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Neural Network Time Series: Forecasting of Financial Markets 1st Edition

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Neural Network Time Series Forecasting of Financial Markets E. Michael Azoff The first comprehensive and practical introduction to using neural networks in financial time series forecasting. This practical working guide shows you how to understand, design and profitably use neural network techniques in financial market forecasting. It encompasses:
  • A tutorial introduction to neural networks
  • Data preprocessing
  • Key network design issues
  • Random walk probability theory
  • Fully specified benchmarks (and code for implementing the benchmarks as pre-trained networks)
  • An overview of futures trading
  • Discussion of trading systems and risk management
The book focuses on the multilayer perception, one of the most powerful and successful network architectures that is used in the majority of commercial applications, especially financial time series forecasting. The fully specified benchmarks are a unique feature of the book and will be of particular benefit if you are contemplating designing your own neural network using one of the many commercial simulators.

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From the Publisher

Introduces the use of neural networks in forecasting and, in particular, financial time series forecasting. Provides much-needed guidance for applying predictive and decision-enhancing functions of neural nets to a wide range of global capital markets investments and futures trading.

From the Inside Flap

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.

Product details

  • Publisher ‏ : ‎ Wiley; 1st edition (September 13, 1994)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 212 pages
  • ISBN-10 ‏ : ‎ 0471943568
  • ISBN-13 ‏ : ‎ 978-0471943563
  • Item Weight ‏ : ‎ 1.05 pounds
  • Dimensions ‏ : ‎ 6.22 x 0.71 x 9.37 inches
  • Customer Reviews:
    3.0 3.0 out of 5 stars 1 rating

About the author

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Eitan Michael Azoff
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My education (BEng from UCL in electronic and electrical engineering, MSc from University of London in solid state physics, and PhD from University of Sheffield in solid state electronics) led to research work in novel semiconductor device simulation for ministry of defence contracts at University of Sheffield, and then Rutherford Appleton Laboratory on UK and EU research contracts.

In 1989 I joined a startup in the University of Nottingham Science Park, which included working with neural networks, funded by an award from the UK government department of trade and industry. On Black Wednesday when the UK left the ERM our startup’s funder backed out and I started working as a consultant through my company Netnumerics. With Perot Systems (now acquired by Dell) I developed electricity generation price forecasts for East Midlands Electricity (now acquired by Powergen) using neural networks. I also built a Microsoft Excel add-in, Prognostica, for time series forecasting. During this time, I engaged with partners, including a boutique investment house, to apply neural networks to time series forecasting of financial markets. My models generated 55% accuracy in predicting the direction of major financial indices like the FTSE 100. We considered this not good enough (kind of mistakenly – interestingly I recently read that Jim Simons at Renaissance had similar statistics and with deep enough pockets could monetize it). I subsequently wrote up my knowledge of neural networks in a book (Azoff, 1994), out of print but soon to re-appear as a Kindle.

In the past two decades I’ve worked as a high-tech industry analyst, mostly at Informa business Ovum, and after an independent break currently back with Informa business Omdia as chief analyst in the cloud and data center practice. This book arose in my time as an independent consultant.

My interest in AI followed the invention of backpropagation, the breakthrough and excitement it stirred touched me around 1988. I joined a newly formed UK group with membership drawn from industry and academia, the Neural Computing Applications Forum, which led to the formation of a research journal published by Springer: Neural Computing and Applications. After Netnumerics in subsequent decades I kept my interest in AI on hold but began to cover it as an industry analyst with the emergence of deep learning in 2010-12, the rise of AI accelerator processors, and more recently by the breakthroughs with generative AI and LLM.

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