A Stochastic Programming Approach for Multi-Period Portfolio Optimization

presented in this paper. The basic model involves Multi-Period decisions (portfolio optimization) and deals with the usual uncertainty of investment returns and future liabilities. Therefore, is it well suited to a stochastic programming approach. We consider the problem of rebalancing policy to accomplish some investment’s criteria. Transaction costs have also been a subject of concern in this paper. In particular, a large amount of transactions usually make asset price move in an unfavorable direction. Therefore, the first problem neglects transactions cost while the second does not

A Stochastic Programming Approach For Multi-Period Portfolio Optimization With Transaction Costs

This paper uses stochastic programming to solve multi-period investment problems. We combine the feature of asset return predictability with practically relevant constraints arising in a multi-period investment context. The objective is to maximize the expected utility of the returns the periods to balance the liabilities. Asset returns and state variables follow a first-order vector auto-regression and the associated uncertainty is described by discrete scenario trees. To deal with the long time intervals involved in multi-period problems, we consider short-term decisions, and incorporate a solution for the long, subsequent steady-state period to account for end effects

Portfolio Optimization in Post Financial Crisis of 2008-2009 in the Mongolian Stock Exchange

In this study, we present the Mongolian stock market’s performance post phenomenal financial crisis of 2008-2009, opportunities to invest and the risks problems. For analysis of the study, we used financial portfolio optimization models with restricted structure, mathematical statistic methods and financial methods. First, we considered about portfolio optimization in the Mongolian Stock Exchange using Markowitz’s modern portfolio theory and Telser’s safety first model. We used MSE weekly trading data chosen 50 most traded stocks out of 237 stocks listed at the MSE between 2009 and 2013. We generated 50 weeks mean-variance portfolio and safety first portfolio for 2014 and discussed. We considered weekly investment in the MSE using mean-variance portfolio and safety first portfolio. The mean-variance portfolio has the best performance of weekly portfolio return with average weekly return and cumulative return. We found stable portfolio against investing risk and did back-test the result. For prospect investors in the MSE, we suggest invest and earn high return in the MSE

A Comparative Study of Multi-Objective Multi-Period Portfolio Optimization Models in a Fuzzy Credibility Environment Using Different Risk Measures

The purpose of the present research is to compare portfolio optimization models in a fuzzy credibility environment, aimed for end-of-period wealth maximization and risk minimization. The investor’s risk was measured using the Value at Risk (VaR), Average Value at Risk (AVaR) and semi Entropy. In order to get closer to the real world investment model, while allowing for transaction costs and investing part of wealth in risk-free assets, in addition to the cardinal constraints, other constraints including the minimum and maximum amount of wealth assigned to each asset, and the minimum and maximum number of stocks present in portfolio were applied. The results of the multi-period models running by MOPSO algorithm indicated for the models Mean-AVaR, Mean-Semi Entropy, and Mean-VaR, respectively, performed better, in terms of Sharp and Treynor measures

Asset allocation and portfolio optimization problems with metaheuristics: a literature survey

The main objective of Markowitz work is seeking optimal allocation of wealth on a defined number of assets while minimizing risk and maximizing returns of expected portfolio. At the beginning, proposed models in this issue are resolved basing on quadratic programming. Unfortunately, the real state of financial markets makes these problems too complex. Metaheuristics are stochastic methods which aim to solve a large panel of NPhard problems without intervention of users. These methods are inspired from analogies with other fields such as physics, genetics, or ethologic. Already various Metaheuristics approaches have been proposed to solve asset allocation and portfolio optimization problems. In a first time, we survey some approaches on the topic, by categorizing them, describing results and involved techniques. Second part of this paper aims providing a good guide to the application of Metaheuristics to portfolio optimization and asset allocation problems

Asset allocation and portfolio optimization problems with metaheuristics: a literature survey

The main objective of Markowitz work is seeking optimal allocation of wealth on a defined number of assets while minimizing risk and maximizing returns of expected portfolio. At the beginning, proposed models in this issue are resolved basing on quadratic programming. Unfortunately, the real state of financial markets makes these problems too complex. Metaheuristics are stochastic methods which aim to solve a large panel of NPhard problems without intervention of users. These methods are inspired from analogies with other fields such as physics, genetics, or ethologic. Already various Metaheuristics approaches have been proposed to solve asset allocation and portfolio optimization problems. In a first time, we survey some approaches on the topic, by categorizing them, describing results and involved techniques. Second part of this paper aims providing a good guide to the application of Metaheuristics to portfolio optimization and asset allocation problems