An Integrated Approach For Stock Price Forecasting

Alvaro Veiga and Gustavo Santos Raposo

No 347, Computing in Economics and Finance 2005 from Society for Computational Economics

Abstract: This article faces the problem of stock price forecasting based on an integrated approach in which the modeling of high frequency financial data (duration, volume and bid-ask spread) uses a contemporaneous ordered probit model – the price changes (measured in numbers of ticks) are the interest variable. Here, the formulation introduced by Raposo and Veiga (2004) – EMACM – is used in order to capture the dynamic that high frequency variables present, and its forecasting function is taken as proxy to the contemporaneous information necessary to the price model being created. In that context, the main purpose of the article is to compare the performance of the current model against NAIVE, testing the use of real data instead of the results of the forecasting function.

Keywords: High frequency data; ordered probit model; EMACM; nonlinear time series (search for similar items in EconPapers)
JEL-codes: C53 (search for similar items in EconPapers)
Date: 2005-11-11
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf5:347

Access Statistics for this paper

More papers in Computing in Economics and Finance 2005 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().