The Quanted Round-up is a curated summary that covers relevant research on key topics in quantitative financial decision-making.

Highlights

This edition looks at how modern markets reflect the behaviours and limitations of the participants within them. Whether through distorted incentives, constrained attention, or learned manipulation, the research reveals how much of today’s market structure emerges not from design, but from interaction—raising tough questions for anyone modelling price formation or execution.

Generating realistic metaorders from public data

Guillaume Maitrier, Grégoire Loeper & Jean-Philippe Bouchaud

This paper introduces a novel algorithm for generating realistic metaorders from public trade data, addressing a longstanding challenge in price impact research that has traditionally relied on proprietary datasets. Our method effectively recovers all established stylized facts of metaorders impact, such as the Square Root Law, the concave profile during metaorder execution, and the post- execution decay. This algorithm not only overcomes the dependence on proprietary data, a major barrier to research reproducibility, but also enables the creation of larger and more robust datasets that may increase the quality of empirical studies. Our findings strongly suggest that average realized short-term price impact is not due to information revelation (as in the Kyle framework) but has a mechanical origin which could explain the universality of the Square Root Law.

Too many irons in the fire: The impact of limited institutional attention on market microstructure and efficiency

Hao Jiang, Yong Ma & Tianyang Wang

This paper presents an in-depth exploration, both empirically and theoretically, of how institutional attention impacts market microstructure. Our innovative theoretical model incorporates an information processing constraint into the dynamic strategic trading framework. The model predicts a trade-off where increased institutional attention enhances price informativeness at the expense of market liquidity, and suggests that the unmonetized portion of institutional investors’ information advantage significantly influences the effect of public information about an asset’s fundamental value on market microstructure. Additionally, our findings are substantiated through rigorous empirical analysis.

The Derivative Payoff Bias

A significant fraction of U.S. equity index derivatives expire "a.m. " on the 3rd Friday of each month via constituent stocks' opening trade price. We show these prices are biased upwards since the advent of overnight trading in the early 2000s. Equity prices drift up from Thursday close to 3rd Friday open and revert at the point derivative payoffs are calculated. As a result, equity futures and call option payoffs are biased upwards, while put option payoffs are biased downwards, generating a wealth transfer of ~$3.5 billion per year in S&P 500 index options alone. Exploring explanations, we show that a novel channel ("charm'') originating from market makers' hedging practices represents a plausible explanation for the derivative payoff bias.

FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models

Yanlong Wang, Jian Xu, Tiantian Gao, Hongkang Zhang, Shao- Lun Huang, Danny Dongning Sun & Xiao-Ping Zhang

Despite the growing attention to time series forecasting in recent years, many studies have proposed various solutions to address the challenges encountered in time series prediction, aiming to improve forecasting performance. However, effectively applying these time series forecasting models to the field of financial asset pricing remains a challenging issue. There is still a need for a bridge to connect cutting-edge time series forecasting models with financial asset pricing. To bridge this gap, we have undertaken the following efforts: 1) We constructed three datasets from the financial domain; 2) We selected over ten time series forecasting models from recent studies and validated their performance in financial time series; 3) We developed new metrics, msIC and msIR, in addition to MSE and MAE, to showcase the time series correlation captured by the models; 4) We designed financial-specific tasks for these three datasets and assessed the practical performance and application potential of these forecasting models in important financial problems. We hope the developed new evaluation suite, FinTSBridge, can provide valuable insights into the effectiveness and robustness of advanced forecasting models in finanical domains.

The complex nature of financial market microstructure: the case of a stock market crash

Feng Shi, John Paul Broussard & G. Geoffrey Booth

This paper uses multivariate Hawkes processes to model the transactions behavior of the US stock market as measured by the 30 Dow Jones Industrial Average individual stocks before, during and after the 36-min May 6, 2010, Flash Crash. The basis for our analysis is the excitation matrix, which describes a complex network of interactions among the stocks. Using high-frequency transactions data, we find strong evidence of self- and asymmetrically cross-induced contagion and the presence of fragmented trading venues. Our findings have implications for stock trading and corresponding risk management strategies as well as stock market microstructure design.

Spoofing and Manipulating Order Books with Learning Algorithms

Álvaro Cartea, Patrick Chang & Gabriel García-Arenas

We propose a dynamic model of the limit order book to derive conditions to test if a trading algorithm will learn to manipulate the order book. Our results show that as a market maker becomes more tolerant to bearing inventory risk, the learning algorithm will find optimal strategies that manipulate the book more frequently. Manipulation occurs to induce mean reversion in inventory to an optimal level and to execute round-trip trades with limit orders at a higher probability than was otherwise likely to occur; spoofing is a special case when the market maker prefers that manipulative limit orders are not filled. The conditions are tested with order book data from Nasdaq and we show that market conditions are conducive for an algorithm to learn to manipulate the order book. Finally, when two market makers use learning algorithms to trade, their algorithms can learn to coordinate their manipulation.

Fredholm Approach to Nonlinear Propagator Models

Eduardo Abi Jaber, Alessandro Bondi, Nathan De Carvalho, Eyal Neuman & Sturmius Tuschmann

We formulate and solve an optimal trading problem with alpha signals, where transactions induce a nonlinear transient price impact described by a general propagator model, including power-law decay. Using a variational approach, we demonstrate that the optimal trading strategy satisfies a nonlinear stochastic Fredholm equation with both forward and backward coefficients. We prove the existence and uniqueness of the solution under a monotonicity condition reflecting the nonlinearity of the price impact. Moreover, we derive an existence result for the optimal strategy beyond this condition when the underlying probability space is countable. In addition, we introduce a novel iterative scheme and establish its convergence to the optimal trading strategy. Finally, we provide a numerical implementation of the scheme that illustrates its convergence, stability, and the effects of concavity on optimal execution strategies under exponential and power-law decay.

References

1. FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models. March 2025. Wang, Y.; Xu, J.; Gao, T.; Zhang, H.; Huang, S.; Sun, D.D. and Zhang, X. Available at arXiv: https://arxiv.org/abs/2503.06928

2. Fredholm Approach to Nonlinear Propagator Models. March 2025. Jaber, E.A.; Bondi, A.; De Carvalho, N; Neuman, E and Tuschmann, S. Available at arXiv: https://arxiv.org/abs/2503.04323

3. Generating realistic metaorders from public data. April 2025. Maitrier, G.; Loeper, G. and Bouchaud, J. Available at arXiv: https://arxiv.org/abs/2503.18199

4. Spoofing and Manipulating Order Books with Learning Algorithms. November 2023. Cartea, Á.; and Chang, P. and Gabriel, G. Available at SSRN: http://dx.doi.org/10.2139/ssrn.4639959

5. The complex nature of financial market microstructure: the case of a stock market crash. January 2022. Shi, F.; Broussard, J.P. and Booth, G.G. Journal of Economic Interaction and Coordination, 20(1): 1-40. Available at Springer: https://doi.org/10.1007/s11403-021-00343-4

6. The Derivative Payoff Bias. September 2023. Baltussen, G.; Terstegge, J. and Whelan, P. Available at SSRN: http://dx.doi.org/10.2139/ssrn.4562800

7. Too many irons in the fire: The impact of limited institutional attention on market microstructure and efficiency. March 2025. Jiang, H.; Ma, Y. and Wang, T. Journal of Financial Markets, 73: 100969. Available at Elsevier: https://doi.org/10.1016/j.finmar.2025.100969