Joey Wenling Yang

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

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.

Order Size, Order Imbalance and the Volatility–Volume Relation in a Bull Versus a Bear Market

This paper revisits the volume–volatility relationship documented in Chan and Fong (2000) in a bull versus a bear market by using data from the period surrounding the subprime crisis in 2008. The relationship is predicted to be asymmetric because of the greater cost of short positions, the lack of liquidity and the differing trading strategies adopted in a bearish market. Our analysis shows the trading patterns in the bull and bear markets are different. Particularly, in a falling market characterised by higher volatility and lower liquidity, the frequency of order placement has doubled but orders are smaller in size. This provides evidence of investors’ shift of trading strategy in relation to the status change of the market. Furthermore, we find evidence to support the proposition that volume is more informative in a bear market than in a bull market.

Predicting Stock Price Movements: An Ordered Probit Analysis on the ASX

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 the cases the system successfully predicts the direction of the subsequent price change.

An Examination of the Role of Time and its Impact on Price Revision

We consider a new class of time series models (introduced by Engle & Russell 1998) used in statistical applications in finance. These models treat the time between events (durations) as a stochastic process and the corresponding durations are modelled using a theory similar to that of autoregressive processes On a sample of six stocks listed on the ASX, we find evidence in support of the important role that both the deterministic and stochastic components of time play in both our quote revision and signed trade equations, and it is the stochastic indicator of time that has a greater influence than the time-of day periodicities.