24. The system according to claim 16 , wherein the common features of the received data values include one or more of at least a: a) closing price value of the financial instrument for a pre-defined time period; b) change in price values of the financial instrument over a pre-defined time period; c) highest value of the financial instrument over a pre-defined time period; d) spread value of the financial instrument at a pre-defined time period; e) volume of the financial instrument traded over a pre-defined time period; f) opening price value of the financial instrument for a pre-defined time period; and g) sector of the financial instrument, wherein the sector name is assigned a standard industrial classification (SIC) code.
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.