1. A method for predicting financial instrument performance, the method comprising: receiving, by a computer, data values corresponding to a financial instrument, the data values comprising a plurality of common features; extracting, by the computer, 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; building, by the computer, 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 combining, by the computer, 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.
1. A method for predicting financial instrument performance, the method comprising: receiving, by a computer, data values corresponding to a financial instrument, the data values comprising a plurality of common features; extracting, by the computer, 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; building, by the computer, 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 combining, by the computer, 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. 2. The method according to claim 1 , further comprising: computing, by the computer, a predictive error from the predicted data values of the each of the second prediction models and the combined prediction model, the predictive error computed using a second random selection of received data values to test each of the second prediction models and the combined prediction model; and iterating, by the computer, the extracting step to create a new first common feature, the building step to build a plurality of new second prediction models, and the combining step to combine each of the new second prediction models using the computed predictive error for each of the second prediction models and the combined prediction model, thereby incorporating the predictive error to each of the second prediction models and the combined prediction model for reducing the predictive error and updating the combined predicted data value corresponding to the financial instrument.