We study the quoting activity of market makers in relation to trading, liquidity, and expected returns. Empirically, we find larger quote-to-trade (QT) ratios in small, illiquid, or neglected firms, yet large QT ratios are associated with low expected returns. The last result is driven by quotes, not by trades. We propose a model of quoting activity consistent with these facts. In equilibrium, market makers monitor the market faster (and thus increase the QT ratio) in neglected, difficult-to-understand stocks. They also monitor faster when their clients are more precisely informed, which reduces mispricing and lowers expected returns.

This paper examines the relation between the Canadian dollar/US dollar (CAD) exchange rate and foreign exchange order flow employing a novel data set on CAD order flow over the period 1994–2005. We investigate empirically the predictive information content and the determinants of order flow. The results suggest that order flow has strong out-of-sample predictive power for CAD returns, yielding significant market timing ability and tangible economic gains in a stylized dynamic asset allocation context. In terms of its determinants, order flow appears to reflect not only the menu of macroeconomic variables typically suggested in the literature but is also closely related to commodity price fluctuations, as expected from a ‘commodity currency’.

We link equity and treasury bond markets via an informational channel. When macroeconomic state shifts are more probable, informed traders are more likely to have valid signals about fundamentals, so that uninformed traders are less willing to trade against informed ones. This implies low volume and high volatility, that is, a high volatility–volume ratio (VVR). Central banks react to state shifts, but their actions are uncertain. Therefore, a higher state shift likelihood implies larger bond risk premia. These arguments together imply that VVR should positively predict bond excess returns. We empirically test and confirm this prediction, both in- and out-of-sample.