Would Evolutionary Computation Help for Designs of Artificial Neural Nets in Financial Applications?
) (Masterlink Securities Corporation) Abstract Since the pioneering work by White (1988), the application of artificial neural networks (ANNs) to finance has enjoyed an exponential growth in research and publications. The evidence accumulated over the last decade indicates that the success of the financial application of an ANN depends crucially on its design. The last few years have seen a series of financial applications of evolutionary ANNs (EANNs). Margarita (1991) applies a genetic search to the weights of a recurrent network for the trading of the FIAT shares in the Milan Stock Exchange. In Dorsey, Johnson and Mayer (1995), the GA is found to perform well when optimizing neural networks (NNs). Sexton, Johnson and Dorsey (1995) also find the GA-optimized NN to outperform the back-propagated NN (BPNN) when testing out-of-sample, thereby addressing the problem of overfitting. Harrald and Kamstra (1998) use evolutionary programming to replace the more familiar back-propagation method
Related Questions
- What advantages may I expect from applying artificial neural networks in the prediction of financial markets?
- What advantages can I expect from applying artificial neural networks in the prediction of financial markets?
- Would Evolutionary Computation Help for Designs of Artificial Neural Nets in Financial Applications?
