Title

Modelling and forecasting stock price movements with serially dependent determinants

Document Type

Journal Article

Publisher

MDPI

School

School of Business and Law

Comments

Originally published as:

Yatigammana, R., Peiris, S., Gerlach, R., & Allen, D. E. (2018). Modelling and forecasting stock price movements with serially dependent determinants. Risks, 6(2), 52.

Original article available here.

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.

DOI

10.3390/risks6020052

Access Rights

Not free_to_read

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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