订单流不平衡条件下的市场冲击实证研究
Empirical Study of Market Impact Conditional on Order-Flow Imbalance
摘要：
在本研究中，我们实证研究了影响股票市场流动性的关键驱动因素。我们说明了理论模型，如凯尔的模型，在金融市场中的代理人的相互作用，是如何与公开的交易和报价数据中观察到的现象。具体来说，我们证实，对于小签署的订单流，价格影响线性增长的订单流不平衡的增加。此外，我们还实施了一种机器学习算法来预测给定签名订单流的市场冲击。我们的研究结果表明，机器学习模型可以用于估计金融变量;这种学习算法的预测准确性可以超过传统统计方法的性能。了解价格影响的决定因素至关重要，原因有几个。从理论上讲，对影响进行建模可以提供流动性的统计衡量。从业者采用影响模型作为交易前的工具，以估计预期的交易成本，并优化其战略的执行。这进一步作为交易后的估值基准，因为次优执行可能会显着恶化投资组合的表现。更广泛地说，价格影响反映了市场流动性的平衡。这对监管机构至关重要，因为它全面解释了市场设计与系统性风险之间的相互关系，使监管机构能够设计更稳定和更有效的市场。
英文摘要：
In this research, we have empirically investigated the key drivers affecting liquidity in equity markets. We illustrated how theoretical models, such as Kyle's model, of agents' interplay in the financial markets, are aligned with the phenomena observed in publicly available trades and quotes data. Specifically, we confirmed that for small signed order-flows, the price impact grows linearly with increase in the order-flow imbalance. We have, further, implemented a machine learning algorithm to forecast market impact given a signed order-flow. Our findings suggest that machine learning models can be used in estimation of financial variables; and predictive accuracy of such learning algorithms can surpass the performance of traditional statistical approaches. Understanding the determinants of price impact is crucial for several reasons. From a theoretical stance, modelling the impact provides a statistical measure of liquidity. Practitioners adopt impact models as a pre-trade tool to estimate expected transaction costs and optimize the execution of their strategies. This further serves as a post-trade valuation benchmark as suboptimal execution can significantly deteriorate a portfolio performance. More broadly, the price impact reflects the balance of liquidity across markets. This is of central importance to regulators as it provides an all-encompassing explanation of the correlation between market design and systemic risk, enabling regulators to design more stable and efficient markets.
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