Michael Holden Faculty Sponsor: Professor Gordon H. Dash

 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

 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

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

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

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

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

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

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

 RBF – Parameters  RBF – Weights  RBF - Predicted

Equity Market Neutral Event Driven Global Macro Long/Short Equity Computed Measures Actual Error 1.33E E E E+00 Training Error 1.66E E E E-03 Validation Error 1.73E E E E-03 Fitness Error 1.71E E E E-03

Performance Measures Equity Market Neutral Event Driven Global Macro Long/Short Equity Direction Modified Direction TDPM R-Square 99.99%99.45%99.89%99.98% AIC Schwarz MAPE

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

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

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

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

 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