The Optimal Use Of Return Predictability
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| Author | : Abhay Abhyankar |
| Publisher | : |
| Total Pages | : 45 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
In this paper we investigate the empirical performance of unconditionally efficient portfolios strategies for a number of commonly used predictive variables. These strategies, which optimally utilize asset return predictability in portfolio formation were studied by Hansen and Richard (1987) and Ferson and Siegel (2001). Our criterion is to maximize various ex-post performance measures and we conduct both in-sample as well as out-of-sample analysis. Our analysis allows us to determine the economic value of using different predictor variables and also groups of predictor variables.Overall we find that the optimal use of conditioning information significantly improves the risk-return tradeoff available to a mean-variance investor relative to fixed weight strategies. These findings are consistent across portfolio efficiency measures such as Sharpe ratios, portfolio variance subject to a mean constraint or portfolio mean subject to a volatility constraint as well as measures of economic value such as switching costs.In addition we also compare the performance of the unconditionally efficient strategies with conditionally efficient strategies from an investment-based perspective. We find that the performance of the two strategies is quite different due to the differing response of the portfolio weights of the two strategies to conditioning information.
| Author | : Robert A. Meyers |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 919 |
| Release | : 2010-11-03 |
| Genre | : Business & Economics |
| ISBN | : 1441977007 |
Finance, Econometrics and System Dynamics presents an overview of the concepts and tools for analyzing complex systems in a wide range of fields. The text integrates complexity with deterministic equations and concepts from real world examples, and appeals to a broad audience.
| Author | : Murray Carlson |
| Publisher | : |
| Total Pages | : 70 |
| Release | : 2001 |
| Genre | : |
| ISBN | : |
A number of recent studies have measured the quantitative effect of excess return predictability on the optimal consumption and portfolio choices of a rational investor, and they have used the utility costs of ignoring predictability as a natural measure of economic significance. We use a general equilibrium model as a laboratory for generating predictable excess returns and for assessing the properties of the estimated consumption/portfolio rules, under both the empirical and the true dynamics of excess returns. We find that conditional rules based on ordinary least squares estimates of excess returns are severely biased, and they have a large variance across multiple simulated histories of the model. In this experiment, we find the estimation issues to be so severe that the simple unconditional consumption and portfolio rules, from Merton (1969), actually outperform (in a utility cost sense) both simple and bias-corrected empirical estimates of conditionally optimal policies.
| Author | : Bernt Øksendal |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 263 |
| Release | : 2007-04-26 |
| Genre | : Mathematics |
| ISBN | : 3540698264 |
Here is a rigorous introduction to the most important and useful solution methods of various types of stochastic control problems for jump diffusions and its applications. Discussion includes the dynamic programming method and the maximum principle method, and their relationship. The text emphasises real-world applications, primarily in finance. Results are illustrated by examples, with end-of-chapter exercises including complete solutions. The 2nd edition adds a chapter on optimal control of stochastic partial differential equations driven by Lévy processes, and a new section on optimal stopping with delayed information. Basic knowledge of stochastic analysis, measure theory and partial differential equations is assumed.
| Author | : Abhay Abhyankar |
| Publisher | : |
| Total Pages | : 44 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
In this paper we propose a new Sharpe ratio based test of asset return predictability. Intuitively, a variable that predicts returns is of value to an investor if it allows the construction of 'managed' portfolios that expand the unconditional mean-variance efficient frontier, and thus the investor's opportunity set. The maximum Sharpe ratio achievable using the predictive information efficiently therefore provides a convenient measure of the extent to which predictability matters. We build on the conditional asset pricing theory of Hansen and Richard (1987) to explicitly characterize the difference in maximum squared Sharpe ratios with and without conditioning information. We show that this difference is directly related to the R^2 of a predictive regression. Our test statistic is closely related to the Wald test for the regression coefficient. Under the null hypothesis of no predictability, the difference in squared Sharpe ratios is zero. Rejection of the null hypothesis thus implies that the presence of return predictability significantly expands the mean-variance frontier.Using our test, we find that at short (monthly) horizon, using the consumption-wealth ratio as predictor variable, (Lettau and Ludvigson, 2001), we clearly reject the null hypothesis of no predictability. In contrast, dividend yield has at most marginal effect. However, at longer horizons the effect of dividend yield becomes more pronounced. An important implication of our results is that neither the fixed-weight three-factor Fama-French (1988) model, nor the Carhart (1996) model, can be viable conditional asset pricing models when consumption-wealth ratio is chosen as the conditioning variable. Our analysis is closely related to, and extends the work of Ferson and Siegel (2001), Bekaert and Liu (2001), and Kirby (1998).
| Author | : Graham Elliott |
| Publisher | : Elsevier |
| Total Pages | : 667 |
| Release | : 2013-08-23 |
| Genre | : Business & Economics |
| ISBN | : 0444627405 |
The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. - Focuses on innovation in economic forecasting via industry applications - Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications - Makes details about economic forecasting accessible to scholars in fields outside economics
| Author | : Wayne Ferson |
| Publisher | : MIT Press |
| Total Pages | : 497 |
| Release | : 2019-03-12 |
| Genre | : Business & Economics |
| ISBN | : 0262039370 |
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.
| Author | : Hisham Abou-Kandil |
| Publisher | : Birkhäuser |
| Total Pages | : 584 |
| Release | : 2012-12-06 |
| Genre | : Science |
| ISBN | : 3034880812 |
The authors present the theory of symmetric (Hermitian) matrix Riccati equations and contribute to the development of the theory of non-symmetric Riccati equations as well as to certain classes of coupled and generalized Riccati equations occurring in differential games and stochastic control. The volume offers a complete treatment of generalized and coupled Riccati equations. It deals with differential, discrete-time, algebraic or periodic symmetric and non-symmetric equations, with special emphasis on those equations appearing in control and systems theory. Extensions to Riccati theory allow to tackle robust control problems in a unified approach. The book makes available classical and recent results to engineers and mathematicians alike. It is accessible to graduate students in mathematics, applied mathematics, control engineering, physics or economics. Researchers working in any of the fields where Riccati equations are used can find the main results with the proper mathematical background.
| Author | : David G. McMillan |
| Publisher | : |
| Total Pages | : 40 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
This paper considers whether the cyclical component of the log dividend-price and price-earnings ratios contain forecast power for stock returns. While the levels of these series contain slow moving information for predicting long horizon returns, for short-horizon returns it is the relative movement between prices and fundamental that matters for investors, and whether prices are accelerating away or converging with fundamentals. We use three approaches to extract the cyclical component of these ratios and conduct a range of in-sample and out-of-sample tests. In addition to the cyclical components, we include further predictive variables that account for economic growth and the relation between stocks and bonds. In-sample estimation using the ratio levels reveals results that do not accord with economic intuition. In contrast, using the cyclical component leads to economically sensible values, as well as improved in-sample fit. Out of-sample forecasting reveals that in comparison to a historical mean model, the performance of our predictive models is generally better, although that depends on metrics used to evaluate the forecasts. Moreover, the cyclical component models outperform the levels based models. Notably, the historical mean model is preferred using standard mean absolute and squared errors measures but the predictive models perform better using Mincer-Zarnowitz and related encompassing regressions. Notably, when using economic based forecast evaluation, the predictive models are clearly preferred, with a stronger ability to predict the future direction of return movements and in obtaining higher trading returns. A further examination of the results reveals that this greater performance largely arises from a superior ability to predict future negative returns.
| Author | : Emmanuel Jurczenko |
| Publisher | : John Wiley & Sons |
| Total Pages | : 460 |
| Release | : 2020-10-06 |
| Genre | : Business & Economics |
| ISBN | : 1786305445 |
This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
