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9780471345084

Expert Trading Systems Modeling Financial Markets with Kernel Regression

by
  • ISBN13:

    9780471345084

  • ISBN10:

    0471345083

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2000-01-12
  • Publisher: Wiley

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Summary

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.

Author Biography

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.

Table of Contents

Preface xiii
Acknowledgments xvii
Introduction
1(24)
Data Modeling
1(2)
The Hills of the Galilee Problem
3(4)
Modeling Financial Markets
7(6)
Evaluating a Model
13(5)
Nonparametric Methods
18(2)
Fundamental versus Technical Analysis
20(5)
Data Modeling of Time Series
25(20)
The Time Series Problem
25(4)
Classical Methods of Time Series Modeling
29(2)
The Curse of Dimensionality
31(2)
Candidate Predictors
33(4)
The Equity Curve
37(5)
Measuring the Efficiency of a Modeling Method
42(3)
Kernel Regression
45(28)
The Basic Concept
45(4)
Higher Order Algorithms
49(6)
The Bandwidth Concept
55(1)
Error Estimates
56(6)
Applying Kernel Regression to Time Series Data
62(3)
Searching for a Model
65(4)
Timing Considerations
69(4)
High-Performance Kernel Regression
73(32)
Software Considerations
73(1)
The p-Tree
74(4)
Partitioning the Learning Data Set
78(7)
Using the p-Tree
85(6)
Time Weighting the Data
91(1)
Multistage Modeling
92(2)
The Search Engine
94(2)
Computational Complexity
96(5)
Parallel Processing
101(4)
Kernel Regression Software Performance
105(38)
Software Evaluation
105(4)
Searching Parameters
109(6)
The Effect of Tree Height
115(5)
Number of Nearest Neighbors
120(4)
Processing Time per Space
124(8)
Data Weighting
132(6)
Comparing the Three Algorithms
138(5)
Modeling Strategies
143(28)
The Modeling Plan
143(3)
Out-of-Sample Testing
146(4)
Modeling Dynamic Systems
150(5)
Cross-Sectional Modeling
155(2)
Combining Models
157(2)
Measures of Performance
159(3)
Using Kernel Regression for Classification Problems
162(5)
Fine Tuning a Model
167(4)
Creating Trading Systems
171(24)
Trading Systems
171(3)
Generating Signals
174(2)
Creating a Filter
176(3)
Cross-Sectional Trading Systems
179(2)
Updating Models
181(2)
Stock Selection: A Case Study
183(12)
Appendix A Linear Least Squares with Data Weighting 195(4)
Appendix B A Test for Significance of Variance Reduction 199(6)
Appendix C Comparing KR and Parametric Regression 205(6)
Appendix D Comparing KR and Neural Networks 211(10)
Appendix E A Test for Significance of Fraction_Same_Sign 221(4)
Bibliography 225(6)
Index 231

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