18. The system according to claim 16 , wherein the extracting part of the system further comprises: the computer for selecting a third random selection of the received data values, the third random selection of received data values corresponding to all the common features comprised in the received data values; and the computer for processing the third random selection of received data values using a conditional independence test and at least one first prediction model comprising a classification and regression tree (CART) model, a multivariate adaptive regression splines (MARS) model, or a random forests model, wherein processing the third random selection of received data values identifies influential features from among the received common features and related dependence values for each influential feature.
16. A system for predicting financial instrument performance, the system comprising: a computer for receiving data values corresponding to a financial instrument, the data values comprising a plurality of common features; the computer for extracting, using at least a first prediction model, more than one first common feature from the received plurality of common features and a corresponding dependence value for each of the extracted first common features, each of the extracted first common features being an influential feature; the computer for building a plurality of second prediction models, each of the second prediction models corresponding to each of the extracted influential features and each of the second prediction models using a first random selection of the received data values corresponding to each of the extracted influential features to generate predicted data values corresponding to the financial instrument for each extracted influential feature; and the computer for combining each of the second prediction models in a series arrangement, wherein the predicted data values corresponding to one second prediction model is input to another second prediction model until all the second prediction models are involved, thereby creating a combined prediction model, and wherein the combined prediction model uses the dependence value identified in the first prediction model as a weighing value for each predicted data value from each second prediction model, and the combined prediction model generates a combined predicted data value corresponding to the financial instrument.