Download or read book The Horizon Effect of Stock Return Predictability and Model Uncertainty on Portfolio Choice written by Guangjie Li and published by . This book was released on 2009 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays on Portfolio Choice with Bayesian Methods written by Deniz Kebabci and published by . This book was released on 2007 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: How investors should allocate assets to their portfolios in the presence of predictable components in asset returns is a question of great importance in finance. While early studies took the return generating process as given, recent studies have addressed issues such as parameter estimation and model uncertainty. My dissertation develops Bayesian methods for portfolio choice - and industry allocation in particular - under parameter and model uncertainty. The first chapter of my dissertation, Allocation to Industry Portfolios under Markov Switching Returns, addresses the effect of parameter estimation error on the relation between asset holdings and the investment horizon. This paper assumes that returns follow a regime switching process with unknown parameters. Parameter uncertainty is accounted for through a Gibbs sampling approach. After accounting for parameter estimation error, buy-and-hold investors are generally found to allocate less to stocks the longer the investment horizon. When the dividend yield and T-bill rates are included as predictor variables, the effect of these predictor variables is minimal, and the allocation to stocks is still smaller, the longer the investor's horizon. The second chapter of my dissertation, Portfolio Choice Implications of Parameter and Model Uncertainty in Factor Models, uses industry portfolios to examine the implications of incorporating uncertainty about a range of (conditionally) linear factor models. The paper specifically examines a CAPM, a linear factor model with different predictor variables (dividend yield, price to book ratio, price to earnings ratio, and price to sales ratio) and a time-varying CAPM specification. All approaches incorporate parameter uncertainty in a mean-variance framework. Time-varying CAPM specifications are intuitive in the sense that one cannot expect the environment for each industry to stay constant through time, and so the underlying parameters can be expected to be time-varying as well. Accounting for time- variation in market betas improves the portfolio performance as measured, e.g., by the Sharpe ratio compared to both an unconditional CAPM and a linear factor model with different predictor variables. The paper also looks at the implications for portfolio performance of utilizing a Black-Litterman approach versus a standard mean-variance approach in the asset allocation step. The former can be thought as a model averaging approach and thus can be expected to help dealing with model uncertainty besides the parameter estimation uncertainty. The third chapter of my dissertation, Style Investing with Uncertainty, develops methods to look at style investing. This paper analyzes the determinants that affect style investing, such as style momentum, and predictor variables such as different macro variables (e.g. yield spread, inflation, term structure, industrial production, etc.) and looks at how learning about these variables affects the predictability of returns. Uncertainty in this paper is incorporated using a time-varying parameter model. Returns on style portfolios such as value and size appear to be related to inflation and other macro variables.
Download or read book Stock Return Predictability and Model Uncertainty written by Doron Avramov and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We use Bayesian model averaging to analyze the sample evidence on return predictability in the presence of model uncertainty. The analysis reveals in-sample and out-of-sample predictability, and shows that the out-of-sample performance of the Bayesian approach is superior to that of model selection criteria. We find that term and market premia are robust predictors. Moreover, small-cap value stocks appear more predictable than large-cap growth stocks. We also investigate the implications of model uncertainty from investment management perspectives. We show that model uncertainty is more important than estimation risk, and investors who discard model uncertainty face large utility losses.
Download or read book Portfolio Choice Problems written by Nicolas Chapados and published by Springer Science & Business Media. This book was released on 2011-07-12 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief offers a broad, yet concise, coverage of portfolio choice, containing both application-oriented and academic results, along with abundant pointers to the literature for further study. It cuts through many strands of the subject, presenting not only the classical results from financial economics but also approaches originating from information theory, machine learning and operations research. This compact treatment of the topic will be valuable to students entering the field, as well as practitioners looking for a broad coverage of the topic.
Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Download or read book Specification Searches written by E. E. Leamer and published by . This book was released on 1978-04-24 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers a radically new approach to inference with nonexperimental data when the statistical model is ambiguously defined. Examines the process of model searching and its implications for inference. Identifies six different varieties of specification searches, discussing the inferential consequences of each in detail.
Download or read book Statistics of Random Processes II written by Robert S. Liptser and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Written by two renowned experts in the field, the books under review contain a thorough and insightful treatment of the fundamental underpinnings of various aspects of stochastic processes as well as a wide range of applications. Providing clear exposition, deep mathematical results, and superb technical representation, they are masterpieces of the subject of stochastic analysis and nonlinear filtering....These books...will become classics." --SIAM REVIEW
Download or read book Essays on Stock Return Predictability and Portfolio Allocation written by Bradley Steele Paye and published by . This book was released on 2004 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Three Essays on the Effect of Learning and Predictability on Optimal Dynamic Portfolio Strategies and Asset Prices written by Yihong Xia and published by . This book was released on 2000 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stock Return Predictability written by Martijn Cremers and published by . This book was released on 2002 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: Attempts to characterize stock return predictability have generated a plethora of papers documenting the ability of various variables to explain conditional expected returns. However, there is little consensus on what the important conditioning variables are, giving rise to a great deal of model uncertainty and data snooping fears. In this paper, we introduce a new methodology that explicitly takes the model uncertainty into account by comparing all possible models simultaneously and in which the priors are calibrated to reflect economically meaningful prior information. Therefore, our approach minimizes data snooping given the information set and the priors. We compare the prior views of a skeptic and a confident investor. The data imply posterior probabilities that are in general more supportive of stock return predictability than the priors for both types of investors, over a wide range of prior views. Furthermore, the stalwarts such as dividends and past returns do not perform well. The out-of- sample results for the Bayesian average models show improved forecasts relative to the classical statistical model selection methods, are consistent with the in-sample results and show some, albeit small, evidence of predictability.
Download or read book Forecasting Expected Returns in the Financial Markets written by Stephen Satchell and published by Elsevier. This book was released on 2011-04-08 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting returns is as important as forecasting volatility in multiple areas of finance. This topic, essential to practitioners, is also studied by academics. In this new book, Dr Stephen Satchell brings together a collection of leading thinkers and practitioners from around the world who address this complex problem using the latest quantitative techniques.*Forecasting expected returns is an essential aspect of finance and highly technical *The first collection of papers to present new and developing techniques *International authors present both academic and practitioner perspectives
Download or read book The Implications of Return Predictability on Long term Portfolio Choice written by Pascal Gisclon and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays on the Predictability and Volatility of Returns in the Stock Market written by Ruojun Wu and published by . This book was released on 2008 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation studies the effect of parameter uncertainty on the return predictability and volatility of the stock market. The first two chapters focus on the decomposition of market volatility, and the third chapter studies the return predictability. When facing imperfect information, the investors tend to form a learning scheme that encompasses both historical data and prior beliefs. In the variance decomposition framework, the introducing of learning directly impacts the way that return forecasts are revised and consequently the relative component of market volatility based on these forecasts, namely the price movements from revision on future discount rates and those from future cash flows. According to the empirical study in Chapter 1, the former is not necessarily the major driving force of market volatility, which provides an alternative view on what moves stock prices. Learning is modeled and estimated by Bayesian method. Chapter 2 follows the topic in Chapter 1 and studies the role of persistent state variables in return decomposition in order to provide more robust inference on variance decomposition. In Chapter 3 we propose to utilize theoretical constraints to help predict market returns when in sample data is very noisy and creates model uncertainty for the investors. The constraints are also incorporated by Bayesian method. We show in the out-of-sample forecast experiment that models with theoretical constraints produce better forecasts.
Download or read book The Predictability of Stock Returns written by Zhong-guo Zhou and published by . This book was released on 1993 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stock Return Predictability and the Drift Between the Outcomes of Portfolio Investment Strategies written by Dirk P.M. De Wit and published by . This book was released on 2013 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anomalies found in tests of market efficiency do not necessarily imply that security prices do not reflect all available information, as the asset-pricing model used to describe the return generating process might also be false. In the present study, this joint hypothesis problem does not arise, because no use is made of an asset-pricing model. Instead, stock return predictability is tested by verifying whether the underlying variables of the drift between different types of indexes are correlated. This unambiguously tests for the sources of return predictability, which can be related to empirical anomalies, such as the "firm-size effect" and the "winner-loser effect". The drift between indexes is large if the (cross-sectional) variation of the underlying variables is large relative to their mean values, and vice versa. The size-related drift, for instance, is shown to be particularly large, but it also appears to be easily rendered statistically insignificant.
Download or read book The Economic Significance of Some Simple Models of Time Series Stock Return Predictability written by Ben Jacobsen and published by . This book was released on 1999 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we study the economic significance of simple time series models of stock return predictability. We investigate the practical usefulness of recent findings on time series return predictability of stock returns and their volatility for dynamic tactical asset allocation decisions. We introduce a mean variance investor with an investment horizon of one year who takes investment decisions daily. When stock returns follow a random walk this investor holds constant proportions of a stock market index and a risk free asset. Using past data and knowledge of some well known return predictability results (i.e. predictability based on calendar anomalies and predictability from economic variables like dividend yields and short term interest rates), we evaluate whether, how and to what extent these predictability results might affect his investment decisions. For this investor we also investigate the practical usefulness of knowledge about the predictability over time of market volatility. The design we choose is as follows. We give the predictability results the benefit of the doubt and assume that all the estimates and models are correct and indeed accurately describe the true return generating process. We then analyze analytically, numerically and by Monte Carlo simulation the effect of investment decisions--conditional on these predictability results--on the return distribution of his portfolio. We also introduce transactions costs in this setting; these influence investment choices. Our main findings are that small transactions costs substantially reduce potential benefits of trading on calendar anomalies. Generally, however trading remains profitable under the assumption that stock market returns are partially predictable from economic variables like dividend yields and interest rates. This holds for relatively large transactions costs. Trading on volatility predictability is not profitable, except in the case of negligible transactions costs.
Download or read book International Stock Return Predictability Under Model Uncertainty written by Andreas Schrimpf and published by . This book was released on 2008 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
