Download or read book Portfolio Optimization in a Downside Risk Framework written by Lars Huelin and published by LAP Lambert Academic Publishing. This book was released on 2011-04 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present study examines how downside risk measures perform in an investment management context compared to variance or standard deviation. To our knowledge, this paper is the first to include several acknowledged downside risk measures in a thorough analysis where their different properties are compared with those of variance Risk is an essential factor to consider when investing in the capital markets. The question of how one should define and manage risk is one that has gained a lot of attention and remains a popular topic in both the academic and professional world. This study considers six different downside risk measures and tests their relationship with the cross-section of returns as well as their performance in portfolio optimization compared to variance. The first part of the analysis suggests that the conditional drawdown-at-risk explains the cross-section of returns the best across methodologies and data frequency. Conditional valueat- risk explains the daily returns the best but the worst in monthly returns. Variance, together with semivariance, perform average in both data frequencies. The second part of the analysis concludes that conditional value-at-risk and conditional drawdown-at-risk are the two superior risk measures whereas semivariance is the worst performing risk measure - mainly caused by the poor performance during bull markets. Again, variance performs average compared to the downside risk measures in most aspects of this analysis. Overall, this thesis shows that the choice of risk measure has a significant effect on the portfolio optimization process. The analysis suggests that some downside risk measures outperform variance while others fail to do so. This suggest that downside risk can be a better tool in investment management than variance.

Download or read book Portfolio Optimization of Hedge Funds in a Downside Risk Framework Optimisation de Portefeuille des Hedge Funds dans le cadre du Downside Risk written by Chokri Mamoghli and published by . This book was released on 2008 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: This article examines the portfolio optimization of hedge funds in the downside risk framework in order to take into account the asymmetry of returns and the behavior of investors towards the risk which are not captured by the mean-variance model. By using the Credit Suisse/Tremont Hedge Fund database, the results showed that the downside risk measures have an impact on the composition of optimal portfolios and on the efficient frontier. The results also showed that the mean-variance model overestimates the risk because of the use of the variance as a risk measure, but the mean- semivariance model of Harlow (1991) underestimates the risk because of the cosemivariances measures used in this model. Likewise, the results obtained proved that the new mean-semivariance model provides a better optimization of hedge funds portfolios because it makes it possible to capture the non-normality of hedge funds strategies and the risk perception of investors which are not taken into account by the mean-variance model and also makes it possible to overcome the problem of inequality of the cosemivariances measures in the mean-semivariance model of Harlow (1991).

Download or read book Downside Risk Optimization in Securitized Real Estate Markets written by Tim Alexander Kroencke and published by . This book was released on 2018 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization of international securitized real estate portfolios has been a key topic for several decades. However, most previous analysis has focused on regional diversification by applying the traditional mean-variance (MV) framework suggested by Markowitz (1952) even if the limitations of this approach are well-known. Thus, we focus on a more suitable and appealing downside risk (DR) framework suggested by Estrada (2008), which applies a similar optimization algorithm as the MV framework. The analysis covers the eight largest securitized real estate markets from January 1990 to December 2009 and thus captures a more global perspective. The main findings are as follows: first, the return distributions are non-normally distributed and negatively skewed. Second, optimal portfolio weights differ substantially between the MV and DR approach. Third, portfolio weights are shifted from the U.S. and Australian market to the Dutch and the French market when applying the DR framework instead of the MV framework. Fourth, the dominance of the DR framework is well-documented by comparing out-of-sample performance. The empirical results are remarkable and emphasize the practical merit of the presented DR framework for investors and portfolio managers.

Download or read book Dynamic Asset Allocation and Down side Risk Aversion written by Arjan Berkelaar and published by . This book was released on 2000 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Sortino Framework for Constructing Portfolios written by Frank A. Sortino and published by Elsevier. This book was released on 2009-11-27 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most common way of constructing portfolios is to use traditional asset allocation strategies, which match the client’s risk appetite to a weighted allocation strategy of fixed income, equities, and other types of assets. This method focuses on how the money is allocated, rather than on future returns.The Sortino method presents an innovative change from this traditional approach. Rather than using the client’s risk as the main factor, this method uses the client’s desired return. Only book to describe the Sortino method and Desired Target ReturnTM in a way that enables portfolio managers to adopt the method Software to implement the portfolio construction method is included free of charge to book buyers on a password protected Elsevier website. Book buyers can use the software to construct portfolios using this method right away, in real time. They can also load in their current portfolios and measure them against these measures The Sortino method has been tested over 20 years at the Pension Research Institute. Portfolio managers can be confident of the success of the method, even returns in the economic crisis, in which the method has still beaten all S&P benchmarks

Download or read book Downside Risk Portfolio Optimization Using Martingale Analysis written by Matthias Frank and published by . This book was released on 2006 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Portfolio Theory and Management written by H. Kent Baker and published by Oxford University Press, USA. This book was released on 2013-03-07 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio Theory and Management examines the foundations of portfolio management with the contributions of financial pioneers up to the latest trends. The book discusses portfolio theory and management both before and after the 2007-2008 financial crisis. It takes a global focus by highlighting cross-country differences and practices.

Download or read book Asset Allocation in a Downside Risk Framework written by Anna Maria Fiori and published by . This book was released on 2000 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Volatility Vs Downside Risk written by Diana Barro and published by . This book was released on 2014 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a consequence of recent market conditions an increasing number of investors are realizing the importance of controlling tail risk to reduce drawdowns thus increasing possibilities of achieving long-term objectives. Recently, so called volatility control strategies and volatility target approaches to investment have gained a lot of interest as strategies able to mitigate tail risk and produce better risk-adjusted returns. Essentially these are rule-based backward looking strategies in which no optimization is considered. In this contribution we focus on the role of volatility in downside risk reduction and, in particular, in tail risk reduction. The first contribution of our paper is to provide a viable way to integrate a target volatility approach, into a multiperiod portfolio optimization model, through the introduction of a local volatility control approach. Our optimized volatility control is contrasted with existing rule-based target volatility strategies, in an out-of sample simulation on real data, to assess the improvement that can be obtained from the optimization process.A second contribution of this work is to study the interaction between volatility control and downside risk control. We show that combining the two tools we can enhance the possibility of achieving the desired performance objectives and, simultaneously, we reduce the cost of hedging.The multiperiod portfolio optimization problem is formulated in a stochastic programming framework that provides the necessary flexibility for dealing with different constraints and multiple sources of risk.

Download or read book Machine Learning Applications for Accounting Disclosure and Fraud Detection written by Papadakis, Stylianos and published by IGI Global. This book was released on 2020-10-02 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: The prediction of the valuation of the “quality” of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of machine learning methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the “actual” financial performance of the business activity. Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies machine learning models to identify “quality” characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students.

Download or read book Managing Downside Risk for Portfolio Optimization written by CA. (Dr.) Hemlata Chelawat and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, stock markets around the world have experienced unprecedented volatility. The steep fall in market values in most markets has adversely affected the investor interest. As a consequence, there is flight of investor capital away from the market. Use of traditional portfolio construction strategies to construct optimal portfolios, which maximize returns and minimize risk, is extremely difficult in such a volatile environment. This has led to a search for portfolio construction using new, innovative asset allocation and selection strategies. Construction of portfolio using the Martin Ratio or Ulcer Performance Index (UPI) is one such strategy designed specifically to address investor's stress of holding a stock by reducing downside volatility and providing investors with higher than benchmark returns. It measures the depth and duration of drawdown in prices from previous high. The purpose of this study is to use the Martin's Ratio to construct a model portfolio from amongst CNX Nifty 50 stocks and to compare its returns with the broad market returns. The study shows that the model portfolio constructed comprises minimum downward volatility stocks and yields an annual return higher than the market return. Hence, it is a very significant tool for identification of stocks for portfolio construction and optimisation.

Download or read book Mean Variance Analysis in Portfolio Choice and Capital Markets written by Harry M. Markowitz and published by John Wiley & Sons. This book was released on 2000-02-15 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 1952, Harry Markowitz published "Portfolio Selection," a paper which revolutionized modern investment theory and practice. The paper proposed that, in selecting investments, the investor should consider both expected return and variability of return on the portfolio as a whole. Portfolios that minimized variance for a given expected return were demonstrated to be the most efficient. Markowitz formulated the full solution of the general mean-variance efficient set problem in 1956 and presented it in the appendix to his 1959 book, Portfolio Selection. Though certain special cases of the general model have become widely known, both in academia and among managers of large institutional portfolios, the characteristics of the general solution were not presented in finance books for students at any level. And although the results of the general solution are used in a few advanced portfolio optimization programs, the solution to the general problem should not be seen merely as a computing procedure. It is a body of propositions and formulas concerning the shapes and properties of mean-variance efficient sets with implications for financial theory and practice beyond those of widely known cases. The purpose of the present book, originally published in 1987, is to present a comprehensive and accessible account of the general mean-variance portfolio analysis, and to illustrate its usefulness in the practice of portfolio management and the theory of capital markets. The portfolio selection program in Part IV of the 1987 edition has been updated and contains exercises and solutions.

Download or read book Extending the MAD Portfolio Optimization Model to Incorporate Downside Risk Aversion written by Wojtek Michalowski and published by . This book was released on 2000 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Portfolio Optimization in a Downside Risk Framework Optimisation De Portefeuille Dans Le Cadre Du Downside Risk written by Chokri Mamoghli and published by . This book was released on 2008 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: We showed in this article that the new mean-semivariance model for portfolio optimization makes it possible to overcome the drawbacks of the mean-variance model concerning the asymmetry of returns and the risk perception of investors. We also showed that this new mean-semivariance model permits to surmount the problem of inequality of the cosemivariances measures which occurs in the mean-semivariance model of Harlow (1991). The empirical investigation based on Morgan Stanley Capital Indices MSCI database of emerging markets demonstrates that the mean-variance model overestimates the risk because of the use of the variance as a risk measure, but the mean-semivariance model of Harlow (1991) underestimates the risk due to the cosemivariances measures used by this model. The results obtained also prove that the new mean-semivariance model provides a more correct portfolio optimization because it makes it possible to overcome all these problems.

Download or read book Risk and Robust Optimization written by David Benjamin Brown (Ph. D.) and published by . This book was released on 2006 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: (Cont.) Furthermore, we illustrate the performance of these risk measures on a real-world portfolio optimization application and show promising results that our methodology can, in some cases, yield significant improvements in downside risk protection at little or no expense in expected performance over traditional methods. While we develop this framework for tile case of linear optimization under uncertainty, we show how to extend the results to optimization over more general cones. Moreover, our methodology is scenario-based, and(we prove a new rate of convergence result on a specific class of convex risk measures. Finally, we consider a multi-stage problem under uncertainty, specifically optimization of quadratic functions over un-certain linear systems. Although the theory of risk measures is still undeveloped with respect to dynamic optimization problems. we show that a set-based model of uncertainty yields a tractable approach to this problem in the presence of constraints. Moreover, we are able to derive a near-closed form solution for this approach and prove new probability guarantees on its resulting performance.

Download or read book Portfolio Risk Optimization by Fuzzy Approaches written by Thanh Thi Nguyen and published by . This book was released on 2013 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the complexity and uncertainty in real world portfolio management, investors might be reluctant and sometimes unable to provide precise judgements regarding stock performance. In this context, analysts have long advocated use of fuzzy mathematics so that uncertainties and lack of precision can be acknowledged. This research therefore explores the applications of fuzzy sets in particular, or fuzzy logic in general for representing vague and imprecise financial data for portfolio risk optimization. Asset returns are uncertain and changeable over time so we model asset returns as fuzzy random variables and propose portfolio optimization models. Using fuzzy random variables, we introduce a new concept of financial risk, and the fuzzy Sharpe ratio contributing an important advancement in portfolio selection in the fuzzy environment. Two solution methods using a fuzzy approach and a genetic algorithm are applied to the proposed models. The proposed approach exhibits advantages over the so-called standard mean-variance optimization (MVO), throughout experimental results. The non-Gaussian distribution of asset returns has long been recognized, and the conventional MVO has been criticized as inadequate. Hence utilizing higher moments than variance, i.e. skewness, kurtosis soon emerged in portfolio selection. This research investigates the importance of higher moments in portfolio optimization through deploying fuzzy approaches. Marginal impacts of stocks on portfolio return and higher moment risks, are modelled by fuzzy numbers. The fuzzy models are constructed to optimize not only portfolio return and normal variance risk but also the portfolio higher moment risks. From the stock marginal impact modelling, two fuzzy approaches are used to derive optimal portfolio allocations. The first approach applies the constrained fuzzy analytic hierarchy process, whereas the second approach uses the fuzzy linear programming method. The efficiency of both approaches shows advantages of the proposed fuzzy models in portfolio selection. Going beyond the normal variance and higher moment risks, investors also should take into account downside risk measures. The downside risks are inspired by the principle of safety first in portfolio selection. The principle states that an investor would prefer the investment with the smallest probability of going below the target return. A fuzzy integrated framework is proposed accounting for portfolio return and six risk criteria including normal risk (volatility), asymmetric risk (skewness), "fat-tail" risk (kurtosis) and downside risks, i.e. semi-variance, modified Value-at-Risk, and modified Expected Shortfall. Fuzzy goals of portfolio's return and risks are constructed by bootstrapping, and kernel smoothing density estimate. A preselection process dealing with large datasets is also adopted to eliminate low diversification potential stocks before running the optimization model. Various investors' risk preference schemes are implemented with both national and international experimental datasets. Results reported demonstrate the advantages of the proposed fuzzy framework compared to a conventional higher moment portfolio optimization model. The conclusion is that fuzzy modelling is efficient and competent in various portfolio selection formulations when uncertainty and vagueness are deemed present. When appropriately utilized, fuzzy approaches can bring superior investment outcomes compared to conventional non-fuzzy models prevalent in the literature.

Download or read book Linear and Mixed Integer Programming for Portfolio Optimization written by Renata Mansini and published by Springer. This book was released on 2015-06-10 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.