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. 15. The method according to claim 1 , wherein the receiving step, by the computer, further comprises receiving emotional sentiment data corresponding to the financial instrument from computer-based sources on the internet; the extracting step, by the computer, further comprises extracting textual information from the emotional sentiment data from computer-based sources on the internet; the building step, by the computer, further comprises assigning a numerical score to the financial instrument corresponding to the positive and negative emotional words extracted from the textual information, wherein the numerical score is a product of a numerical value attached to each positive and negative emotional word, and numerical weights associated with performance of the industry associated with the financial instrument; and the combination step, by the computer, further comprises weighing each second prediction model in the series arrangement by the numerical score.