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. Some computer programs are technically-based (such as the work in Schwager, Technical Analysis) and some are fundamental. More recently, and especially on Wall Street, computer models are based on mathematical models called time series forecasting. 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 such as in weather forecasting. What makes it particularly interesting is that it can be faster than neural networks.
Book Details:
- Author: John R. Wolberg
- ISBN: 9780470352670
- Year Published: 2000
- Pages: 235
- BISAC: BUS027000, BUSINESS & ECONOMICS/Finance
About the Book and Topic:
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. Some computer programs are technically-based (such as the work in Schwager, Technical Analysis) and some are fundamental. More recently, and especially on Wall Street, computer models are based on mathematical models called time series forecasting. 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 such as in weather forecasting. What makes it particularly interesting is that it can be faster than neural networks.
To make buy and sell decisions, traders and investors must have a sense of where the markets are going. One of the most accurate ways to predict market direction is through the use of financial times series forecasting. Kernel regression is an especially important methodology as it can handle massive data quickly. Expert Trading Systems: Modeling Financial Markets with Kernel Regression is a step-by-step guide to such modeling techniques for the non-statistician. Written by a mathematician with extensive computer modeling experience, it is clear and concise.
A comprehensive overview of data modeling geared to the non-statistician and non-mathematician. * Provides a data modeling methodology that the reader can use to develop a trading system. * Explains how kernel regression offers various “speed” enhancements that can be combined with neural networks to further enhance one’s trading program. * Shows how to design, test, and measure the significance of the results.
About the Author
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.