1. Michael Holden Faculty Sponsor: Professor Gordon H. Dash

2.  ANN is structured after a biological neural network  A mathematical model that attempts to mine, predict, and forecast data  Provides Artificial Intelligence (AI)

3.  A process of pattern recognition and manipulation is based on: ◦ Massive Parallelism ◦ Connectionism ◦ Associative Distributed Memory

4. Brain contains an interconnected net of approximately 10 billion neurons (cortical cells) Biological Neuron The simple “arithmetic computing” element

5.  Mathematical Model of human- brain 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

6.  New paradigms of computing mathematics consists of the combination of artificial neurons into artificial neural net ? Brain-Like Computer

7. Data Acquisition Data Analysis Interpretation and Decision Making Signals & parameters Characteristics & Estimations Rules & Knowledge Productions Data Acquisition Data Analysis Decision Making Knowledge Base Adaptive Machine Learning via Neural Network

8. Independent VariablesDependent Variables  30-Day Treasury Bill  20-Year Treasury Bond  Volatility Index (VIX) -Equity Market Neutral -Event Driven -Global Macro -Long/Short Equity

9.  WinORS e-AI  Windows Operating Research System with e-data and artificial intelligence capabilities  Developed by NKD-Group, Inc.

12.  Neural Network is not programmed – it learns  Training = Learning  Validating = Testing  33.3%

13. Kajiji-4 is the algorithm GCV is Generalized Cross Validation Gaussian transfers information between nodes

14.  RBF – Parameters  RBF – Weights  RBF - Predicted

15. Equity Market Neutral Event Driven Global Macro Long/Short Equity Computed Measures Actual Error 1.33E-011.33E+002.13E+001.10E+00 Training Error 1.66E-031.10E-013.55E-026.54E-03 Validation Error 1.73E-034.22E-021.13E-026.36E-03 Fitness Error 1.71E-036.45E-021.92E-026.42E-03

16. Performance Measures Equity Market Neutral Event Driven Global Macro Long/Short Equity Direction 0.9810.9320.9510.990 Modified Direction 0.9940.9630.9611.000 TDPM 0.0000.0070.0020.001 R-Square 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 MAPE 10.1729.7114.678.23

17.  Gives relativity of independent variables  Absolute numbers > signs  *Global Macro and Event Driven

19. Actual Return -Predicted Return Residual How well did it learn?

20.  Small Residuals ◦ Most < 1bp  Very Fit Model

22.  2 Factors ◦ Global vs. Domestic  Principal Component Analysis  Explains Majority of Variance ◦ Some variance not captured by residuals

23.  Fit Model ◦ Learned very well  Small Residuals ◦ Trained very well  Factors explained 90.4% of variance ◦ Include global and domestic independent variable next time  Excellent Predictive Ability