虚假交易风险、成交量信息含量与价格发现--基于A股高频数据的经验证据

摘要：

本文基于A股逐笔高频数据构造背包式订单流闭合度指标(KnapOFC)，并通过两阶段框架识别在短窗口内同时呈现成交活跃异常、买卖订单高度匹配与价格发现弱化特征的可疑订单流窗口(NOC事件),以刻画虚假交易风险在公开市场数据中的间接痕迹.研究发现:第1.A股市场存在触发率较低但稳定可识别的NOC窗口，且在科创板和创业板相对更为活跃;第2.相对于安慰剂窗口、高成交非NOC窗口与成交爆发匹配窗口，NOC窗口的未来30min价格影响及事件后短期已实现波动率均显著偏低，表明异常成交量未表现出与其成交规模相匹配的信息含量;第3.NOC窗口在订单级具有更高的KnapOFC、更广的匹配覆盖度与更低的残余不平衡，并明显区别于高撤单、订单不平衡与尾市异动等传统异常形态.本文从成交量信息含量与价格发现视角，为高频数据条件下虚假交易风险的间接检测提供了市场侧证据，并为将订单级匹配结构纳入异常交易监测提供了可操作的指标工具.

Using tick-by-tick high-frequency data from Chinas A-share market, this paper constructs a knapsack-based order-flow closure measure, KnapOFC, and develops a two- stage framework to identily suspicious order-flow windows that simultaneously exhibit abnormal trading activity, high buy-sell order matching, and weakened price discovery, referring to as NOC events. These events are used to capture indirect market-level traces of false trading risk in publicly observable high-fre-quency data. The main findings are as follows. First, NOC windows exist in the A-share market with a low but stable occurrence rate, and are relatively more active on the STAR Market and ChiNext. Second, compared with placebo windows, highvolume non-NOC windows, and volume-burst-matched windows, NOC windows exhibit significantly lower future 30 min price impact and lower short-term realized volatility after the event, suggesting that abnormal trading volume does not carry information content commensurate with its trading scale. Third, at the order level, NOC windows display higher KnapOFC, broader matching coverage. and lower residual imbalance, and are clearly distinguishable from traditional abnormal trading pattemns such as high cancellation, order imbalance, and end-of-day price movements. From the perspective of trading-volume information content and price discovery, this paper provides market-side evidence for the indirect detection of false trading risk using high-frequency data, and offers an operational indicator for incorporating order-level matching structures into abnormal trading surveillance.

作者：

姚远

Yao Yuan

机构地区：

河南大学商学院

引用本文：

姚远.虚假交易风险、成交量信息含量与价格发现--基于A股高频数据的经验证据[J].河南师范大学学报(自然科学版),2026,54(5):1-10.(Yao Yuan.Wash-trading risk,volume informativeness,and price discovery:evidence from high-frequency data in China's A-Share market[J].Joumal of Henan Normal University (Natural Science Edition).2026.54(5):1-10.DOI:10.16366/j.cnki.1000-2367.2026.05.30.0001.)

基金：

国家社会科学基金

关键词：

虚假交易风险;成交量信息含量;价格发现;背包式订单流闭合度;逐笔高频数据

wash-trading risk; volume informativeness; price discovery; knapsack-based order-flow closure; trade-by-trade high-frequency data

分类号：

F832.51;F830.91

虚假交易风险、成交量信息含量与价格发现--基于A股高频数据的经验证据.pdf