Abstract: An approach to large-scale portfolio optimization for asset returns represented by factor models is disclosed. Factor models can be used within general portfolio optimization problems, such as mean-variance optimization, expected utility maximization, and mean-risk optimization, with various measures of risk, including conditional Value-at-Risk, as well as the representation of risk constraints and constraints on higher moments of the asset return distribution. Both expected utility maximization and mean-risk optimization are more general than mean-variance optimization and can consider fat tails in the asset return distribution and, thus, allow for better control of downside risk. Explicit risk constraints especially constraints on conditional Value-at-Risk, limit downside risk in either mean-variance optimization, expected utility maximization, or mean-risk optimization. Constraints on higher moments limit fat tails of the asset return distribution.

Abstract: 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 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 and general framework for representing investor preference by representing utility by directly modeling risk aversion as a function of wealth. The system and method demonstrate how the optimal asset allocation depends on the investment horizon, wealth, and the investor's risk preference and how optimal asset allocation therefore changes over time depending on cash flow and the returns achieved.

Abstract: A system and method efficiently solve the expected utility maximization problem in large-scale financial asset portfolio optimization. The system and method solve the expected utility maximization problem employing a factor representation of asset returns. Additionally, the system and method calibrate the optimization model to a benchmark to obtain unconditional mean returns and enable active management based on conditional expected return predictions. The system and method also enable options to be considered as part of the portfolio.

Abstract: A system and method efficiently solve the expected utility maximization problem in large-scale financial asset portfolio optimization. The system and method solve the expected utility maximization problem employing a factor representation of asset returns. Additionally, the system and method calibrate the optimization model to a benchmark to obtain unconditional mean returns and enable active management based on conditional expected return predictions. The system and method also enable options to be considered as part of the portfolio.

Abstract: 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.