Dynamic asset allocation using stochastic dynamic programming

摘要

A system and method are disclosed for capturing the full dynamic and multi-dimensional nature of the asset allocation problem through applications of stochastic dynamic programming and stochastic programming techniques. The system and method provide a novel approach to asset allocation and based on stochastic dynamic programming and Monte Carlo sampling that permit one to consider many rebalancing periods, many asset classes, dynamic cash flows, and a general representation of investor risk preference. The system and method further provide a novel approach of representing utility by directly modeling risk aversion as a function of wealth, and thus provide a general framework for representing investor preference. The system and method demonstrate how the optimal asset allocation depends on the investment horizon, wealth, and the investors risk preference and how optimal asset allocation therefore changes over time depending on cash flow and the returns achieved and how dynamic asset allocation leads to superior results compared to static or myopic techniques. Examples of dynamic strategies for various typical risk preferences and multiple asset classes are described.