Dynamic portfolio optimization under multi‐factor model in stochastic markets

We consider the modeling and solution of the multi‐period portfolio selection problem in stochastic markets with bankruptcy risk control. This research differs from current results in the following ways: rather than in terms of return moments, the stochastic evolution of the market is directly described in terms of investment returns by a finite‐state Markov chain; the multi‐factor model is initially introduced in the modeling process to better control bankruptcy risk and to cope with large‐scale portfolio selection problems; the stable distributions are adopted to describe factors’ fluctuations to properly reflect the return distribution characteristics of risky assets; the bankruptcy risk in each period is flexibly controlled by utilizing the properties of the multi‐factor model and restricting the portfolio loss caused by each factor; a specific bi‐level programming method is proposed to find the analytical investment strategy; the practical significance and good performance of the stage‐wise investment decision, when not optimal, are verified. Empirical results are finally provided to illustrate the suitability and practical performance of the new model and the derived explicit investment strategy.