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.

由于报价变化频率的增加，识别金融市场交易数据中流动性需求者的交易分类算法的可靠性受到质疑。因此，本文提出了一种新方法。虽然已建立的算法依赖于将交易临时分配给报价，但建议的全信息 (FI) 算法会主动搜索与交易匹配的报价。FI 算法优于现有算法，特别是在时间戳精度较低的情况下：对于以秒为时间戳的数据，与流行的 Lee-Ready 算法相比，错误分类减少了一半。这些改进也延续到了经验应用中，例如交易成本的估计。最近提出的插值方法和体积分类算法没有提供改进。