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.
Highlights
There has been a significant growth in market micro-structure research, which is concerned with the study of the underlying process that translates the latent demands of investors into transaction prices and volumes (Madhavan 2000)
Gurland et al (1960) developed it further and later it was introduced into the social sciences by McKelvey and Zavoina (1975), which became an analytical tool in the financial market security price dynamics of micro-structure research
Autoregressive integrated moving average (ARIMA) models of order (p,d,q) or ARIMA (p,d,q) models are used tomodel the autocorrelation in a time series and are used to predict behaviour based on past values alone
Summary
There has been a significant growth in market micro-structure research, which is concerned with the study of the underlying process that translates the latent demands of investors into transaction prices and volumes (Madhavan 2000). Gurland et al (1960) developed it further and later it was introduced into the social sciences by McKelvey and Zavoina (1975), which became an analytical tool in the financial market security price dynamics of micro-structure research This could be used to quantify the effects of various factors on stock price movements, whilst accounting for discreteness in price changes and the irregular spacing of trades. Sign forecasts are subsequently generated, based on those predicted regressor values, rather than on observed values and the estimated coefficients of the ordered probit model These prediction results are compared with those of the existing literature. The primary motivation of this paper is to introduce a method to enhance the flexibility and adaptability of the ordered probit model to generate multi-step ahead forecasts of stock price changes.
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