Please use this identifier to cite or link to this item:
Title: Probability of an incoming order signal
Authors: Pérez Rodríguez, Jorge Vicente 
UNESCO Clasification: 530406 Dinero y operaciones bancarias
Keywords: Buy-sell trade indicator
Qualitative variable modes
Probabilistic neural network model
Operaciones financieras
Issue Date: 2011
Journal: Quantitative Finance 
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.
ISSN: 1469-7688
DOI: 10.1080/14697681003685555
Source: Quantitative Finance [ISSN 1469-7688], v. 11 (6), p. 901-916
Appears in Collections:Artículos
Show full item record

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.