Nonlinear Models
8 February 1999
Data Mining in Finance
Andreas S. Weigend
Leonard N. Stern School of Business, New York University
1
RiskTeam/ Zürich, 6 July 1998 Andreas S. Weigend, Data Mining Group, Information Systems Department, Stern School of Business, NYU
The seven steps of model building
1. Task
Predict distribution of portfolio returns, understand structure in yield curves, find profitable time scales, discover trade styles, …
2. Data
Which data to use, and how to code/ preprocess/ represent them
3. Architecture
4. Objective/ Cost function (in-sample)
5. Search/ Optimization/ Estimation
6. Evaluation
7. Analysis and Interpretation
2
RiskTeam/ Zürich, 6 July 1998 Andreas S. Weigend, Data Mining Group, Information Systems Department, Stern School of Business, NYU
How to make predictions?
“Pattern”= Input + Output Pair
Keep all data
Nearest neighbor lookup
Local constant model
Local linear model
Throw away data, only keep model
Global linear model
Global nonlinear model
work with hidden units
Sigmoids or hyperbolic tangents (tanh)
Radial basis functions
Keep only a few representative data point
Support vector machines
3
RiskTeam/ Zürich, 6 July 1998 Andreas S. Weigend, Data Mining Group, Information Systems Department, Stern School of Business, NYU
Training data: Inputs and corresponding outputs
input1
output
input2
4
RiskTeam/ Zürich, 6 July 1998 Andreas S. Weigend, Data Mining Group, Information Systems Department, Stern School of Business, NYU
What is the prediction for a new input?
input1
output
input2
new input
5
RiskTeam/ Zürich, 6 July 1998 Andreas S. Weigend, Data Mining Group, Information Systems Department, Stern School of Business, NYU
input1
output
input2
new input
nearest neighbor
prediction
Nearest neighbor
Use output value of nearest neighbor in input space as prediction
6
RiskTeam/ Zürich, 6 July 1998 Andreas S. Weigend, Data Mining Group, Information Systems Department, Stern School of Business, NYU
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