Abstract: In one embodiment, a computer-implemented method for calculating the price of a corporate loan comprises receiving, by a server, a first data input regarding a particular corporate loan; receiving, by the server, a second data input regarding market data; generating a pricing lattice for the corporate loan, wherein the pricing lattice has a first axis for a credit rating and a second axis for a time period; and deriving, by a server, using backward induction, a value for the corporate loan.

Abstract: The present invention relates to methods and systems for devising and implementing automated artificial neural networks to predict market performance and direction movements of the U.S. Treasury market, mortgage option-adjusted spreads (OAS), interest rate swap spreads, and U.S. Dollar/Mexican Peso exchange rate. The methods and systems of the present invention employ techniques used in actual neural networks naturally occurring in biological organisms to develop artificial neural network models for predicting movements in the financial market that are capable of extracting in a very consistent fashion non-linear relationships among input variables of the models that are readily apparent to the human traders.