Abstract
The reliability of trade classification algorithms that identify the liquidity demander in financial markets transaction data has been questioned due to an increase in the frequency of quote changes. Hence, this paper proposes a new method. While established algorithms rely on an ad hoc assignment of trades to quotes, the proposed full-information (FI) algorithm actively searches for the quote that matches a trade. The FI algorithm outperforms the existing ones, particularly at low timestamp precision: For data timestamped at seconds misclassification is reduced by half compared to the popular Lee-Ready algorithm. These improvements also carry over into empirical applications such as the estimation of transaction costs. The recently proposed interpolation method and bulk volume classification algorithm do not offer improvements.
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