Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance

Abstract

From the Publisher: Neural networks are revolutionizing virtually every aspect of financial and investment decision making. Financial firms worldwide are employing neural networks to tackle difficult tasks involving intuitive judgement or requiring the detection of data patterns which elude conventional analytic techniques. Many observers believe neural networks will eventually outperform even the best traders and investors. Neural networks are already being used to trade the securities markets, to forecast the economy and to analyze credit risk. Indeed, apart from the U.S. Department of Defense, the financial services industry has invested more money in neural network research than any other industry or government body. Unlike other types of artificial intelligence, neural networks mimic to some extent the processing characteristics of the human brain. As a result, neural networks can draw conclusions from incomplete data, recognize patterns as they unfold in real time and forecast the future. They can even learn from past mistakes! In Neural Networks in Finance and Investing, Robert Trippi and Efraim Turban have assembled a stellar collection of articles by experts in industry and academia on the applications of neural networks in this important arena. They discuss neural network successes and failures, as well as identify the vast unrealized potential of neural networks in numerous specialized areas of financial decision making. Topics include neural network fundamentals and overview, analysis of financial condition, business failure prediction, debt risk assessment, security market applications, and neural network approaches to financial forecasting. Nowhere else will the finance professional find such an exciting and relevant in-depth examination of neural networks. Individual chapters discuss how to use neural networks to forecast the stock market, to trade commodities, to assess bond and mortgage risk, to predict bankruptcy and to implement investment strategies. Taken toge

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