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Special Reports
on TECHNOLOGY for INVESTING

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The BIG Picture
A unified perspective, showing how and why Jurik's modules work well as building blocks for reliable low-lag indicators. Includes graphics. Author: Mark Jurik

Why Use JMA ?
Outlines the four basic benchmarks for judging the quality of moving averages with regard to financial trading. Compares JMA to classical and modern filter designs. Includes graphics. Author: Mark Jurik

Evolution of Moving Averages
Summarizes the recent evolution of moving average filter design. Compares popular versions to a set of ideal performance features. Regarding how well filters process noisy time series data with price-gaps, report shows that the latest designs are getting very close to theoretical performance limits. Includes graphics. Author: Mark Jurik

Relating Neural Networks to Statistical Methods
Summarizes the relationship between neural nets and modern statistical methods. No mathematics. Author's conclusion is that "most neural networks that can learn to generalize effectively from noisy data are similar or identical to statistical methods." Also lists neural net models that have no close relatives in the existing statistical literature. Appended to this document is a comparison between verbal jargon used by neural netters and statisticians. Author: Warren Sarle

Neural Networks for Trading the Markets: Primer
A brief introduction to the use of neural networks suitable for futures forecasting. Author: Don W. Fitzpatrick

Neural Networks for Trading the Markets: Case Study #1

TITLE: Neural Nets for Personal Investing

This version, submitted to us by the author, is an adaptation of his original article submitted to HEURISTICS: The Journal of Intelligent Technologies, to be published in their special issue: Neural Networks for Financial Systems, v9, #1. Reviews the development and results of a neural-net based trading system. Author: William Arnold

Neural Networks for Trading the Markets: Case Study #2

TITLE: Financial Time Series Forecasting by Neural Networks

Compares two different neural network training algorithms used to model the time series of companies on the Shanghai Stock Exchange. Shows that the Conjugate Gradient Descent algorithm is better than classic Gradient Descent. Authors: CHAN Man-Chung, WONG Chi-Cheong, LAM Chi-Chung -- (Hong Kong Polytechnic University)

Overview of BackPercolation
A non-mathematical overview of the philosophy behind the design of the BackPercolation method of training Perceptron-based neural nets. Author: Mark Jurik

Some Programming Issues in TradeStation EasyLanguage
This document illustrates how TradeStation may produce counter-intuitive results when calling Easy Language functions. Alternative code that avoids the problem is provided and each case clearly explains why one method works and the other does not. Lastly, examples are provided showing how to avoid these situations when using studies from Jurik Research. -- Author: Mark Jurik

Series/Simple Functions in Easy Language
Explains the fundamental difference between two types of Easy Language functions in TradeStation. Charts included. Author: Mark Jurik

Optimal Forecast Horizon
Leading indicators require data with low noise and low lag, because that combination yields the widest window of time in which a forecast can be accurate. This paper briefly touches on chaos theory to present the notion of any time series having an "optimal" forecast horizon. Author: Mark Jurik

Classification Tree of Modeling Techniques
This one page diagram shows all the modeling methods arranged in a hierarchical tree, where results from one method feed into other methods. Great for getting the big picture on modeling methods and how they relate. Author: Unknown

 Neural Networks: Myths and Reality (web link)
So what is neural network technology, what should and what shouldn't a trader expect from it if he selects to use it to achieve his trading goals?

















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