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Michael Holden
Faculty Sponsor: Professor Gordon H. Dash
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ANN is structured after a biological neural
network
A mathematical model that attempts to mine,
predict, and forecast data
Provides Artificial Intelligence (AI)

A process of pattern recognition and
manipulation is based on:
◦ Massive Parallelism
◦ Connectionism
◦ Associative Distributed Memory
Brain contains an
interconnected net
of approximately
10 billion neurons
(cortical cells)
Biological Neuron
The simple “arithmetic
computing” element
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Mathematical Model of humanbrain principles of computations
Consists of elements called the
biological neuron prototype
◦ Interconnected by direct links
(connections)
◦ Cooperate to perform PDP to solve a
computational task
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New paradigms of computing mathematics
consists of the combination of artificial
neurons into artificial neural net
Brain-Like Computer
?
Rules
&
Knowledge
Productions
Interpretation
and
Decision Making
Data
Analysis
Data
Acquisition
Signals
&
parameters
Data
Acquisition
Characteristics
&
Estimations
Adaptive Machine Learning
via Neural Network
Data
Analysis
Knowledge
Base
Decision
Making
Independent Variables
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
30-Day Treasury Bill
20-Year Treasury Bond
Dependent Variables
- Equity Market Neutral
- Event Driven
- Global Macro

Volatility Index (VIX)
- Long/Short Equity
 WinORSe-AI
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Windows Operating Research
System with e-data and
artificial intelligence
capabilities
Developed by NKD-Group, Inc.
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Neural Network is
not programmed – it
learns
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Training = Learning
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Validating = Testing
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33.3%
Kajiji-4 is the algorithm
GCV is Generalized Cross Validation
Gaussian transfers information between
nodes
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RBF – Parameters
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RBF – Weights
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RBF - Predicted
Equity Market
Neutral
Event
Driven
Global
Macro
Long/Short
Equity
Actual Error
1.33E-01
1.33E+00
2.13E+00
1.10E+00
Training Error
1.66E-03
1.10E-01
3.55E-02
6.54E-03
Validation Error
1.73E-03
4.22E-02
1.13E-02
6.36E-03
Fitness Error
1.71E-03
6.45E-02
1.92E-02
6.42E-03
Computed Measures
Equity Market
Neutral
Event
Driven
Global
Macro
Long/Short
Equity
Direction
0.981
0.932
0.951
0.990
Modified Direction
0.994
0.963
0.961
1.000
TDPM
0.000
0.007
0.002
0.001
99.99%
99.45%
99.89%
99.98%
AIC
-1299.784
-555.89
-803.838
-1028.749
Schwarz
-1289.815
-545.921
-793.869
-1018.78
10.17
29.71
14.67
8.23
Performance
Measures
R-Square
MAPE

Gives relativity of independent variables
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Absolute numbers > signs
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*Global Macro and Event Driven
Actual Return
-Predicted Return
Residual
How well did it learn?
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Small Residuals
◦ Most < 1bp
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Very Fit Model
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2 Factors
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Principal Component Analysis
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Explains Majority of Variance
◦ Global vs. Domestic
◦ Some variance not captured by residuals
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Fit Model
◦ Learned very well
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Small Residuals
◦ Trained very well
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Factors explained 90.4% of variance
◦ Include global and domestic independent variable
next time
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Excellent Predictive Ability