Durations, volume and the prediction of financial returns in transaction time

Traditional microstructural theories of asset pricing emphasize the role of volume as a trend indicator. With the availability of large transaction data sets, one has started recently to incorporate more information of the trades, such as the time between trades, to describe the multivariate dynamics of transactions. Without knowing a priori the relation between the observed components of a trade—price, duration between trades, and volume—one may follow the principle of 'letting the data speak for themselves'. The goal of this paper is to evaluate the informational content of both volume and durations to predict transaction returns using explorative non-parametric methods. The empirical results for transaction data of IBM stock prices confirm the role of volume as a trend indicator. After a sell (buy) expected returns are decreasing (increasing) with volume and increasing (decreasing) with durations. A.forecasting exercise shows that the superiority of the non-parametric model over simple parameterizations carries over to out-of-sample prediction.