Neural Network Time Series Forecasting of Financial Markets (A Wiley finance edition) - Tapa dura

A practical introduction to the use of neural networks in financial time series forecasting, this monograph encompasses such topics as data preprocessing, random walk probability theory and specified benchmarks (and the code that is used to implement benchmarks as pre-trained networks). There is also an overview of futures trading and discussion of trading systems and risk management. Emphasis is placed on multilater perception networks, which are used in many commercial applications.

Neural Network Time Series Forecasting of Financial Markets A neural network is a computer program that can recognise patterns in data, learn from this and (in the case of time series data) make forecasts of future patterns. There are now over 20 commercially available neural network programs designed for use on financial markets and there have been some notable reports of their successful application. However, like any other computer program, neural networks are only as good as the data they are given and the questions that are asked of them. Proper use of a neural network involves spending time understanding and cleaning the data: removing errors, preprocessing and postprocessing. This book takes the reader beyond the ?black-box? approach to neural networks and provides the knowledge that is required for their proper design and use in financial markets forecasting ?with an emphasis on futures trading. Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software. For the professional financial forecaster this book is without parallel as a comprehensive, practical and up-to-date guide to this important subject.