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Estimating Equity Market Impact from Order-Flow Imbalance

Article arXiv papers · Author: Anastasia Bugaenko
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Summary

This study examines factors that shape equity market liquidity, focusing on how signed order flow relates to price impact. It connects Kyle-style theoretical models of trading and liquidity to patterns in publicly available trade and quote data. The reported empirical finding is that, for small signed order flows, price impact increases approximately linearly with order-flow imbalance.

The authors also apply machine learning to forecast market impact from signed order flow and report better predictive accuracy than traditional statistical approaches. Such estimates can help traders anticipate transaction costs before execution and assess execution quality afterward. The document gives no dataset details, model specifications, forecast metrics, or evidence about performance beyond the stated comparison. Its findings are therefore best treated as a description of the study’s reported results, not as proof that a particular model will generalize across securities or market conditions.

Key ideas

  • Price impact is presented as a statistical measure of market liquidity.
  • For small signed order flows, the study reports a roughly linear relationship between imbalance and price impact.
  • The authors use machine learning to forecast impact from signed order flow.
  • Impact estimates can inform pre-trade cost estimates and post-trade execution assessment.
  • The document does not provide model details or quantitative forecast results.

Tags

EquitiesMarket microstructureMachine learningExecutionStatistics
Full text
# Empirical Study of Market Impact Conditional on Order-Flow Imbalance


# Empirical Study of Market Impact Conditional on Order-Flow Imbalance









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.