Returns? Evidence from Japanese SEOs","authors":"H. Kato, Katsushi Suzuki","doi":"10.2139/ssrn.2023490","DOIUrl":"https://doi.org/10.2139/ssrn.2023490","url":null,"abstract":"The divergence of opinion model originally proposed by Miller (1977) has recently received a great deal of attention. Focusing on the unique offering process of Japanese seasoned equity offerings (SEOs), we are able to directly test the Miller model. A comparable analysis cannot be performed on U.S. SEOs. We find our proxy for divergence of opinion is negatively related to stock returns on both the announcement day and the issue day. This implies that the demand curve for the issuing firm’s common stock steepens as the dispersion of opinion for a stock widens. We find that issue size is also related to stock returns on both dates. The relation is stronger for stocks with higher dispersions of opinion. This finding is consistent with Miller’s prediction. We also show that short sales constraints cause market underreaction. Further, we show that manipulative short selling is concentrated around the price determination day. However, our results regarding opinion divergence are free from the manipulative short selling effect.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115243279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper finds that returns to the post-earnings-announcement drift (PEAD) strategy result from differential sensitivity of individual stock returns to aggregate earnings shocks. Larger negative aggregate earnings shocks are associated with higher PEAD returns, because stocks in the PEAD’s sell portfolio are more sensitive to aggregate earnings shocks than those in the buy portfolio. Such differential sensitivity to aggregate earnings shocks drives a significant portion of PEAD returns. During the 1985 to 2009 sample period, investors were on average negatively surprised by aggregate earnings shocks, leading to average positive returns to the PEAD strategy. Further analysis suggests that macroeconomic shocks (that work through aggregate earnings shocks) explain the variation in PEAD returns.
{"title":"Does Differential Sensitivity to Aggregate Earnings Shocks Drive Post-Earnings-Announcement Drift?","authors":"Suresh Nallareddy","doi":"10.2139/ssrn.2163237","DOIUrl":"https://doi.org/10.2139/ssrn.2163237","url":null,"abstract":"This paper finds that returns to the post-earnings-announcement drift (PEAD) strategy result from differential sensitivity of individual stock returns to aggregate earnings shocks. Larger negative aggregate earnings shocks are associated with higher PEAD returns, because stocks in the PEAD’s sell portfolio are more sensitive to aggregate earnings shocks than those in the buy portfolio. Such differential sensitivity to aggregate earnings shocks drives a significant portion of PEAD returns. During the 1985 to 2009 sample period, investors were on average negatively surprised by aggregate earnings shocks, leading to average positive returns to the PEAD strategy. Further analysis suggests that macroeconomic shocks (that work through aggregate earnings shocks) explain the variation in PEAD returns.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116432880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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