Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants

Rasika Yatigammana, Shelton Peiris, Richard Gerlach and David Allen
Additional contact information
Rasika Yatigammana: Central Bank of Sri-Lanka, Colombo 01, Sri Lanka
Shelton Peiris: School of Mathematics and Statistics, The University of Sydney, Sydney 2006, Australia
Richard Gerlach: Discipline of Business Analytics, The University of Sydney, Sydney 2006, Australia

Risks, 2018, vol. 6, issue 2, 1-22

Abstract: The direction of price movements are analysed under an ordered probit framework, recognising the importance of accounting for discreteness in price changes. By extending the work of Hausman et al. (1972) and Yang and Parwada (2012),This paper focuses on improving the forecast performance of the model while infusing a more practical perspective by enhancing flexibility. This is achieved by extending the existing framework to generate short term multi period ahead forecasts for better decision making, whilst considering the serial dependence structure. This approach enhances the flexibility and adaptability of the model to future price changes, particularly targeting risk minimisation. Empirical evidence is provided, based on seven stocks listed on the Australian Securities Exchange (ASX). The prediction success varies between 78 and 91 per cent for in-sample and out-of-sample forecasts for both the short term and long term.

Keywords: ordered probit; stock prices; auto-regressive; multi-step ahead forecasts (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-9091/6/2/52/pdf (application/pdf)
https://www.mdpi.com/2227-9091/6/2/52/ (text/html)

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:gam:jrisks:v:6:y:2018:i:2:p:52-:d:144971

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().