Price informativeness and predictability: how liquidity can help

Abstract

Information asymmetry and liquidity concentration has been widely discussed in literatures. This study shows how liquidity influences not only forecasting performances of term structure estimation, but also information transmission and price adjustment across markets. Our analysis helps understanding how extreme market movements affect one another. This study examines, and provides a rationale for incorporating, liquidity in estimating term structure. Forecasting performance can be greatly enhanced when conditioning on trading liquidity. It reduces information asymmetry in the sense of Easley and O’Hara (2004) and Burlacu et al. (2007). We adopt a time series forecasting model following Diebold and Li (2006) to compare behaviour of forecasted price errors. Our findings indicate that forecasted price errors in markets with less depth would influence those with more. Information asymmetry induces volatile trading first and then price adjustment is transmitted to another market due to insufficient market depth. Cross-market price adjustment could be as much as 21 bps on average. Compared with previous studies, our results establish a valid reason to condition on liquidity when forecasting prices.

Notes

¹ Liquidity in the Treasury markets has been the topic of numerous studies. See for example, Sarig and Warga (1989), Amihud and Mendelson (1991), Warga (1992), Daves and Ehrhardt (1993), Kamara (1994), Elton and Green (1998), Fleming (2002, 2003), Strebulaev (2002), Krishnamurthy (2002) and Goldreich et al. (2005).

² See Darbha (2004) and Diaz et al. (2006), among others.

³ The AIM measure is obtained directly from a Rational Expectations (RE) model with multiple securities and many sources of uncertainty. This model is essentially a generalization of the Grossman and Stiglitz (1980) model, which focuses on an economy where some investors are more informed on the future distributions of a security's returns than others.

⁴ We have used alternative liquidity measures such as the inverse of average bid-ask spreads and average number of quotes or trades. Fitted results are not much different from using trading volume as the proxy for liquidity. But using trading volume as the proxy produces the best performance in consistency and accuracy, especially in periods of extremely uneven distribution of liquidity across markets for various issues.