Download or read book Multiperiod Portfolio Selection with Risk Management Using Gene Expression Programming and Stochastic Programming written by 陳紘昕 and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stochastic Dynamic Programming Methods for the Portfolio Selection Problem written by Dimitrios Karamanis and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we study the portfolio selection problem with multiple risky assets, linear transaction costs and a risk measure in a multi-period setting. In particular, we formulate the multi-period portfolio selection problem as a dynamic program and to solve it we construct approximate dynamic programming (ADP) algorithms, where we include Conditional-Value-at-Risk (CVaR) as a measure of risk, for different separable functional approximations of the value functions. We begin with the simple linear approximation which does not capture the nature of the portfolio selection problem since it ignores risk and leads to portfolios of only one asset. To improve it, we impose upper bound constraints on the holdings of the assets and we notice that we have more diversified portfolios. Then, we implement a piecewise linear approximation, for which we construct an update rule for the slopes of the approximate value functions that preserves concavity as well as the number of slopes. Unlike the simple linear approximation, in the piecewise linear approximation we notice that risk affects the composition of the selected portfolios. Further, unlike the linear approximation with upper bounds, here wealth flows naturally from one asset to another leading to diversified portfolios without us needing to impose any additional constraints on how much we can hold in each asset. For comparison, we consider existing portfolio selection methods, both myopic ones such as the equally weighted and a single-period portfolio models, and multi-period ones such as multistage stochastic programming. We perform extensive simulations using real-world equity data to evaluate the performance of all methods and compare all methods to a market Index. Computational results show that the piecewise linear ADP algorithm significantly outperforms the other methods as well as the market and runs in reasonable computational times. Comparative results of all methods are provided and some interesting conclusions are drawn especially when it comes to comparing the piecewise linear ADP algorithms with multistage stochastic programming.
Download or read book A Stochastic Programming Approach to Portfolio Selection with a New Definition of Risk written by Jill Anna Smith and published by . This book was released on 2010 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Stochastic Convergence Model for Portfolio Selection written by Amy Puelz and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio selection techniques must provide decision-makers with a dynamic model framework that incorporates realistic assumptions regarding financial markets, risk preferences and required portfolio characteristics. Unfortunately, multi-stage stochastic programming (SP) models for portfolio selection very quickly become intractable as assumptions are relaxed and uncertainty is introduced. In this paper I present an alternative model framework for portfolio selection, stochastic convergence (SC), that systematically incorporates uncertainty under a realistic assumption set. The optimal portfolio is derived through an iterative procedure where portfolio plans are evaluated under many possible future scenarios then revised until the model converges to the optimal plan. This approach allows for scenario analysis over all stochastic components, requires no limitation on the structural form of the objective or constraints, and permits evaluation over any length planning horizon while maintaining model tractability by aggregating the scenario tree at each stage in the solution process. In simulated tests, the SC model, with scenario aggregation, generated portfolios exhibiting performance similar to those generated using the SP model form with no aggregation. Empirical tests using historical fund returns show that a multi-period SC decision strategy outperforms various benchmark strategies over a long-term test horizon.
Download or read book Stochastic Programming Models and Methods for Portfolio Optimization and Risk Management written by Rudabeh Meskarian and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multi period Scenario Generation to Support Portfolio Optimization written by Erhan Deniz and published by . This book was released on 2009 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Programming (SP) models are widely used for real life problems involving uncertainty. The random nature of problem parameters is modeled via discrete scenarios, which makes the scenario generation process very critical to the success of the overall approach. In this study we consider a portfolio management problem and propose two scenario generation algorithms and a SP model to support investment decisions. The main objective of the scenario generation algorithms is to infer representative probability values to be assigned to the scenario realizations sampled from historical data. The first algorithm assigns the probabilities by using similarity scores, assigning higher probabilities to the scenarios with data paths that are relatively similar to historical paths, where similarity scores are computed by means of distance measures. We first implement this approach using the weighted Euclidean distance (WED). We also propose a new distance measure to obtain similarity scores as an alternative to WED. The second scenario generation algorithm is based on the combination of moment-matching technique and the Exponential Generalized Auto-Regressive Conditional Heteroskedasticity (EGARCH) model. Scenario probabilities are assigned such that the first four moments of the sampled returns are fit to target moments through a linear programming model, where the second target moments are set to be conditional on the past scenarios on the scenario tree using the EGARCH model. An additional set of constraints are proposed to increase robustness. The generated scenarios become input to the SP model to restructure the existing portfolio such that the expected final wealth is maximized and the risk exposure is controlled through constraining Conditional Value-at-Risk at each decision epoch on the scenario tree. We finally propose a generic approach to reduce potential losses and implement it on a logistic regression framework.
Download or read book Stochastic Programming in Portfolio Selection written by R. J. Peters and published by . This book was released on 1979 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Portfolio Optimization and Risk Management via Stochastic Programming written by Jitaka Dupacova and published by . This book was released on 2009-06 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stochastic Programming Models for Dedicated Portfolio Selection written by Jeremy F. Shapiro and published by . This book was released on 1986 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimal Portfolios written by Ralf Korn and published by World Scientific. This book was released on 1997 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of the book is the construction of optimal investment strategies in a security market model where the prices follow diffusion processes. It begins by presenting the complete Black-Scholes type model and then moves on to incomplete models and models including constraints and transaction costs. The models and methods presented will include the stochastic control method of Merton, the martingale method of Cox-Huang and Karatzas et al., the log optimal method of Cover and Jamshidian, the value-preserving model of Hellwig etc.
Download or read book Portfolio Optimization Using Stochastic Programming with Market Trend Forecast written by Yutian Yang and published by . This book was released on 2014 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report discusses a multi-stage stochastic programming model that maximizes expected ending time profit assuming investors can forecast a bull or bear market trend. If an investor can always predict the market trend correctly and pick the optimal stochastic strategy that matches the real market trend, intuitively his return will beat the market performance. For investors with different levels of prediction accuracy, our analytical results support their decision of selecting the highest return strategy. Real stock prices of 154 stocks on 73 trading days are collected. The computational results verify that accurate prediction helps to exceed market return while portfolio profit drops if investors partially predict or forecast incorrectly part of the time. A sensitivity analysis shows how risk control requirements affect the investor's decision on selecting stochastic strategies under the same prediction accuracy.
Download or read book Stochastic Programming in Portfolio Selection written by and published by . This book was released on 1978 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Stochastic Optimization Model for Multi currency Bond Portfolio Management written by Tuula Hakala and published by . This book was released on 1996 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimal Dynamic Portfolio Selection written by Duan Li and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The mean-variance formulation by Markowitz in the 1950s paved a foundation for modern portfolio selection analysis in a single period. This paper considers an analytical optimal solution to the mean-variance formulation in multiperiod portfolio selection. Specifically, analytical optimal portfolio policy and analytical expression of the mean-variance efficient frontier are derived in this paper for the multiperiod mean-variance formulation. An efficient algorithm is also proposed for finding an optimal portfolio policy to maximize a utility function of the expected value and the variance of the terminal wealth.
Download or read book Multi Objective Linear Programming in Portfolio Selection written by Gayatri Biswal and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio theory originally proposed by Markowitz is based on the assumption that the utility of an investor is a function of two factors, viz., mean and variance (or standard deviation) of return. However, the single index model of Sharpe is a statistical representation of return generating process that expresses return on stock in the form of a regression equation. Literature review on investment portfolio management shows that Sharpe's coefficient is the most commonly used performance measure in the determination of optimal portfolio. Sharpe's model is a linear programming model of the problem considering as the measure of risk. The present paper, building on the above model, proposes a multi-objective linear programming portfolio selection model that ensures a nondominated solution on the efficient frontier based on the outputs of the single index model. Taking Dow Jones Industrial Average (DJIA) as the market index and considering monthly indices along with the monthly prices of 28 securities for the period from March 1999 to March 2015, this model solves a practical portfolio selection problem in a multi-objective framework. The proposed model also shows its superiority over Sharpe's single index model.
Download or read book A Multi period Stochastic Programming Approach to Integrated Asset and Liability Management of Investment Products with Guarantees written by Helgard Raubenheimer and published by . This book was released on 2009 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Portfolio Selection Under Discrete Choice Constraints written by Stephen James Stoyan and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last year or so, we have witnessed the global effects and repercussions related to the field of finance. Supposed blue chip stocks and well-established companies have folded and filed for bankruptcy, an event that might have thought to been absurd two years ago. In addition, finance and investment science has grown over the past few decades to include a plethora of investment options and regulations. Now more than ever, developments in the field are carefully examined and researched by potential investors. This thesis involves an investigation and quantitative analysis of key money management problems. The primary area of interest is Portfolio Selection, where we develop advanced financial models that are designed for investment problems of the 21 st century. Portfolio selection is the process involved in making large investment decisions to generate a collection of assets. Over the years the selection process has evolved dramatically. Current portfolio problems involve a complex, yet realistic set of managing constraints that are coupled to general historic risk and return models. We identify three well-known portfolio problems and add an array of practical managing constraints that form three different types of Mixed-Integer Programs. The product is advanced mathematical models related to risk-return portfolios, index tracking portfolios, and an integrated stock-bond portfolio selection model. The numerous sources of uncertainty are captured in a Stochastic Programming framework, and Goal Programming techniques are used to facilitate various portfolio goals. The designs require the consideration of modelling elements and variables with respect to problem solvability. We minimize trade-offs in modelling and solvability issues found in the literature by developing problem specific algorithms. The algorithms are tailored to each portfolio design and involve decompositions and heuristics that improve solution speed and quality. The result is the generation of portfolios that have intriguing financial outcomes and perform well with respect to the market. Portfolio selection is as dynamic and complex as the recent economic situation. In this thesis we present and further develop the mathematical concepts related to portfolio construction. We investigate the key financial problems mentioned above, and through quantitative financial modelling and computational implementations we introduce current approaches and advancements in field of Portfolio Optimization.
