Multi-period Mean-variance Portfolio Selection Research Articles

Multi-period mean–variance portfolio selection with real constraints based on machine learning

Multi-period mean–variance portfolio selection with real constraints based on machine learning

Dynamic mean-variance portfolio selection under factor models

Utilizing insights from financial literature and empirical financial data, we introduce a comprehensive system of factor models designed to capture both return and risk dynamics. Our focus extends to addressing the multi-period mean-variance portfolio selection challenge within the framework of these proposed factor models. Through rigorous analysis, we formulate a semi-analytical optimal portfolio policy, characterized by a linear relationship with the current wealth level. The coefficients of this optimal policy are intricately linked to a specific stochastic process known as the future investment opportunity (FIO), reflecting the investor's anticipation of future investment prospects. Furthermore, empirical examination within the U.S. market context underscores the efficacy of our approach. By incorporating the factor models for return and risk, our optimal portfolio policy exhibits superior out-of-sample Sharpe ratio compared to benchmark policies.

Survey on Multi-period Mean–Variance Portfolio Selection Model

Survey on Multi-period Mean–Variance Portfolio Selection Model

A Nash-Type Fictitious Game Framework to Time-Inconsistent Stochastic Control Problems

In this paper, a Nash-type fictitious game framework is introduced to handle a time-inconsistent linear-quadratic optimal control. The Nash-type game in this framework is called fictitious as it is between the decision maker (called real player) and an auxiliary control variable (called fictitious player) with the real player and fictitious player looking for time-consistent policy and precomitted optimal policy, respectively. Namely, the fictitious-game framework is actually an auxiliary-variable-based mechanism where the fictitious player is our particular design. Noting that the real player's cost functional is revised in accordance with that of fictitious player, the equilibrium policy of real player is called an open-loop self-coordination control of original linear-quadratic problem. As a generalization, a time-inconsistent nonzero-sum stochastic linear-quadratic dynamic game is investigated, where one player is to look for precommitted optimal policy and the other player is to search time-consistent policy. Necessary and sufficient conditions are presented to ensure the existence of open-loop equilibrium of the nonzero-sum game, which resort to a set of Riccati-like equations and linear equations. By applying the developed theory of nonzero-sum game, open-loop self-coordination control of the linear-quadratic optimal control is fully characterized, and multi-period mean-variance portfolio selection is also investigated. Finally, numerical simulations are presented, which show the efficiency of the proposed fictitious-game framework.

Bayesian Filtering for Multi-period Mean–Variance Portfolio Selection

For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model. Usually, dynamic programming techniques are applied to optimize the portfolio for the multi-period model. However, this assumes a known distribution for the parameters of the financial time series. We consider the situation where the distribution of parameters is unknown and is estimated directly from the dynamically arriving data. We implement the Bayesian filtering method through dynamic linear models to sequentially update the parameters. We also acknowledge the uncertain investment lifetime to make the model more adaptive to the market conditions. These updated parameters are put into the dynamic mean–variance problem to arrive at optimal efficient portfolios. Implementing this model to the S&P500 illustrates that the data strongly favor the Bayesian updating and is practically implementable.

Multi-period mean-variance portfolio selection in a regime-switching market with a bankruptcy state and a state-dependent uncertain exit-time

Multi-period mean-variance portfolio selection in a regime-switching market with a bankruptcy state and a state-dependent uncertain exit-time

Multi-period mean-variance portfolio selection with practical constraints using heuristic genetic algorithms

Since Markowitz proposed the mean-variance (MV) formulation in 1952, it has been used to configure various portfolio selection problems. However Markowitz's solution is only for a single period. Multi-period portfolio selection problems have been studied for a long time but most solutions depend on various forms of utility function, which are unfamiliar to general investors. Some works have formulated the problems as MV models and solved them analytically in closed form subject to certain assumptions. Unlike analytical solutions, genetic algorithms (GA) are more flexible because they can solve problems without restrictive assumptions. The purpose of this paper is to formulate multi-period portfolio selection problems as MV models and solve them by GA. To illustrate the generality of our algorithm, we implement a program by Microsoft Visual Studio to solve a multi-period portfolio selection problem for which there exists no general analytical solution.

Multi-period mean-variance portfolio selection with practical constraints using heuristic genetic algorithms

Since Markowitz proposed the mean-variance (MV) formulation in 1952, it has been used to configure various portfolio selection problems. However Markowitz's solution is only for a single period. Multi-period portfolio selection problems have been studied for a long time but most solutions depend on various forms of utility function, which are unfamiliar to general investors. Some works have formulated the problems as MV models and solved them analytically in closed form subject to certain assumptions. Unlike analytical solutions, genetic algorithms (GA) are more flexible because they can solve problems without restrictive assumptions. The purpose of this paper is to formulate multi-period portfolio selection problems as MV models and solve them by GA. To illustrate the generality of our algorithm, we implement a program by Microsoft Visual Studio to solve a multi-period portfolio selection problem for which there exists no general analytical solution.

Multi-period mean-variance portfolio selection in a regime-switching market with a bankruptcy state and a state-dependent uncertain exit-time

In this paper, we study optimal multi-period portfolio selection problem with uncertain exit-time under mean-variance criterion in a Markovian regime-switching market. The market state space contains an absorbing state which represents the bankruptcy state. It is assumed that all random key parameters, i.e., asset returns, the recovery rate, and the exit-time depend on the current market state. Three common mean-variance formulations are considered, i.e., minimum variance formulation, maximum expected return formulation and the trade-off formulation. First, the problem with an uncertain exit-time is reformulated as a problem with a certain exit-time. Then, by applying the Lagrange duality method and the dynamic programming approach, the optimal multi-period portfolio strategies and the efficient frontier are derived in a closed form. Moreover, the conditions under which the aforementioned three problems are (mutually) equivalent are given. A numerical example is provided to illustrate the results.

Equilibrium Solutions of Multiperiod Mean-Variance Portfolio Selection

This is a companion paper of [Mixed equilibrium solution of time-inconsistent stochastic linear-quadratic problem, SIAM J. Control Optim., vol. 57, no. 1, 533-569, 2019], where general theory has been established to characterize the open-loop equilibrium control, feedback equilibrium strategy and mixed equilibrium solution for a time-inconsistent stochastic linear-quadratic problem. This note is, on the one hand, to test the developed theory of that paper and on the other hand to push the solvability of multiperiod mean-variance portfolio selection. A nondegenerate assumption, which is popular in the existing literature about multiperiod mean-variance portfolio selection, has been removed in this note; and neat conditions have been obtained to characterize the existence of equilibrium solutions.

Mean-variance portfolio selection with an uncertain exit-time in a regime-switching market

In this paper, we deal with multi-period mean-variance portfolio selection problems with an exogenous uncertain exit-time in a regime-switching market. The market is modelled by a non-homogeneous Markov chain in which the random returns of assets depend on the states of the market and investment time periods. Applying the Lagrange duality method, we derive explicit closed-form expressions for the optimal investment strategies and the efficient frontier. Also, we show that some known results in the literature can be obtained as special cases of our results. A numerical example is provided to illustrate the results.

Multi-Period Mean-Variance Portfolio Selection with State-Dependent Exit Probability and Bankruptcy State

Based on the mean-variance portfolio selection under multi-period criterion, this paper focuses on the study of the uncertain time horizon and the regime-switching market including the bankruptcy state, where the conditional distribution of exit time is followed by the market state. When the market enters the bankruptcy state, investors are assumed to get back δ part of the wealth from the bankrupt company, where δ refers to the retrieval rate. By introducing the Lagrange multiplier λ, we create an innovative expression for the wealth process and the iterative representation of the value function to obtain the analytical expression of the optimal strategy and the corresponding efficient frontier. Besides, some special cases and numerical examples are presented to demonstrate the effects of state-dependent exit probability and bankruptcy state on the investment strategy.

A symmetric Gauss-Seidel based method for a class of multi-period mean-variance portfolio selection problems

It is commonly accepted that the estimation error of asset returns' sample mean is much larger than that of sample covariance. In order to hedge the risk raised by the estimation error of the sample mean, we propose a sparse and robust multi-period mean-variance portfolio selection model and show how this proposed model can be equivalently reformulated as a multi-block nonsmooth convex optimization problem. In order to get an optimal strategy, a symmetric Gauss-Seidel based method is implemented. Moreover, we show that the algorithm is globally linearly convergent. The effectiveness of our portfolio selection model and the efficiency of its solution method are demonstrated by empirical experiments on both the synthetic and real datasets.

Time-Consistent Strategies for Multi-Period Portfolio Optimization with/without the Risk-Free Asset

The pre-commitment and time-consistent strategies are the two most representative investment strategies for the classic multi-period mean-variance portfolio selection problem. In this paper, we revisit the case in which there exists one risk-free asset in the market and prove that the time-consistent solution is equivalent to the optimal open-loop solution for the classic multi-period mean-variance model. Then, we further derive the explicit time-consistent solution for the classic multi-period mean-variance model only with risky assets, by constructing a novel Lagrange function and using backward induction. Also, we prove that the Sharpe ratio with both risky and risk-free assets strictly dominates that of only with risky assets under the time-consistent strategy setting. After the theoretical investigation, we perform extensive numerical simulations and out-of-sample tests to compare the performance of pre-commitment and time-consistent strategies. The empirical studies shed light on the important question: what is the primary motivation of using the time-consistent investment strategy.

Portfolio selection in a regime switching market with a bankruptcy state and an uncertain exit-time: multi-period mean–variance formulation

This paper studies three versions of the multi-period mean–variance portfolio selection problem, that are: minimum variance problem, maximum expected return problem and the trade-off problem, in a Markovian regime switching market where the exit-time is uncertain and exogenous. The underlying Markov chain contains an absorbing state, which denotes the bankruptcy state. When the Markov chain switches to this state, the investors only get a random fraction, known as the recovery rate, taking values in [0, 1] of their wealth. Asset returns, as well as recovery rate, depend on the market state. Dynamic programming and Lagrange duality method are used to derive analytical expressions for optimal investment strategies and the mean–variance efficient frontier. It is shown that portfolio selection models with no bankruptcy state and certain exit-time can be considered as special cases of our model. Some numerical examples are provided to demonstrate the effect of the recovery rate and exit-probabilities.

A mean-field formulation for multi-period asset-liability mean-variance portfolio selection with probability constraints

This paper is concerned with studying an optimal multi-period asset-liability mean-variance portfolio selection with probability constraints using mean-field formulation without embedding technique. We strictly derive its analytical optimal strategy and efficient frontier. Numerical examples shed light on efficiency and accuracy of our method when dealing with this class of multi-period non-separable mean-variance portfolio selection problems.

The Admissible Multiperiod Mean Variance Portfolio Selection Problem with Cardinality Constraints

Uncertain factors in finical markets make the prediction of future returns and risk of asset much difficult. In this paper, a model,assuming the admissible errors on expected returns and risks of assets, assisted in the multiperiod mean variance portfolio selection problem is built. The model considers transaction costs, upper bound on borrowing risk-free asset constraints, cardinality constraints and threshold constraints. Cardinality constraints limit the number of assets to be held in an efficient portfolio. At the same time, threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Because of these limitations, the proposed model is a mix integer dynamic optimization problem with path dependence. The forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, to evaluate the model, our result of a meaning example is compared to the terminal wealth under different constraints.

Multi-Period Portfolio Selection with No-Shorting Constraints: Duality Analysis

This paper considers a multi-period mean-variance portfolio selection problem with no shorting constraint. We assume that the sample space is finite, and the possible securities price vector transitions is equivalent to the number of securities. By making use of the embedding technique of Li and Ng (2000), the original nonseparable problem can be solved by introducing an auxiliary problem. After the risk neutral probability is calculated, the auxiliary problem can be solved by using the martingale method of Pliska (1986). Finally, we derive a closed form of the optimal solution to the original constrained problem.

Multi-period mean-variance portfolio selection with stochastic interest rate and uncontrollable liability

While the literature on dynamic portfolio selection with stochastic interest rates only confines its investigation to the continuous-time setting up to now, this paper studies a multi-period mean-variance portfolio selection problem with a stochastic interest rate, where the movement of the interest rate is governed by the discrete-time Vasicek model. Invoking dynamic programming approach and the Lagrange duality theory, we derive the analytical expressions for both the efficient investment strategy and the efficient mean-variance frontier of the model formulation. We then extend our model to the situation with an uncontrollable liability.

A Mean-Field Formulation for Optimal Multi-Period Asset-Liability Mean-Variance Portfolio Selection with an Uncertain Exit Time

This paper is concerned with optimal multi-period asset-liability mean-variance portfolio selection with an uncertain exit time. By extending the current mean-field theory and proposing two dimensional mean-field formulation, the analytical optimal strategies and efficient frontiers are successfully derived. Sensitivity analysis is also presented to help the investor understand the influences of liability and uncertain exit time.