Abstract: The system and method described herein may be used to forecast realized volatility via wavelets and non-linear dynamics. In particular, a volatility time series that includes daily volatility values associated with a security may be decomposed into wavelets via multi-resolution analysis and dynamical properties associated with the individual wavelets may be analyzed to identify deterministic and non-deterministic wavelets and produce a volatility forecast derived from a fit computed on the deterministic wavelets. For example, the wavelets may be analyzed to discover time delay, Theiler, and embedding dimension values associated therewith, which may be used to project volatility values associated with each wavelet. The projected volatility values associated with each wavelet may then be summed to produce a volatility forecast associated with the security.

Abstract: The system and method described herein may be used to construct outperforming portfolios relative to target benchmarks. In particular, the system and method described herein may use multi-factor models that employ multi-objective evolutionary algorithms and mean variance optimization calculations to select constituents from a target benchmark index to include in a portfolio. The selected constituents may then be weighed to construct or rebalance the portfolio in a manner that can consistently outperform the target benchmark index while satisfying real-world constraints that relate to turnover limits, minimum and maximum stock positions, cardinalities, target market capitalizations, investment strategies, and other characteristics associated with the portfolio.

Abstract: The system and method described herein may be used to forecast realized volatility via wavelets and non-linear dynamics. In particular, a volatility time series that includes daily volatility values associated with a security may be decomposed into wavelets via multi-resolution analysis and dynamical properties associated with the individual wavelets may be analyzed to identify deterministic and non-deterministic wavelets and produce a volatility forecast derived from a fit computed on the deterministic wavelets. For example, the wavelets may be analyzed to discover time delay, Theiler, and embedding dimension values associated therewith, which may be used to project volatility values associated with each wavelet. The projected volatility values associated with each wavelet may then be summed to produce a volatility forecast associated with the security.