Multi-period Portfolio Selection Model Research Articles

Composite time-consistent multi-period risk measure and its application in optimal portfolio selection

Through the composition of two real-valued functions, we propose a new class of multi-period risk measure which is time consistent. The new multi-period risk measure is monotonous and convex when the two real-valued functions satisfy monotonicity and convexity. Based on this generic framework, we construct a specific class of time-consistent multi-period risk measure by considering the lower partial moment between the realized wealth and the target wealth at individual periods. With the new multi-period risk measure as the objective function, we formulate a multi-period portfolio selection model by considering transaction costs at individual investment periods. Furthermore, this stochastic programming model is transformed into a deterministic programming problem using the scenario tree technology. Finally, we show through empirical tests and comparisons the rationality, practicality and efficiency of our new multi-period risk measure and the corresponding portfolio selection model.

Multi-period Mean-dynamic VaR Optimal Portfolio Selection: Model and Algorithm

This paper proposes the mean-dynamic VaR multi-period portfolio selection model with the transaction costs and the constraints on trade volumes. The Bat algorithm is applied to solve the multi-period mean-dynamic VaR model. Numerical results show that the Bat algorithm is effective and feasible to solve multi-period portfolio selection problems.

Dynamic Portfolio Selection Methods for Power Generation Assets

In this paper we start off by reviewing the literature on how to extend the mean-variance portfolio model to multi-stage portfolio problems. We then apply a multi-period portfolio selection model to power generation assets, which is based on a reallocation methodology with scenario tree. Two solution approaches are used: the multi-period rebalancing model and the global solution one. These approaches are contrasted with the efficient frontier obtained for a "buy-and-hold policy", thus helping to illustrate the effect of portfolio dynamization. The study covers all major electricity generation technologies in Germany and investigates the impact of offshore wind and solar power plants on existing power generation portfolios. We find that solar power technology has a positive impact on the efficiency of the portfolios, and the analysis underlines the advantages of using a multi-period rebalancing model for decision-making.

Multi-period Possibilistic Mean Semivariance Portfolio Selection with Cardinality Constraints and its Algorithm

In this paper, we consider a multi-period portfolio selection problem in a fuzzy investment environment, in which the return and risk of assets are characterized by possibilistic mean value and possibilistic semivariance, respectively. Based on the theories of possibility, a new multi-period possibislistic portfolio selection model is proposed, which contains risk control, transaction costs, borrowing constraints, threshold constraints and cardinality constraints. the proposed model can be transformed into a crisp nonlinear dynamic optimization problem by using fuzzy programming approach. Because of the transaction costs and cardinality constraints, the multi-period portfolio selection is a mix integer dynamic optimization problem with path dependence A forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, a comparison analysis of the different cardinality constraints is provided to illustrate the efficiency of the proposed approaches and the designed algorithm.

Multi-period investment decision problem based on time consistent generalized convex risk measure and extremum scenarios

Purpose – To help investors find an investment policy with strong competitiveness, the purpose of this paper is to construct a multi-period investment decision model with practicality and superior performance. Design/methodology/approach – The paper uses a suitable multi-period risk measure to construct a multi-period portfolio selection model, where target returns at intermediate periods and market frictions are taken into account simultaneously. An efficient scenario tree generation approach is proposed in order to transform the complex multi-period portfolio selection problem into a tractable one. Findings – Numerical results show the new scenario tree generation algorithms are stable and can further reduce the tree size. With the scenario tree generated by the new scenario tree generation approach, the optimal investment strategy obtained under the multi-period investment decision model has more superior performance and robustness than the corresponding optimal investment strategy obtained under the single period investment model or the multi-period investment model only paying attention to the terminal cash flow. Research limitations/implications – The new risk measure and multi-period investment decision models can stimulate readers to find even better models and to efficiently solve realistic multi-period portfolio selection problems. Practical implications – The empirical results show the superior performance and robustness of optimal investment strategy obtained with the new models. What's more important, the empirical analyses tell readers how different market frictions affect the performance of optimal portfolios, which can guide them to efficiently solve real multi-period investment decision problems in practice. Originality/value – The paper first derives the concrete structure of the time consistent generalized convex multi-period risk measure, then constructs a multi-period portfolio selection model based on the new multi-period risk measure, and proposes a new extremum scenario tree generation algorithm. The authors construct a realistic multi-period investment decision model. Furthermore, using the proposed scenario tree generation algorithm, the authors transform the established stochastic investment decision model into a deterministic optimization problem, which can provide optimal investment decisions with robustness and superior performance.

Discrete-Time Behavioral Portfolio Selection Under Prospect Theory

We formulate and study a general multi-period behavioral portfolio selection model under Kahneman and Tversky's prospect theory, featuring an incomplete market and an S-shaped utility function. We first discuss the ill-posedness issue under a multi-period framework and identify the conditions for the well-posedness under which infinitely leveraging an asset is not optimal for the investor. Moreover, we show that the well-posedness of the multi-period portfolio selection problem can be characterized in terms of an induced loss-aversion measure, which is an increasing function of time. Under the conditions for well-posedness, we solve the multi-period behavioral portfolio selection problem completely by deriving its semi-analytical optimal policy. In particular, we identify two cases: the case with one risky asset and the case with multiple risky assets that are jointly elliptically distributed, under which the optimal behavioral portfolio policy takes a piecewise linear feedback form. For the multiple risky assets case, we further demonstrate that the two-fund separation is still valid under the S-shaped utility. We also discuss the implications of our findings to the well documented phenomena of non-participation effect and horizon effect.

Dynamic strategies for fixed-income investment

The last 30 years have witnessed an enormous growth in fixed-income markets. How long-term fixed-income strategies should be implemented for the welfare of investors has become a major concern of bond managers. This study makes use of stochastic optimal control to formulate a multi-period portfolio selection model and implements it using backward recursion algorithm to find numerically the optimal allocation of wealth between long- and short-term bonds for an investor with power utility and an investment horizon of 10 years. By way of a technical manipulation, this study uses the fact that the long rate and the spread (difference between long rate and short rate) are uncorrelated to simplify model formulation and parameter estimation. The results show that an investor would increase his/her holding of short-term bond if his/her investment horizon becomes shorter or if he/she is more risk averse, or both.

An utilities based approach for multi-period dynamic portfolio selection

This paper proposed a multi-period dynamic optimal portfolio selection model. Assumptions were made to assure the strictness of reasoning. This Approach depicted the developments and changing of the real stock market and is an attempt to remedy some of the deficiencies of recent researches. The model is a standard form of quadratic programming. Furthermore, this paper presented a numerical example in real stock market.

Optimisation and quantitative investment management

This paper provides a brief survey of some of the key issues in building a successful quantitative equity portfolio construction platform, integrating a data warehouse, a rebalancing engine, a back-testing engine as well as an attribution methodology. Optimisation models and software are central elements of such a platform. They serve as sophisticated tools for transferring the excess return ideas generated through research and testing into portfolios that best represent these ideas. In addition to the standard mean-variance optimisation models that are adjusted for transaction costs and taxes, advanced topics such as multi-period portfolio selection models and robust optimisation approaches are discussed.

The Optimal Portfolio Revision Policy

In a dynamic setting, portfolio management is a portfolio revision process through which an investor revises his portfolio periodically to adapt to changing conditions, either endogenous or exogenous to the portfolio. For example, an investor accepting an intrinsic-value hypothesis may find it profitable to revise his portfolio when his expectations in returns and the riskiness of some earning assets deviate significantly from those of the market. Likewise, an investor accepting the random-walk hypothesis may find it profitable to revise his portfolio because of an exogenously determined cash demand (either inflow or outflow) or changes in portfolio value and proportions due to change in prices of earning assets and cash earnings received. The problem is of considerable importance because investment decisions are usually made starting with a portfolio rather than cash, and consequently some assets must be liquidated to permit investment in others. Despite its importance, the problem has received only limited attention in the literature. This paper seeks to address this problem. After briefly reviewing single-period and multiperiod portfolio selection models, we shall examine a revision procedure proposed by Smith' and illustrate its inadequacies. In Section II, an alternative single-period portfolio revision model is formulated and compared with Smith's model. The model takes into consideration the expected transfer costs to be incurred in the transition. In Section III, the model is extended to the multiperiod case and compared with the Mossin's dynamic portfolio selection model. Section IV summarizes the findings and indicates areas for further research.