Expert Trading Systems

Modeling Financial Markets with Kernel Regression

Wolberg, John R.

Wiley Trading Series

1. Edition February 2000
256 Pages, Hardcover
Wiley & Sons Ltd

ISBN: 978-0-471-34508-4

John Wiley & Sons

Short Description

With the proliferation of computer programs to predict market direction, professional traders and sophisticated individual investors have increasingly turned to mathematical modeling to develop predictive systems. Kernel regression is a nonlinear, nonparametric mathematical methodology that can be applied to financial market prediction. It is a data modeling technique used when the independent variable (f(X)) is not known, and can be faster than neural networks.

Further versions

With the proliferation of computer programs to predict market direction, professional traders and sophisticated individual investors have increasingly turned to mathematical modeling to develop predictive systems. Kernel regression is a popular data modeling technique that can yield useful results fast.

Provides data modeling methodology used to develop trading systems.
* Shows how to design, test, and measure the significance of results

John R. Wolberg (Haifa, Israel) is professor of mechanical engineering at the Haifa Institute in Israel. He does research and consulting in data modeling in the financial services area.

Data Modeling of Time Series.

Kernel Regression.

High-Performance Kernel Regression.

Kernel Regression Software Performance.

Modeling Strategies.

Creating Trading Systems.

Appendices.

Bibliography.

Index.

JOHN R. WOLBERG, PhD, is a professor of mechanical engineering at the Technion-Israel Institute of Technology in Haifa, Israel. An expert in financial data modeling, he does research and consulting for leading financial institutions, and has worked with some of the pioneers of computerized trading. Dr. Wolberg holds a bachelor's degree in mechanical engineering from Cornell University and a PhD in nuclear engineering from MIT.