Empirical Study of Market Impact Conditional on Order-Flow Imbalance
This research addresses the need for accurate market impact models for practitioners to estimate transaction costs and optimize trading strategies, and for regulators to assess systemic risk, though it is incremental in applying machine learning to an existing financial problem.
The study tackled the problem of understanding price impact in equity markets by empirically confirming that price impact grows linearly with order-flow imbalance for small signed order-flows, and implemented a machine learning algorithm that surpasses traditional statistical approaches in forecasting market impact.
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.