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Predicting stock price movements: an ordered probit analysis on the Australian Securities Exchange

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  • Joey Wenling Yang
  • Jerry Parwada

Abstract

Using stocks from a wide range of industry sectors on the Australian Securities Exchange, this paper examines the conditional distribution of intra-day stock prices and predicts the direction of the next price change in an ordered-probit-GARCH framework that accounts for the discreteness of prices. The analysis also incorporates the endogeneity of the time between trades in an ACD framework. Other elements considered include depth, trade imbalance, and volume. The results show that trade imbalance has a positive effect on the probability of price change. Durations have a negative effect. In-sample and out-of-sample forecasting analyses reveal that, in 71% of cases, the system successfully predicts the direction of the subsequent price change.

Suggested Citation

  • Joey Wenling Yang & Jerry Parwada, 2012. "Predicting stock price movements: an ordered probit analysis on the Australian Securities Exchange," Quantitative Finance, Taylor & Francis Journals, vol. 12(5), pages 791-804, October.
    • Handle: RePEc:taf:quantf:v:12:y:2012:i:5:p:791-804
    DOI: 10.1080/14697688.2010.494612
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    Cited by:

    1. Dimitrakopoulos, Stefanos & Tsionas, Mike, 2019. "Ordinal-response GARCH models for transaction data: A forecasting exercise," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1273-1287.
    2. Dimitrakopoulos, Stefanos & Dey, Dipak K., 2017. "Discrete-response state space models with conditional heteroscedasticity: An application to forecasting the federal funds rate target," Economics Letters, Elsevier, vol. 154(C), pages 20-23.
    3. Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020. "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper 103250, University Library of Munich, Germany, revised 01 Oct 2020.
    4. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos & Verousis, Thanos, 2020. "A conditional fuzzy inference approach in forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 196-216.
    5. Rasika Yatigammana & Shelton Peiris & Richard Gerlach & David Edmund Allen, 2018. "Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants," Risks, MDPI, vol. 6(2), pages 1-22, May.
    6. Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).

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