Self-organizing data mining using enhanced group method data handling approach

TL;DR: Experimental results reveal that all the models considered could represent the stock indices behavior very accurately and that the proposed e-GMDH approach is a useful for data mining technique for forecasting and modeling stock indices.

Abstract: Data Mining (DM) is a relatively recent technology that is employed in inferring useful knowledge that can be put to use from a vast amount of data. This paper presents the data mining processes applied to the seemingly chaotic behavior of stock markets which could be well represented using the enhance GMDH, and we compared its results with published results using neural network, TS fuzzy system and hierarchical TS fuzzy techniques. To demonstrate the capabilities of the different techniques, we considered Nasdaq-100 index of Nasdaq Stock Market SM and the S&P CNX NIFTY stock index. We analyzed 7 year's Nasdaq 100 main index values and 4 year's NIFTY index values. This paper investigates the development of novel reliable and efficient techniques to model the seemingly chaotic behavior of stock markets. Experimental results reveal that all the models considered could represent the stock indices behavior very accurately and that the proposed e-GMDH approach is a useful for data mining technique for forecasting and modeling stock indices.