Matlab Neural Networks In Finance

Questions of finance and market success or failure are first and foremost

quantitative. Applied researchers and practitioners are interested not only in predicting the direction of change but also how much prices, rates of return, spreads, or likelihood of defaults will change in response to changes in economic conditions, policy uncertainty, or waves of bullish and bearish behavior in domestic or foreign markets. For this reason, the premium is on both the precision of the estimates of expected rates of return, spreads, and default rates, as well as the computational ease and speed with which these estimates may be obtained. Finance and market research is both empirical

and computational.
Peter Bernstein (1998) reminds us in his best-selling book

Against the Gods, that the driving force behind the development of probability theory was the precise calculation of odds in games of chance. Financial markets represent the foremost “games of chance” today, and there is no reason to doubt that the precise calculation of the odds and the risks in this global game is the driving force in quantitative financial analysis, decision making, and policy evaluation.

Besides precision, speed of computation is of paramount importance in quantitative financial analysis. Decision makers in business organizations or in financial institutions do not have long periods of time to wait before having to commit to buy or sell, set prices, or make investment decisions.