Modeling the behavior of the S&P 500 index: a neural network approach

Abstract

The October 1987 stock market crash challenged the prevailing financial models of a random walk and led to the emergence of a new and competing model of stock price time series. This new approach supports a nonrandom underlying structure and is labeled chaotic dynamics. If a neural network can be constructed which determines market prices better than the random walk model, it would support those who claim that they have found statistical evidence that a chaotic dynamics structure underlies the market. This paper constructs a neural network which lends support to the deterministic paradigm.<>