Contributed article
Stock performance modeling using neural networks: A comparative study with regression models

https://doi.org/10.1016/0893-6080(94)90030-2Get rights and content

Abstract

We examine the use of neural networks as an alternative to classical statistical techniques for forecasting within the framework of the APT (arbitrage pricing theory) model for stock ranking. We show that neural networks outperform these statistical techniques in forecasting accuracy terms, and give better model fitness in-sample by one order of magnitude. We identify intervals for the network parameter values for which these performance figures are statistically stable. Neural networks have been criticised for not being able to provide an explanation of how they interact with their environment and how they reach an outcome. We show that by using sensitivity analysis, neural networks can provide a reasonable explanation of their predictive behaviour and can model their environment more convincingly than regression models.

References (16)

  • R.P. Gorman et al.

    Analysis of hidden units in a layered network trained to classify sonar targets

    Neural Networks

    (1988)
  • S. Dutta et al.

    Bond rating: A non-conservative application of neural networks

  • G. Hinton

    Connectionist learning procedures

    (1987)
  • C.C. Klimisaukas et al.

    Neural Computing

    (1989)
  • A.N. Refenes

    Constructive learning and its application to currency exchange rate prediction

  • A.N. Refenes et al.

    Neural network applications in financial asset management

    Neural Computing & Applications Journal

    (1993)
  • A. Refenes et al.

    Currency exchange rate prediction and neural network design strategies

    Neural Computing & Applications Journal

    (1991)
  • A.N. Refenes et al.

    Managing exchange rate prediction strategies with neural networks

There are more references available in the full text version of this article.

Cited by (0)

View full text