Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods
利用贝叶斯变点检测在线学习订单流与市场冲击AI 解读 ↓
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利用贝叶斯变点检测在线学习订单流与市场冲击
- 研究问题
- 订单拆分导致持续买卖方向和不同订单流状态时，如何实时识别状态变化，并改善订单流及市场冲击的在线预测？
- 方法与思路
- 作者提出采用得分驱动更新的贝叶斯在线变点检测，在各状态内允许时间相关和时变参数，并使用 NASDAQ 数据进行经验分析。
- 材料陈述的贡献
- 摘要称模型较状态内独立同分布模型具有更好的样本外预测和残差表现，且引入状态信息能改善订单流与市场冲击预测。
- 阅读关注点
- 应核对状态内相关与变点的区分、在线信息集、预测基准和评估指标，以及价格对时间和成交量的凹性如何与大单执行联系。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明样本期间、数据频率和改进幅度，预测优势仅为所述应用的结论。
原文摘要
Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.