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Risks 2018, 6(2), 52;

Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants

Central Bank of Sri-Lanka, Colombo 01, Sri Lanka
School of Mathematics and Statistics, The University of Sydney, Sydney 2006, Australia
Department of Statistics, The University of Colombo, Colombo 03, Sri-Lanka
Discipline of Business Analytics, The University of Sydney, Sydney 2006, Australia
Department of Finance, Asia University, Taichung 41354, Taiwan
School of Business and Law, Edith Cowan University, Joondalup 6027, Australia
Author to whom correspondence should be addressed.
Received: 16 March 2018 / Revised: 25 April 2018 / Accepted: 1 May 2018 / Published: 7 May 2018
(This article belongs to the Special Issue Computational Methods for Risk Management in Economics and Finance)
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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. View Full-Text
Keywords: ordered probit; stock prices; auto-regressive; multi-step ahead forecasts ordered probit; stock prices; auto-regressive; multi-step ahead forecasts

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Yatigammana, R.; Peiris, S.; Gerlach, R.; Allen, D.E. Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants. Risks 2018, 6, 52.

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