Abstract: Embodiments model a series of partial trades on an investment portfolio to reduce the optimality discrepancy relative to a target optimal portfolio, and determine whether a partially rebalanced portfolio along the trading path is within a predefined threshold of statistical optimality relative to the target optimal portfolio. Certain embodiments maximize the impact of partial rebalancing of a portfolio by maximizing reduction of an optimality discrepancy while minimizing the trade cost function along the trading path from the initial portfolio toward the target optimal portfolio.

Abstract: Michaud rebalance probabilities are renormalized in the case of successive datasets, historical or simulated, where partial commonality of information is imputed to the two datasets. Two separate sets of optimization inputs correspond to a stochastic process and optimization subject to a set of constraints making the optimization analytically intractable. A subset of data drawn on the basis the first optimization input is recursively replaced with data sampled from the second optimization input, the extent of replacement governed by the extent of common information. A set of rebalance probabilities is calculated, and the Lth percentile is selected from the set of rebalance probabilities, where L is a specified confidence level. An adjusted critical value serves as a need-to-execute trigger for a single portfolio or a class of portfolios.