Traders can reduce search costs in dealership markets by entering relationships with dealers. However, dealers draw little informational benefit from these relationships in Treasury markets, due to low risk and information asymmetry. We investigate the extent, duration, effects on pricing, and potential benefits of client–dealer relationships. We find that relationship strength leads to higher execution costs for clients, more so during stressed market conditions but less so in the presence of information asymmetry and when trading in corporate bonds. Relationship traders are compensated with immediacy at times when search is costly.

Recent local price growth explains differences in search behavior across prospective homebuyers. Those experiencing higher growth in their postcode of residence search more broadly across locations and house characteristics, without changing attention devoted to individual sales listings, and have shorter search duration. Effects are stronger for homeowners, in particular those living in less wealthy areas and looking for a new primary residence. We use reduced‐form analysis and a quantitative equilibrium model to show that the expansion of search breadth translates into widespread spillovers onto house sales prices and inventories of listings across postcodes within a metropolitan area.

While economic forecasting is ubiquitous within the industry, its role in the trading process has received little attention in the literature. We examine how economist forecasts are related to trading activity in the OTC treasury bond market at the participant level. Consistent with models of heterogeneous opinions, we show that the forecasting economists employing institution places a disproportionately large reliance on the forecast. There is pervasive evidence that this reliance is asymmetric. Only forecasts which imply a fall in future treasury bond prices are associated with an abnormal trading reaction consistent with the forecast. Reference dependence and loss aversion offer one possible explanation for this asymmetric trading response.

This study employs firm-specific announcements as a proxy for information flows and investigates the information–volatility relation using high-frequency data from the Australian Stock Exchange. Our analysis reveals a positive and significant impact of the arrival rate of the selected news variable on the conditional variance of stock returns, even after controlling for the potential effects of trading volume and high opening volatility. Furthermore, the inclusion of the news variable in the conditional variance equation of the generalized autoregressive conditional heteroscedastic model also reduces volatility persistence, especially with intraday data. Combined with the evidence that news arrivals display a very strong pattern of autocorrelation, our results are consistent with the Mixture of Distribution Hypothesis, which attributes conditional heteroscedasticity of stock returns to time-dependence in the news arrival process.