Download or read book Portfolio Optimization Using Fundamental Indicators Based on Multi Objective EA written by Antonio Daniel Silva and published by Springer. This book was released on 2016-02-11 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a new approach to portfolio composition in the stock market. It incorporates a fundamental approach using financial ratios and technical indicators with a Multi-Objective Evolutionary Algorithms to choose the portfolio composition with two objectives the return and the risk. Two different chromosomes are used for representing different investment models with real constraints equivalents to the ones faced by managers of mutual funds, hedge funds, and pension funds. To validate the present solution two case studies are presented for the SP&500 for the period June 2010 until end of 2012. The simulations demonstrates that stock selection based on financial ratios is a combination that can be used to choose the best companies in operational terms, obtaining returns above the market average with low variances in their returns. In this case the optimizer found stocks with high return on investment in a conjunction with high rate of growth of the net income and a high profit margin. To obtain stocks with high valuation potential it is necessary to choose companies with a lower or average market capitalization, low PER, high rates of revenue growth and high operating leverage

Download or read book Bio inspired Information and Communications Technologies written by Yifan Chen and published by Springer Nature. This book was released on 2023-10-26 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed conference proceedings of the 14th International Conference on Bio-inspired Information and Communications Technologies, held in Okinawa, Japan, during April 11-12, 2023. The 17 full papers were carefully reviewed and selected from 33 submissions. The papers focus on the latest research that leverages the understanding of key principles, processes, and mechanisms in biological systems for development of novel information and communications technologies (bio-inspired ICT). BICT 2023 will also highlight innovative research and technologies being developed for biomedicine that are inspired by ICT (ICT-inspired biomedicine).

Download or read book Fuzzy Portfolio Optimization written by Pankaj Gupta and published by Springer. This book was released on 2014-03-17 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents a comprehensive study of portfolio optimization, an important area of quantitative finance. Considering that the information available in financial markets is incomplete and that the markets are affected by vagueness and ambiguity, the monograph deals with fuzzy portfolio optimization models. At first, the book makes the reader familiar with basic concepts, including the classical mean–variance portfolio analysis. Then, it introduces advanced optimization techniques and applies them for the development of various multi-criteria portfolio optimization models in an uncertain environment. The models are developed considering both the financial and non-financial criteria of investment decision making, and the inputs from the investment experts. The utility of these models in practice is then demonstrated using numerical illustrations based on real-world data, which were collected from one of the premier stock exchanges in India. The book addresses both academics and professionals pursuing advanced research and/or engaged in practical issues in the rapidly evolving field of portfolio optimization.

Download or read book Multicriteria Portfolio Management written by Panos Xidonas and published by Springer Science & Business Media. This book was released on 2012-05-09 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary purpose in this book is to present an integrated and innovative methodological approach for the construction and selection of equity portfolios. The approach takes into account the inherent multidimensional nature of the problem, while allowing the decision makers to incorporate specified preferences in the decision processes. A fundamental principle of modern portfolio theory is that comparisons between portfolios are generally made using two criteria; the expected return and portfolio variance. According to most of the portfolio models derived from the stochastic dominance approach, the group of portfolios open to comparisons is divided into two parts: the efficient portfolios, and the dominated. This work integrates the two approaches providing a unified model for decision making in portfolio management with multiple criteria.​

Download or read book Multi objective Evolutionary Methods for Time changing Portfolio Optimization Problems written by Iason Hatzakis and published by . This book was released on 2007 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is focused on the discovery of efficient asset allocations with the use of evolutionary algorithms. The portfolio optimization problem is a multi-objective optimization problem for the conflicting criteria of risk and expected return. Furthermore the nonstationary nature of the market makes it a time-changing problem in which the optimal solution is likely to change as time advances. Hence the portfolio optimization problem naturally lends itself to an exploration with multi-objective evolutionary algorithms for time-changing environments. Two different risk objectives are treated in this work: the established measure of standard deviation, and the Value-at-Risk. While standard deviation is convex as an objective function, historical Value-at-Risk is non-convex and often discontinuous, making it difficult to approach with most conventional optimization techniques. The value of evolutionary algorithms is demonstrated in this case by their ability to handle the Value-at-Risk objective, since they do not have any convexity or differentiability requirements. The D-QMOO time-changing evolutionary algorithm is applied to the portfolio optimization problem. Part of the philosophy behind D-QMOO is the exploitation of predictability in the optimal solution's motion. This problem however is characterized by minimal or non-existent predictability, since asset prices are hard to forecast. This encourages the development of new time-changing optimization heuristics for the efficient solution of this problem. Both the static and time-changing forms of the problem are treated and characteristic results are presented. The methodologies proposed are verified through comparison with established methods and through the performance of the produced portfolios as compared to the overall market. In general, this work demonstrates the potential for the use of evolutionary algorithms in time-changing portfolio optimization as a tool for portfolio managers and financial engineers.

Download or read book Portfolio Selection Using Multi Objective Optimisation written by Saurabh Agarwal and published by Springer. This book was released on 2017-08-21 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the risk-return paradox in portfolio selection by incorporating multi-objective criteria. Empirical research is presented on the development of alternate portfolio models and their relative performance in the risk/return framework to provide solutions to multi-objective optimization. Next to outlining techniques for undertaking individual investor’s profiling and portfolio programming, it also offers a new and practical approach for multi-objective portfolio optimization. This book will be of interest to Foreign Institutional Investors (FIIs), Mutual Funds, investors, and researchers and students in the field.

Download or read book Portfolio Optimization and Performance Analysis written by Jean-Luc Prigent and published by CRC Press. This book was released on 2007-05-07 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: In answer to the intense development of new financial products and the increasing complexity of portfolio management theory, Portfolio Optimization and Performance Analysis offers a solid grounding in modern portfolio theory. The book presents both standard and novel results on the axiomatics of the individual choice in an uncertain framework, cont

Download or read book A Hybrid Multi Objective Optimization Approach For Portfolio Selection Problem written by Osman Pala and published by . This book was released on 2017 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio selection problem is a major subject in finance where investors deal with selecting satisfying portfolio which is composed of a vast number of risky assets, under some restricting criteria that are defined by themselves. Asset prices can be effected from different events, such as political crisis, financial turmoil and technological improvements. Due to uncertainty nature of these events, it is difficult to forecast future prices of assets. However, Markowitz's Modern Portfolio Theory, which is mainly focused on portfolio risk, introduced a new idea for asset diversification in portfolio optimization. According to this approach, an investor can reduce portfolio risk simply by holding combinations of assets that are not perfectly positively correlated and also efficient portfolio can only be obtained by focusing portfolio return and risk together. In this paper, a two stage multi objective portfolio selection model is proposed for obtaining best portfolio. In the first stage, Pareto efficient portfolios are obtained by genetic algorithm with using mean and variance of assets. Then in the second stage a multi criteria decision method is applied for ranking Pareto-optimum portfolios that are obtained in previous stage. Effectiveness of criteria, such as entropy measures and higher moments are taken into consideration and also performance ratios are examined in evaluating Pareto efficient portfolios and their rankings. An illustrated example is given and results of proposed model are discussed in experimental section.

Download or read book Robust Equity Portfolio Management written by Woo Chang Kim and published by John Wiley & Sons. This book was released on 2015-11-30 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive portfolio optimization guide, with provided MATLAB code Robust Equity Portfolio Management + Website offers the most comprehensive coverage available in this burgeoning field. Beginning with the fundamentals before moving into advanced techniques, this book provides useful coverage for both beginners and advanced readers. MATLAB code is provided to allow readers of all levels to begin implementing robust models immediately, with detailed explanations and applications in the equity market included to help you grasp the real-world use of each technique. The discussion includes the most up-to-date thinking and cutting-edge methods, including a much-needed alternative to the traditional Markowitz mean-variance model. Unparalleled in depth and breadth, this book is an invaluable reference for all risk managers, portfolio managers, and analysts. Portfolio construction models originating from the standard Markowitz mean-variance model have a high input sensitivity that threatens optimization, spawning a flurry of research into new analytic techniques. This book covers the latest developments along with the basics, to give you a truly comprehensive understanding backed by a robust, practical skill set. Get up to speed on the latest developments in portfolio optimization Implement robust models using provided MATLAB code Learn advanced optimization methods with equity portfolio applications Understand the formulations, performances, and properties of robust portfolios The Markowitz mean-variance model remains the standard framework for portfolio optimization, but the interest in—and need for—an alternative is rapidly increasing. Resolving the sensitivity issue and dramatically reducing portfolio risk is a major focus of today's portfolio manager. Robust Equity Portfolio Management + Website provides a viable alternative framework, and the hard skills to implement any optimization method.

Download or read book Robust Portfolio Optimization and Management written by Frank J. Fabozzi and published by John Wiley & Sons. This book was released on 2007-04-27 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Robust Portfolio Optimization and Management "In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real-world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a technically rigorous yet remarkably accessible guide to the latest advances in portfolio construction." --Mark Kritzman, President and CEO, Windham Capital Management, LLC "The topic of robust optimization (RO) has become 'hot' over the past several years, especially in real-world financial applications. This interest has been sparked, in part, by practitioners who implemented classical portfolio models for asset allocation without considering estimation and model robustness a part of their overall allocation methodology, and experienced poor performance. Anyone interested in these developments ought to own a copy of this book. The authors cover the recent developments of the RO area in an intuitive, easy-to-read manner, provide numerous examples, and discuss practical considerations. I highly recommend this book to finance professionals and students alike." --John M. Mulvey, Professor of Operations Research and Financial Engineering, Princeton University

Download or read book Three Studies on Portfolio Optimization and Performance Appraisal written by Huazhu Zhang and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis studies three important issues in portfolio management: the impact of estimation risk on portfolio optimization, the role of fundamental analysis in portfolio selection and the power of the bootstrap approach for separating skill from luck across a sample of portfolio managers. The first study examines the practical value of the mean-variance portfolio optimization. This issue arises from the concern that the performance of the meanvariance portfolio suffers seriously from estimation errors in input parameters. Based on simulated asset returns, we compare the performance of selected popular portfolios against the naïve equally weighted portfolio (1/N) in terms of the Sharpe Ratio. We conclude that given relatively small and persistent anomalies, some sophisticated portfolio rules can outperform the naïve one at estimation windows of reasonable lengths. We find that (1) an estimation window of 120 months is needed for the optimization-based portfolio rules to outperform the 1/N rule when annual abnormal returns lie between a certain range; (2) given the same abnormal returns, even longer estimation windows are needed when asset returns exhibit fat tails; (3) our preferred portfolio rule, which combines optimally the sample tangency portfolio with MacKinlay and Pástor's (2000) portfolio, performs well relative to other rules. Our second study examines the role of fundamental analysis in portfolio selection. Fundamental analysis assumes implicitly that asset prices mean-revert to their fundamental values. We solve the instantaneous mean-variance portfolio choice problem when asset prices mean-revert to their fundamentals and analyze how this meanreversion feature affects the performance of the optimal portfolio. Our analytical results show that the expected appraisal ratio of the optimal portfolio is increasing in the meanreversion speed for a given stationary distribution of the mispricing and it is increasing in the standard deviation of the stationary distribution for a given level of the meanreversion speed. The contribution from dividends is positive, increasing in the dividend yield and is tantamount to increasing the mean-reversion speed. Our numerical examples indicate that fundamental analysis can be more helpful than practitioners' performance shows. One implication of this is that it must be very challenging to obtain reasonable forecasts of the mispricing. Our third study provides a simulation analysis of the power of the bootstrap approach for identifying skill among a large population of mutual funds. Unlike the standard t-test, this approach does not require ex ante parametric assumption on fund alphas and allows us to infer on the existence of genuine skill across a large sample of fund managers. Its recent applications in mutual fund performance analysis have produced strikingly different findings from those documented in the classical literature. However, as far as we know, its power has not been subject to any rigorous statistical analysis. We provide a Monte Carlo simulation analysis of the validity and power of this method by applying it to evaluating the performance of hypothetical funds under varieties of parameter assumptions. We find that this method can be misleading, which is true regardless of using alpha estimates or their t-statistics. This makes the recent findings dubious. The major problem with this method lies in the inappropriate use or misinterpretation of what Fama and French (2010) call "likelihoods" in testing for difference between realized and bootstrapped alphas at selected percentiles. We also show that the variance decomposition and the Kolmogrov-Smirnov test can lead to correct inferences on fund managers' skill when likelihoods fail to do so.

Download or read book Evolutionary Multi objective Optimisation for Large scale Portfolio Selection with Both Random and Uncertain Returns written by Kailong Liu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multi objective technique to solve large-scale portfolio optimisation problems with both long-term listed and newly listed securities. The future returns of long-term listed securities are defined as random variables whose probability distributions are estimated based on sufficient historical data, while the returns of newly listed securities are defined as uncertain variables whose uncertainty distribution are estimated based on experts' knowledge. Our approach defines security returns as theoretically uncertain random variables and proposes a three-moment optimisation model with practical trading constraints. In this study, a framework for applying arbitrary multi-objective evolutionary algorithms to portfolio optimisation is established, and a novel evolutionary algorithm based on large-scale optimisation techniques is developed to solve the proposed model. The experimental results show that the proposed algorithm outperforms state-of-the-art evolutionary algorithms in large-scale portfolio optimisation.

Download or read book Multi Objective Portfolio Optimization by Mixed Integer Programming written by Bartosz Sawik and published by . This book was released on 2016 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this PhD dissertation ...

Download or read book Fusing Economic Indicators for Portfolio Optimization A Simulation Based Approach written by Jiayang Yu and published by . This book was released on 2020 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio optimization and quantitative risk management have been extensively studied since the 1990s, and attracted even more attention after the financial crisis in 2008. Such a disastrous event required portfolio managers to better manage the risk and return trade-off when building their clients' portfolios. With that said, the advancement of machine-learning algorithms and computing resources helps portfolio managers explore rich information by incorporating the macro-economy conditions into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a simulation-based approach by fusing eleven macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). Then, we combine it with Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR) framework to derive an optimal portfolio comprised of six index funds. Empirical test on the achieved portfolio is conducted on an out-of-sample dataset utilizing a rolling-horizon approach. Finally, we compare its performance against the three benchmark portfolios over a twelve-year period (01/2007 - 12/2018). The results indicate that the proposed EFPM-based asset allocation strategy outperforms the three alternatives on many common metrics, including annualized return, 99% VaR, and Sharpe ratio.

Download or read book Evolutionary Multiobjective Optimization written by Ajith Abraham and published by Springer Science & Business Media. This book was released on 2005-09-05 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary Multi-Objective Optimization is an expanding field of research. This book brings a collection of papers with some of the most recent advances in this field. The topic and content is currently very fashionable and has immense potential for practical applications and includes contributions from leading researchers in the field. Assembled in a compelling and well-organised fashion, Evolutionary Computation Based Multi-Criteria Optimization will prove beneficial for both academic and industrial scientists and engineers engaged in research and development and application of evolutionary algorithm based MCO. Packed with must-find information, this book is the first to comprehensively and clearly address the issue of evolutionary computation based MCO, and is an essential read for any researcher or practitioner of the technique.

Download or read book Multi period portfolio optimization written by Heiko Siede and published by . This book was released on 2000 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book On the Topic of Portfolio Optimization written by Peter Joseph Mercurio and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation explores the use of information entropy as a risk measure for the purpose of investment portfolio optimization and selection. First, we present an improved method of applying entropy as a risk in portfolio optimization. A new family of portfolio optimization problems called the return-entropy portfolio optimization (REPO) is introduced that simplifies the computation of portfolio entropy using a combinatorial approach. REPO addresses five main practical concerns with traditional mean-variance portfolio optimization (MVPO), by using a mean-entropy objective function instead of the mean-variance objective function used in MVPO. REPO also simplifies the portfolio entropy calculation by utilizing combinatorial generating functions in the optimization objective function. Next, we extend the REPO approach to the optimization problem for assets with discrete distributed returns, such as those from a Bernoulli distribution like binary options. Under a discrete probability distribution, portfolios of binary options can be viewed as repeated short-term investments with an optimal buy/sell strategy or general betting strategy. Portfolio selection under this setting can be formulated as a new optimization problem called discrete entropic portfolio optimization (DEPO). DEPO creates optimal portfolios for discrete return assets based on expected growth rate and relative entropy. We show how a portfolio of binary options provides an ideal general setting for this kind of portfolio selection. Finally, we introduce a further generalized portfolio selection method called generalized entropic portfolio optimization (GEPO). GEPO extends DEPO to include intervals of continuous returns, with direct application to a wide range of options strategies. This lays the groundwork for an adaptable optimization framework that can accommodate a wealth of option portfolios. These options strategies exhibit mixed returns: a combination of discrete and continuous returns with performance best measured by portfolio growth rate, making entropic portfolio optimization an ideal method for option portfolio selection. GEPO provides the mathematical tools to select efficient option portfolios based on their growth rate and relative entropy.