JEL CATEGORY C22 ECONOMETRIC METHODS: Time Series Models C45 ECONOMETRIC AND STATISTICAL METHODS; Neural Networks C53 ECONOMETRIC MODELING; Forecasting

JEL CATEGORY C22 ECONOMETRIC METHODS: Time Series Models C45 ECONOMETRIC AND STATISTICAL METHODS; Neural Networks C53 ECONOMETRIC MODELING; Forecasting

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by Gordon H. Dash , Nina Kajiji

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TITLE	JEL CATEGORY C22 ECONOMETRIC METHODS: Time Series Models C45 ECONOMETRIC AND STATISTICAL METHODS; Neural Networks C53 ECONOMETRIC MODELING; Forecasting	SVM HeaderParse 0.2
AUTHOR NAME	Gordon H. Dash	SVM HeaderParse 0.2
AUTHOR NAME	Nina Kajiji	SVM HeaderParse 0.2
AUTHOR AFFIL	; Initial Printing: 31-MAR-03; Submitted to The PanAgora Quantitative Research Institute, Boston, MA for consideration and reading for the 2003 Crowell Competition.	SVM HeaderParse 0.2
AUTHOR ADDR	; Voice: (401) 241-7730; Fax: (401) 874; **National Center on Public Education & Social Policy, University of Rhode Island, 80 Washington Street, Providence, RI 02903	SVM HeaderParse 0.2
ABSTRACT	Over the recent past, stylized facts have not yielded a synthesis regarding the predictability of returns for alternative investment assets such as hedge funds. Recent studies on alternative asset return predictability have added to the ambiguity. These studies suggest that classification prediction methods may dominate more traditional return-level prediction methodology. This paper examines the predictive accuracy of three alternate radial basis function neural networks when applied to the returns of thirteen Credit Swiss First Boston/Tremont (CSFB) hedge fund indices. We provide evidence that the Kajiji-4 RBF neural network dominates within the RBF topology in the prediction of hedge fund returns by both level and classification. The results also show that the Kajiji-4 method is capable of near perfect directional prediction.	SVM HeaderParse 0.2
CITATIONS	36 found	ParsCit 1.0