Download or read book Dynamic Portfolio Choice with Bayesian Learning written by Georgios Skoulakis and published by . This book was released on 2008 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines the importance of parameter uncertainty and learning in the context of dynamic portfolio choice. In a discrete time setting, we consider a Bayesian investor who faces parameter uncertainty and solves her portfolio choice problem while updating her beliefs about the parameters. For different return data generating processes, including i.i.d. returns, autoregressive returns, and exogenous predictability, we show how the investor makes dynamic portfolio choices, taking into account that she will learn from future data. We find that, in general, learning introduces negative horizon effects and that ignoring parameter uncertainty may lead to significant losses in certainty equivalent return on wealth. However, the significance of learning is reduced when the investor uses more past data in her estimation and/or when her risk aversion increases. Learning about unconditional expected returns appears to be the most important aspect of the learning process. Using the earnings-to-price ratio as a predictor and an empirical Bayes prior, we find that learning reduces, but does not necessarily eliminate, the positive hedging demands induced by predictability and correlation between the return and predictor innovations.

Download or read book Dynamic Bayesian Learning and Optimization in Portfolio Choice Models written by Shea Daniel Chen and published by . This book was released on 2014 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop two dynamic Bayesian portfolio allocation models that address questions of learning and model uncertainty by taking model-specific shortcomings into account. In our first model, we formulate a multi-period portfolio choice problem in which the investor is uncertain about parameters of the model, can learn these parameters over time from observing asset returns, but is also concerned about robustness. To address these concerns, we introduce an objective function which can be regarded as a Bayesian version of relative regret. The optimal portfolio is characterized and shown to involve a ``tilted'' posterior, where the tilting is defined in terms of a family of stochastic benchmarks. We have found this model to perform at least as well as a benchmark given the true market parameters, while outperforming it when the market assets have the same trend. Our next model extends the Black-Litterman portfolio choice model by taking several potential errors into account. We extend Black-Litterman to multiple periods, which allows for us to take into account the pairs of expert forecasts and the realized return. By doing so, we can then perform inference on these experts and discover whether they may have any bias for or against any specific assets. We can also perform similar inference on the market equilibrium distribution, which is typically represented by the capital asset pricing model (CAPM). The result is a model that is analytically intractable but may be solved numerically via Gibbs sampling. Controlled tests show our model performs favorably when Black-Litterman's model assumptions about the market equilibrium and expert views are violated. Backtests shed light on the model's ability to account for CAPM's shortcomings.

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 A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning About Return Predictability written by Michael W. Brandt and published by . This book was released on 2009 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values.

Download or read book Sequential Binary Investment Decisions written by Werner Jammernegg and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes some models from the theory of investment which are mainly characterized by three features. Firstly, the decision-maker acts in a dynamic environment. Secondly, the distributions of the random variables are only incompletely known at the beginning of the planning process. This is termed as decision-making under conditions of uncer tainty. Thirdly, in large parts of the work we restrict the analysis to binary decision models. In a binary model, the decision-maker must choose one of two actions. For example, one decision means to undertake the invest ·ment project in a planning period, whereas the other decision prescribes to postpone the project for at least one more period. The analysis of dynamic decision models under conditions of uncertainty is not a very common approach in economics. In this framework the op timal decisions are only obtained by the extensive use of methods from operations research and from statistics. It is the intention to narrow some of the existing gaps in the fields of investment and portfolio analysis in this respect. This is done by combining techniques that have been devel oped in investment theory and portfolio selection, in stochastic dynamic programming, and in Bayesian statistics. The latter field indicates the use of Bayes' theorem for the revision of the probability distributions of the random variables over time.

Download or read book Some Contributions of Bayesian and Computational Learning Methods to Portfolio Selection Problems written by Johann Nicolle and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present thesis is a study of different optimal portfolio allocation problems in the case where the appreciation rate, named the drift, of the Brownian motion driving the dynamics of the assets is uncertain. We consider an investor having a belief on the drift in the form of a probability distribution, called a prior. The uncertainty about the drift is managed through a Bayesian learning approach which allows for the update of the drift's prior probability distribution. The thesis is divided into two self-contained parts; the first part being split into two chapters: the first develops the theory and the second contains a detailed application to actual market data. A third part constitutes an Appendix and details the data used in the applications. The first part of the thesis is dedicated to the multidimensional Markowitz portfolio selection problem in the case of drift uncertainty. This uncertainty is modeled via an arbitrary prior law which is updated using Bayesian filtering. We first embed the Bayesian-Markowitz problem into an auxiliary standard control problem for which dynamic programming is applied. Then, we show existence and uniqueness of a smooth solution to the related semi-linear partial differential equation (PDE). In the case of a Gaussian prior probability distribution, the multidimensional solution is explicitly computed. Additionally, we study the quantitative impact of learning from the progressively observed data, by comparing the strategy which updates the initial estimate of the drift, i.e. the learning strategy, to the one that keeps it constant, named the non-learning strategy. Ultimately, we analyze the sensitivity of the gain from learning, called value of information or informative value, with respect to different parameters. Next, we illustrate the theory with a detailed application of the previous results on actual market data. We emphasize the robustness of the value added of learning by comparing learning to non-learning optimal strategies in different investment universes: indices of various asset classes, currencies and smart beta strategies. The second part tackles a discrete-time portfolio optimization problem. Here, the goal of the investor is to maximize the expected utility of the terminal wealth of a portfolio of risky assets, assuming an uncertain drift and a maximum drawdown constraint. In this part, we formulate the problem in the general case, and we solve numerically the Gaussian case with the Constant Relative Risk Aversion (CRRA) type utility function via a deep learning resolution. Ultimately, we study the sensitivity of the strategy to the degree of uncertainty of the drift and, as a byproduct, give empirical evidence of the convergence of the non-learning strategy towards a no short-sale constrained Merton problem.

Download or read book Empirical Bayes Estimation with Dynamic Portfolio Models written by Leonard C. MacLean and published by . This book was released on 2004 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Theory of Dynamic Portfolio Choice for Maximization of Survival Probability written by Santanu Roy and published by . This book was released on 1992 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multiperiod Portfolio Selection and Bayesian Dynamic Models written by Petter N. Kolm and published by . This book was released on 2017 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Techniques inspired by Bayesian statistics provide an elegant solution to the classic investment problem of optimally planning a sequence of trades in the presence of transaction costs.

Download or read book Portfolio Management under Stress written by Riccardo Rebonato and published by Cambridge University Press. This book was released on 2014-01-09 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio Management under Stress offers a novel way to apply the well-established Bayesian-net methodology to the important problem of asset allocation under conditions of market distress or, more generally, when an investor believes that a particular scenario (such as the break-up of the Euro) may occur. Employing a coherent and thorough approach, it provides practical guidance on how best to choose an optimal and stable asset allocation in the presence of user specified scenarios or 'stress conditions'. The authors place causal explanations, rather than association-based measures such as correlations, at the core of their argument, and insights from the theory of choice under ambiguity aversion are invoked to obtain stable allocations results. Step-by-step design guidelines are included to allow readers to grasp the full implementation of the approach, and case studies provide clarification. This insightful book is a key resource for practitioners and research academics in the post-financial crisis world.

Download or read book Bayesian Forecasting and Dynamic Models written by Mike West and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.

Download or read book Machine Learning in Portfolio and Risk Management written by Timothy Tao Hin Law and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates the applications of machine learning in Financial Portfolio and Risk Management. The focus is to customize machine learning algorithms to accommodate the intuitions or practical needs in the domain. Empirical experiments are carried out to examine the proposed customizations. An extensive breadth of machine learning topics are discussed, explored, and extended. The experiments in this thesis represent the customization of algorithms in three aspects of any portfolio and risk management system: 1. A generic prediction framework that automates predictions to provide insights for future expectations. 2. A risk-aware agent that controls the balance between actively shifting a portfolio and the transaction costs involved. 3. A robust dynamic portfolio selection algorithm that continually diversifies to track switching regimes. Experiment 1: Practical Bayesian support vector regression for Financial Time Series Prediction The first experiment outlines a generic prediction framework that takes advantage of the powerful support vector regression. This framework introduces a faster and easier parameter selection process to determine the model that generates predictions and the corresponding uncertainty estimates. It is shown that the generalization performance of this parameter selection process can reach or sometimes surpass the computationally expensive cross-validation procedure. In addition, an ad-hoc adaptive calibration process is described to enable practical use of the prediction uncertainty estimates to assess the quality of predictions, which is also interpreted as a potential indicator of market condition changes. Experiment 2: Risk-Aware Reinforcement Learning Algorithm to Improve a Portfolio The model-free Monte Carlo control reinforcement learning algorithm is extended, by making use of its episodic nature, to allow consideration of "risk" when training the algorithm. The risk-aware reinforcement learning algorithm introduced allows the user to intuitively and flexibly incorporate any form(s) of risk consideration desired. A procedure is then suggested to filter out potentially unstable policies. The risk-aware mechanism is examined, and its abilities to control "risk" are demonstrated in empirical experiments. In addition, it is recommended to diversity out-of-sample by simultaneously following multiple policies with high in-sample Sharpe ratio. Experiment 3: Expert Advice Algorithms for Dynamic Portfolio Selection The connections between online machine learning and the sequential investment problem are explored in this experiment, and the Smart Switching Portfolio (SSP) Algorithm is proposed. It continually diversifies wealth to assets based on their previous performances to track switching regimes. A newly introduced scaling parameter illustrates the linkage between the learning rate and the action of leveraging. Moreover, the algorithm is theoretically generalized to select assets from a dynamic pool of investible assets. The behavior of the SSP Algorithm is examined. The effect of the new parameter under different volatility levels is also assessed. The proposed algorithm is shown to be the most robust. It outperforms some well-known algorithms, and is particularly impressive as transaction cost increases. A few ad-hoc methods are proposed to potentially enhance the algorithm further.

Download or read book Dynamic Portfolio Choice under Ambiguity and Regime Switching Mean Returns written by Hening Liu and published by . This book was released on 2011 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: I examine a continuous-time intertemporal consumption and portfolio choice problem under ambiguity, where expected returns of a risky asset follow a hidden Markov chain. Investors with Chen and Epstein''s (2002) recursive multiple priors utility possess a set of priors for unobservable investment opportunities. We explicitly characterize optimal consumption and portfolio policies in terms of the Malliavin derivatives and stochastic integrals. When the model is calibrated to U.S. stock market data, I find that continuous Bayesian revisions under incomplete information generate ambiguity-driven hedging demands that mitigate intertemporal hedging demands. In addition, ambiguity aversion magnifies the importance of hedging demands in the optimal portfolio policies. Out-of-sample experiments demonstrate the economic importance of accounting for ambiguity.

Download or read book Frontiers of Statistical Decision Making and Bayesian Analysis written by Ming-Hui Chen and published by Springer Science & Business Media. This book was released on 2010-07-24 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.

Download or read book Predictions Nonlinearities and Portfolio Choice written by Friedrich Christian Kruse and published by BoD – Books on Demand. This book was released on 2012 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finance researchers and asset management practitioners put a lot of effort into the question of optimal asset allocation. With this respect, a lot of research has been conducted on portfolio decision making as well as quantitative modeling and prediction models. This study brings together three fields of research, which are usually analyzed in an isolated manner in the literature: - Predictability of asset returns and their covariance matrix - Optimal portfolio decision making - Nonlinear modeling, performed by artificial neural networks, and their impact on predictions as well as optimal portfolio construction Including predictability in asset allocation is the focus of this work and it pays special attention to issues related to nonlinearities. The contribution of this study to the portfolio choice literature is twofold. First, motivated by the evidence of linear predictability, the impact of nonlinear predictions on portfolio performances is analyzed. Predictions are empirically performed for an investor who invests in equities (represented by the DAX index), bonds (represented by the REXP index) and a risk-free rate. Second, a solution to the dynamic programming problem for intertemporal portfolio choice is presented. The method is based on functional approximations of the investor's value function with artificial neural networks. The method is easily capable of handling multiple state variables. Hence, the effect of adding predictive parameters to the state space is the focus of analysis as well as the impacts of estimation biases and the view of a Bayesian investor on intertemporal portfolio choice. One important empirical result shows that residual correlation among state variables have an impact on intertemporal portfolio decision making.

Download or read book Incumbents Challenges and Bandits written by Jeffrey S. Banks and published by . This book was released on 1990 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Dynamic Portfolio Choice with Multiple Conditioning Variables written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: