AIAI/ECFI Seminar-Thursday 4th September 2025 by Visiting Speaker: Prof. Mihai Cucuringu

Title: Cross-impact, decomposition, and co-occurrence of order flow in equity limit order booksAbstract: We investigate the impact of order flow imbalance (OFI) on price movements in equity markets in a multi-asset setting. We propose a systematic approach for combining OFIs at the top levels of the limit order book into an integrated OFI variable which better explains price impact, compared to the best-level OFI. We show that once the information from multiple levels is integrated into OFI, multi-asset models with cross-impact do not provide additional explanatory power for contemporaneous impact compared to a sparse model without cross-impact terms. On the other hand, we show that lagged cross-asset OFIs do improve the forecasting of future returns, and establish that this lagged cross-impact mainly manifests at short-term horizons and decays rapidly in time. Incorporating knowledge about order book event types leads to a decomposed OFI which attains significant improvement in a forward-looking predictive scenario.Furthermore, we demonstrate that the time proximity of high-frequency trades contains a salient signal. We propose a method to classify every trade into five types, based on its proximity with other trades in the market, within a short period of time, ranging from 50 microseconds to 50 milliseconds. By means of a suitably defined normalized order imbalance associated to each type of trade, which we denote as conditional order imbalance (COI), we investigate the price impact of the decomposed trade flows. Our empirical findings indicate strong positive correlations between contemporaneous returns and COIs. In terms of predictability, we document that associations with future returns are positive for COIs of trades which are isolated from trades of stocks other than themselves, and negative otherwise. Furthermore, trading strategies developed using COIs achieve competitive returns and Sharpe Ratios, in an extensive experimental setup on a universe of over 450 stocks, for a period of three years.  

Bio: Mihai Cucuringu is a Professor in the Department of Mathematics at UCLA. Previously, he was an Associate Professor in the Department of Statistics, an Affiliate Faculty in the Mathematical and Computational Finance group at the Mathematical Institute at University of Oxford, and a Turing Fellow at The Alan Turing Institute in London. He finished his Ph.D. in Applied Mathematics at Princeton University in 2012. His research pertains to the development and mathematical analysis of algorithms that extract information from massive noisy data sets, network analysis and certain inverse problems on graphs, such as clustering and ranking, with an eye towards extracting structure from time-dependent data which can be subsequently leveraged for prediction. His research interests in finance focus on statistical arbitrage, machine-learning for asset pricing, market microstructure, and synthetic data generation.