Probability of an incoming order signal

Abstract

Taking into account that transaction prices are realized at the bid or the ask price, we propose a probabilistic neural network model and a Bayesian rule to predict the incoming order signal of a stock and its probability using the buy–sell trade indicator or trade direction sign. We consider that if there is any private information to be inferred from trade, agents can use a trade equation to form an expectation about the future trade based on the trade and quote revision history. In addition, we use it to analyse the classification and forecasting capacity of various discrete regression and probabilistic neural network models to estimate the probability of an incoming order signal by means of statistical and economic criteria. Our results indicate that the probabilistic neural network classifies and predicts slightly better than linear, Probit and MLP models for short forecast horizons, among other statistical criteria, and reversed trades with respect to the economic assessment of the negotiation for both short and long forecast horizons.