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Neural networks in finance: an information analysis

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2 Author(s)
R. N. Kahn ; BARRA Inc., Berkeley, CA, USA ; A. K. Basu

We classify financial applications of neural networks into two broad classes by stability and signal-to-noise ratio. We present two statistical measures typically applied to investment analysis: the information ratio (IR) and the information coefficient (IC); then we use Monte-Carlo simulations to critically examine neural net performance as a function of signal-to-noise ratio in characteristic investment domains. We thus measure the maximum noise level tolerable by neural nets during training on a representative class of investment problems

Published in:

Computational Intelligence for Financial Engineering, 1995.,Proceedings of the IEEE/IAFE 1995

Date of Conference:

9-11 Apr 1995