Abstract: In an exemplary embodiment, invention is a method for analyzing portfolios of distressed financial assets for the purpose of bidding to acquire those assets. The method utilizes a network-based system including a server system coupled to a centralized database and at least one client system. The method comprises of various steps from generating cash flow data table from variety of data sources to performing sensitivity analysis using Monte Carlo Simulation Model to provide different scenarios. The method utilizes a variety of assumptions in performing analysis, and exports cash flow projections into a pre-determined format to develop financially attractive bids, which have strong probability of, expected return on investment after taking into account a variety of foreseeable risks.

Abstract: The present invention is a method of allowing inclusion of more than one variable in a Classification and Regression Tree (CART) analysis. The method includes predicting y using p exploratory variables, where y is a multivariate, continuous response vector, describing a probability density function at “parent” and “child” nodes using a multivariate normal distribution, which is a function of y, and defining a split function where “child” node distributions are individualized, compared to the parent node. In one embodiment a system is configured to implement the multivariate CART analysis for predicting behavior in a non-performing loan portfolio.

Abstract: In an exemplary embodiment, invention is a method for analyzing portfolios of distressed financial assets for the purpose of bidding to acquire those assets. The method utilizes a network-based system including a server system coupled to a centralized database and at least one client system. The method comprises of various steps from generating cash flow data table from variety of data sources to performing sensitivity analysis using Monte Carlo Simulation Model to provide different scenarios. The method utilizes a variety of assumptions in performing analysis, and exports cash flow projections into a pre-determined format to develop financially attractive bids, which have strong probability of, expected return on investment after taking into account a variety of foreseeable risks.

Abstract: A method of valuation of large groups of assets by partial full underwriting, partial sample underwriting and inferred values of the remainder using an iterative and adaptive statistical evaluation of all assets and statistical inferences drawn from the evaluation and applied to generate inferred values. Individual asset values are developed and listed in tables so that individual asset values can be taken and quickly grouped in any desired or prescribed manner for bidding purposes. The assets are collected into a database, divided by credit variable, subdivided by ratings as to those variables and then rated individually. The assets are then regrouped according to a bidding grouping and a collective valuation established by cumulating the individual valuations.

Abstract: A method of valuation of large groups of assets using classification and regression trees is described. The method includes defining relevant portfolio segmentations, assessing performance of the classification and regression tree based model against a simple model and ranking all portfolio segments based upon performance of the models. Iterative and adaptive statistical evaluation of all assets and statistical inferences are used to generate the segmentations. The assets are collected into a database, grouped by credit variable, subdivided by ratings as to those variables and then rated individually. The assets are then regrouped and a collective valuation is established by cumulating individual valuations.