This study examines whether short-sellers and financial analysts develop complementary information about future earnings and returns and assesses whether investors can improve predictions made by each of these intermediaries using information provided by the other. The first main result is that the relative short interest ratio (shares sold short divided by total shares outstanding) contains information that is useful for predicting future earnings, beyond (i.e., incremental to) the information in analyst forecasts. I also find that analysts do not fully incorporate short interest information into their forecasts and demonstrate that analyst forecasts can be improved (i.e., can be made to be less biased and more accurate) by adjusting for short interest information. The second main result is that analyst forecast revisions contain information that is useful for predicting future abnormal returns, beyond the information in the relative short interest ratio. I demonstrate that portfolios of stocks formed based on consistent signals from short-sellers and analysts produce abnormal return spreads that are significantly larger than spreads produced by portfolios formed using signals from short-sellers alone. Collectively, the evidence suggests that short-sellers and analyst provide complementary information about future firm performance that is useful to investors.

This study examines whether short-sellers and financial analysts develop complementary information about future earnings and returns and assesses whether investors can improve predictions made by each of these intermediaries using information provided by the other. The first main result is that the relative short interest ratio (shares sold short divided by total shares outstanding) contains information that is useful for predicting future earnings, beyond (i.e., incremental to) the information in analyst forecasts. I also find that analysts do not fully incorporate short interest information into their forecasts and demonstrate that analyst forecasts can be improved (i.e., can be made to be less biased and more accurate) by adjusting for short interest information. The second main result is that analyst forecast revisions contain information that is useful for predicting future abnormal returns, beyond the information in the relative short interest ratio. I demonstrate that portfolios of stocks formed based on consistent signals from short-sellers and analysts produce abnormal return spreads that are significantly larger than spreads produced by portfolios formed using signals from short-sellers alone. Collectively, the evidence suggests that short-sellers and analyst provide complementary information about future firm performance that is useful to investors.

The current study investigates the relationship between analyst coverage and the moments of the return distribution. Results are presented to support a time-varying pattern in the premiums associated with the higher moments of returns, particularly for the fourth moment of the distribution. In addition, evidence is presented to suggest that there exists some ex-post and ex-ante forecasting ability based on the use of the higher moments of the return distribution as stock selection criteria. In the second half of the study, results show that as the number of analysts following a firm increases, the third and fourth moments of the return distribution are impacted, with the former being reduced and the latter increased. In addition, the initiation and discontinuation of analyst coverage are both found to be related to the higher moments of the return distribution. The initiation of analyst coverage is associated with a reduction in skewness and an increase in excess kurtosis, while the discontinuation of coverage results in an increase in both of the higher moments of the distribution. Taken together, the results of the two main questions in the current research study suggest that investors seeking higher distributional moments of returns may favor neglected firms over their followed counterparts, particularly in periods of heightened market volatility. In addition, the results show that the two main competing hypotheses concerning the causes of non-normal security returns, namely firm information structure and security liquidity, both impact the higher moments of the return distribution. / Thesis (Ph.D, Management) -- Queen's University, 2010-08-26 12:18:47.528

No description available.

This thesis will be concerned with investigating the empirical characteristics of stock returns, forUKfirms which are distinguished by market value. The primary aimof thisworkis to identify whether there are differences between the behaviour of large and small firm retums. A substantial amount of attention has recently focused upon how firm size influences the behaviour of stock returns in US markets, but, the role that firm size might have in determining the behaviour of stock returns in UK markets has received very little attention. The aim of this thesis is to redress this imbalance. The first part of this study will be concerned with showing that the returns of small firms are more predictable than the returns of large firms. The second part of this study will show that the relationship between risk and return depends on firm size. The third and final part of this thesis will show that not only are the mean returns of large and small firms different but that there are also important differences in the conditional variances of large and small firms. In all three parts of this thesis, important differences between the behaviour of large and small firm returns are documented for the first time.

No description available.

Innovation and technological improvements are essential components in generating growth. While this topic is well studied, limited research exists on the effectiveness of innovation and how it drives firm value. The 2008 Financial Crisis serves as a major historical event that significantly changed the economic environment in the US. Centering my analysis around this event, my study empirically examines the efficiency of innovation investments and industry-level stock returns. By taking patents issued as a percentage of R&D outlays, I measure Efficient Innovation—how effective a firm’s R&D is in generating significant innovative change.

This thesis aims at investigating the puzzling relationship between cor- porate acquisitions and expected stock returns by reviewing numerous studies on this topic through the use of state of the art meta-analysis tools. Such an analysis is required because many papers examined this relationship but their results varied. We therefore collected 421 estimates from 20 papers and led multiple regressions to test for the presence of publication bias. Throughout this analysis we indeed found evidence supporting the existence of publication bias. Furthermore, we decided to apply Bayesian Model Averaging to reduce the model uncertainty and find out why our abnormal returns estimates greatly vary across stud- ies. Our results suggest that one of the most important drivers are the standard-error terms. This subsequently proves that publication bias is the most responsible for the heterogeneity amongst our estimates. Our analysis fails to demonstrate any positive effects from M&A activity on a firm post-acquisition performance. We suggest that other motives are under-represented in the underlying theory that aims to assess M&A outcomes. Keywords Mergers and Acquisitions, Stock Returns, Abnormal Re- turns, Meta-Analysis, Publication bias Author's e-mail thibault.parreau@gmail.com Supervisor's e-mail...

Thesis advisor: Zhijie Xiao / In recent years, quantile regression has achieved increasing prominence as a quantitative method of choice in applied econometric research. The methodology focuses on how the quantile of the dependent variable is influenced by the regressors, thus providing the researcher with much information about variations in the relationship between the covariates. In this dissertation, I consider two quantile regression models where the information set may contain quantiles of the regressors. Such frameworks thus capture the dependence between quantiles - the quantile of the dependent variable and the quantile of the regressors - which I call models of quantile dependence. These models are very useful from the applied researcher's perspective as they are able to further uncover complex dependence behavior and can be easily implemented using statistical packages meant for standard quantile regressions. The first chapter considers an application of the quantile dependence model in empirical finance. One of the most important parameter of interest in risk management is the correlation coefficient between stock returns. Knowing how correlation behaves is especially important in bear markets as correlations become unstable and increase quickly so that the benefits of diversification are diminished especially when they are needed most. In this chapter, I argue that it remains a challenge to estimate variations in correlations. In the literature, either a regime-switching model is used, which can only estimate correlation in a finite number of states, or a model based on extreme-value theory is used, which can only estimate correlation between the tails of the returns series. Interpreting the quantile of the stock return as having information about the state of the financial market, this chapter proposes to model the correlation between quantiles of stock returns. For instance, the correlation between the 10th percentiles of stock returns, say the U.S. and the U.K. returns, reflects correlation when the U.S. and U.K. are in the bearish state. One can also model the correlation between the 60th percentile of one series and the 40th percentile of another, which is not possible using existing tools in the literature. For this purpose, I propose a nonlinear quantile regression where the regressor is a conditional quantile itself, so that the left-hand-side variable is a quantile of one stock return and the regressor is a quantile of the other return. The conditional quantile regressor is an unknown object, hence feasible estimation entails replacing it with the fitted counterpart, which then gives rise to problems in inference. In particular, inference in the presence of generated quantile regressors will be invalid when conventional standard errors are used. However, validity is restored when a correction term is introduced into the regression model. In the empirical section, I investigate the dependence between the quantile of U.S. MSCI returns and the quantile of MSCI returns to eight other countries including Canada and major equity markets in Europe and Asia. Using regression models based on the Gaussian and Student-t copula, I construct correlation surfaces that reflect how the correlations between quantiles of these market returns behave. Generally, the correlations tend to rise gradually when the markets are increasingly bearish, as reflected by the fact that the returns are jointly declining. In addition, correlations tend to rise when markets are increasingly bullish, although the magnitude is smaller than the increase associated with bear markets. The second chapter considers an application of the quantile dependence model in empirical macroeconomics examining the money-output relationship. One area in this line of research focuses on the asymmetric effects of monetary policy on output growth. In particular, letting the negative residuals estimated from a money equation represent contractionary monetary policy shocks and the positive residuals represent expansionary shocks, it has been widely established that output growth declines more following a contractionary shock than it increases following an expansionary shock of the same magnitude. However, correctly identifying episodes of contraction and expansion in this manner presupposes that the true monetary innovation has a zero population mean, which is not verifiable. Therefore, I propose interpreting the quantiles of the monetary shocks as having information about the monetary policy stance. For instance, the 10th percentile shock reflects a restrictive stance relative to the 90th percentile shock, and the ranking of shocks is preserved regardless of shifts in the shock's distribution. This idea motivates modeling output growth as a function of the quantiles of monetary shocks. In addition, I consider modeling the quantile of output growth, which will enable policymakers to ascertain whether certain monetary policy objectives, as indexed by quantiles of monetary shocks, will be more effective in particular economic states, as indexed by quantiles of output growth. Therefore, this calls for a unified framework that models the relationship between the quantile of output growth and the quantile of monetary shocks. This framework employs a power series method to estimate quantile dependence. Monte Carlo experiments demonstrate that regressions based on cubic or quartic expansions are able to estimate the quantile dependence relationships well with reasonable bias properties and root-mean-squared errors. Hence, using the cubic and quartic regression models with M1 or M2 money supply growth as monetary instruments, I show that the right tail of the output growth distribution is generally more sensitive to M1 money supply shocks, while both tails of output growth distribution are more sensitive than the center is to M2 money supply shocks, implying that monetary policy is more effective in periods of very low and very high growth rates. In addition, when non-neutral, the influence of monetary policy on output growth is stronger when it is restrictive than expansive, which is consistent with previous findings on the asymmetric effects of monetary policy on output. / Thesis (PhD) — Boston College, 2009. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

Thesis (M.Sc. (Statistics)) -- University of Limpopo. 2014 / A number of previous research studies have investigated volatility and financial risks in the ermeging markets. This dissertation investigates stock returns volatility and financial risks in the Johannesburg Stock Exchange (JSE). The investigation is con- ducted in modelling volatility using Autoregressive Moving Average-Generalised Au- toregressive Conditional Heteroskedastic (ARMA-GARCH)-type models. Daily data of the log returns at the JSE over the period 08 January, 2002 to 30 December, 2011 is used. The results suggest that daily returns can be characterised by an ARMA (1, 0) process. Empirical results show that ARMA (1, 0)-GARCH (1, 1) model achieves the most accurate volatility forecast. Modelling tail behaviour of rare and extreme events is an important issue in the risk management of a financial portfolio. Extreme Value Theory (EVT) is applied to quantify upper extreme returns. Generalised Ex- treme Value (GEV) distribution is used to model the behaviour of extreme returns. Empirical results show that the Weibull distribution can be used to model stock re- turns on the JSE. In using the Generalised Pareto Distribution (GPD), the modelling framework used accommodates ARMA and GARCH models. The GPD is applied to ARMA-GARCH filtered returns series and the model is referred to as the ARMA- GARCH-GPD model. The threshold value is estimated using Pareto quantile plot while peak-over-threshold approach is used to model the upper extreme returns. In general, the ARMA-GARCH-GPD model produces more accurate estimates of ex- treme returns than the ARMA-GARCH model. The out of sample forecast indicates that the ARMA (1, 3)-GARCH (1, 1) model provides the most accurate results.