N E U R A L   N E T S   &   F I N A N C E

▪ In highly volatile MARKETS, transactions in every type of financial instrument produce huge volumes of trading data that form the basis for neural network models to forecast everything from interest rates to the movement of currencies or commodities. The many influences on institutional or individual investor behavior can never be quantified in a closed form algorithm, yet empirical models derived from the data consistently outperform human traders and portfolio managers.

▪ Our RESULTS prove that NIF neurals are implemented very successfully to forecast the financial market.

---> EURO/USD: +45% p.a.

---> GOLD: +80% p.a.

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▪  NEURAL NETWORKS have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology and physics. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. This sweeping success can be attributed to the following few key factors:

▪ POWER: Neural networks are very sophisticated modeling techniques capable of modeling extremely complex functions. In particular, neural networks are nonlinear. For many years linear modeling has been the commonly used technique in most modeling domains since linear models have well-known optimization strategies. Where the linear approximation was not valid (which was frequently the case) the models suffered accordingly. Neural networks also keep in check the curse of dimensionality problem that bedevils attempts to model nonlinear functions with large numbers of variables.

▪ METHOD OF USE: Neural networks learn by example. The neural network user gathers representative data, and then invokes training algorithms to automatically learn the structure of the data. Users need to have knowledge of how to select and prepare data, how to select an appropriate neural network, and how to interpret the results.