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Cross-Sectional Variation of Intraday Liquidity, Cross-Impact, and their Effect on Portfolio Execution

Author

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  • Seungki Min
  • Costis Maglaras
  • Ciamac C. Moallemi

Abstract

The composition of natural liquidity has been changing over time. An analysis of intraday volumes for the S&P500 constituent stocks illustrates that (i) volume surprises, i.e., deviations from their respective forecasts, are correlated across stocks, and (ii) this correlation increases during the last few hours of the trading session. These observations could be attributed, in part, to the prevalence of portfolio trading activity that is implicit in the growth of ETF, passive and systematic investment strategies; and, to the increased trading intensity of such strategies towards the end of the trading session, e.g., due to execution of mutual fund inflows/outflows that are benchmarked to the closing price on each day. In this paper, we investigate the consequences of such portfolio liquidity on price impact and portfolio execution. We derive a linear cross-asset market impact from a stylized model that explicitly captures the fact that a certain fraction of natural liquidity providers only trade portfolios of stocks whenever they choose to execute. We find that due to cross-impact and its intraday variation, it is optimal for a risk-neutral, cost minimizing liquidator to execute a portfolio of orders in a coupled manner, as opposed to a separable VWAP-like execution that is often assumed. The optimal schedule couples the execution of the various orders so as to be able to take advantage of increased portfolio liquidity towards the end of the day. A worst case analysis shows that the potential cost reduction from this optimized execution schedule over the separable approach can be as high as 6% for plausible model parameters. Finally, we discuss how to estimate cross-sectional price impact if one had a dataset of realized portfolio transaction records that exploits the low-rank structure of its coefficient matrix suggested by our analysis.

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  • Seungki Min & Costis Maglaras & Ciamac C. Moallemi, 2018. "Cross-Sectional Variation of Intraday Liquidity, Cross-Impact, and their Effect on Portfolio Execution," Papers 1811.05524, arXiv.org.
    • Handle: RePEc:arx:papers:1811.05524
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    Cited by:

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    2. Natascha Hey & Iacopo Mastromatteo & Johannes Muhle-Karbe & Kevin Webster, 2025. "Trading with Concave Price Impact and Impact Decay—Theory and Evidence," Operations Research, INFORMS, vol. 73(3), pages 1230-1247, May.
    3. Johannes Muhle-Karbe & Zexin Wang & Kevin Webster, 2024. "Stochastic Liquidity as a Proxy for Nonlinear Price Impact," Operations Research, INFORMS, vol. 72(2), pages 444-458, March.
    4. Mihai Cucuringu & Kang Li & Chao Zhang, 2025. "Forecasting Intraday Volume in Equity Markets with Machine Learning," Papers 2505.08180, arXiv.org.
    5. Masamitsu Ohnishi & Makoto Shimoshimizu, 2022. "Optimal Pair–Trade Execution with Generalized Cross–Impact," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(2), pages 253-289, June.

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