Forecasting the S&P 500 Index with Circuit Breakers

Abstract

The purpose of this paper is to develop a Bayesian model of the S&P 500 stock index in the presence of a circuit breaker rule that would be useful to traders who wish to update positions when information is limited because of a market trading halt. We assume that the market index is distributed by a Poisson process with an unknown parameter. First, using a conjugate Gamma prior probability distribution, we can revise the distribution of the prior distribution, to get an updated Gamma posterior distribution. Second, we calculate the market index’s truncated posterior and predictive distributions in the presence of circuit breakers. Third, our predicted index’s values (during the activation of the circuit breakers that results in a fifteen-minute trading halt) are demonstrated by numerical examples. Thus, investors would be able to adjust, their long/short positions, when market information is temporarily unavailable.