• lead-lag detection, dynamic network clustering, and portfolio strategies based on time-varying lead-lag structure (US equity)
• cross-asset and bipartite network models for learning stock-ETF interactions and international cross-market return forecasting
• deep learning frameworks for asset pricing with heterogeneous data sources (news, technical, fundamentals) (US/Chinese equity)
• graph-based and network-informed asset pricing; fundamentals, characteristics, and trade flow data (US equity)
• implementation-aware asset pricing: capacity constraints, liquidity limits, and the scalability of anomaly-based strategies
• market microstructure in equity and cryptocurrency markets: fragmentation, price impact, order failure, and large-scale live trading experiments
• limit order book modelling: conditional order flow imbalance, cancellation behaviour, and client order flow modelling in limit order markets
• foundational models for limit order book data, including mixture-of-experts, order clustering, and execution-aware learning architectures
• latent and rejected order flow ("invisible liquidity") and its predictive power for future returns
• probabilistic forecasting and simulation for financial time series, including GANs and flow-based generative models for prices and limit order books
• reinforcement learning and multi-agent learning for high-frequency trading and execution, including fine-tuning foundation models for trading applications
• volatility and covariance forecasting with network effects, including GNN-based spillovers and idiosyncratic jump spillovers
• factor-driven network time series models (restricted VAR, dynamic network linear models) for high-dimensional forecasting and covariance estimation
• high-dimensional regression and representation learning (including LLM embedding priors) for financial prediction
• clustering, change-point detection, and macro regime identification in financial time series and correlation networks
• tactical asset allocation with macroeconomic regime detection; temporal motifs and network predictors

Publications and preprints (finance related; all 2021-present)
Last updated: July 2026. Please see Google Scholar, arXiv, and SSRN for an up-to-date list. Green labels indicate press coverage.

1. Dayne Marcus Lopena et al., Gaussian Boson Sampling for Asset Clustering in Statistical Arbitrage Portfolios, arXiv (2026), [Press Coverage: Quantum Zeitgeist], [BibTeX] (2026)
2. Álvaro Cartea, Mihai Cucuringu, Danni Shi, Intraday Lead-Lag Relationships in Idiosyncratic Stock Returns, SSRN (2026), [BibTeX] (2026)
3. Daniel Cunha Oliveira, Kieran Wood, Stefan Zohren, Mihai Cucuringu, André Fujita Macro-aware time series forecasting via hierarchical mixed-frequency attention models, arXiv:2606.00624 (2026). [BibTeX]
4. Jakob Albers, Mihai Cucuringu, Sam Howison, Alexander Y. Shestopaloff, An Open Book: Level 4 Order Book Data from the Hyperliquid Exchange, SSRN, [BibTeX] (2026)
5. Álvaro Cartea, Mihai Cucuringu, Lei Fang, Network-Augmented Volatility Forecasting with Idiosyncratic Jump Spillovers , SSRN, [BibTeX] (2026)
6. Jing Liu, Maria Grith, Xiaowen Dong, Mihai Cucuringu, A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting , SSRN,[BibTeX] (2026)
7. Jakob Albers, Mihai Cucuringu, Sam Howison, Alexander Y. Shestopaloff, The "Neutrinos" of the Order Book: Pervasive, Weakly Interacting Order Flow and its Consequences, SSRN, [BibTeX] (2026)
8. Jakob Albers, Mihai Cucuringu, Sam Howison, Alexander Y. Shestopaloff, The Price Impact of Nothing: Rejected Orders as Predictors of Future Returns, SSRN, [BibTeX] (2026)
9. Álvaro Cartea, Mihai Cucuringu, Donggeun Kim, Learning Firm Characteristics for Asset Management, SSRN (2025), [BibTeX] (2025)
10. Álvaro Cartea, Mihai Cucuringu, Qi Jin, Jiexiu Zhu, Bottom-Up Capacity Constraints and the Limits of Anomaly Profitability, SSRN (2025), [Press Coverage: Institutional Money], [BibTeX] (2025)
11. Valentin Mohl et al., JaxMARL-HFT: GPU-Accelerated Large-Scale Multi-Agent Reinforcement Learning for High-Frequency Trading, ICAIF 2025, Association for Computing Machinery, New York, NY, USA, Best Paper Honorary Mention, [BibTeX] (2025)
12. Wanyun Zhou et al., DeltaLag: Learning Dynamic Lead-Lag Patterns in Financial Markets, ICAIF 2025, Association for Computing Machinery, New York, NY, USA, [BibTeX] (2025)
13. Nicolas Petit, Mihai Cucuringu, Álvaro Cartea, Data-Driven Trade Flow Decomposition for Exchange-Traded Funds and their Constituents, ICAIF 2025, Association for Computing Machinery, New York, NY, USA, [BibTeX] (2025)
14. Kang Li, Jiawei Miao, Mihai Cucuringu, Leandro Sánchez-Betancourt, LLM Embedding for Regression Priors, ICAIF 2025, Association for Computing Machinery, New York, NY, USA, [BibTeX] (2025)
15. Kang Li et al., Discrete Flow Matching is a Surprisingly Effective Post-training Method to Address Compound Error in Autoregressive Models, ICAIF 2025, Association for Computing Machinery, New York, NY, USA, [BibTeX] (2025)
16. Brendan Martin, Mihai Cucuringu, Alessandra Luati, Francesco Sanna Passino, Factor-Driven Network Informed Restricted Vector Autoregression, ICAIF 2025, Association for Computing Machinery, New York, NY, USA, [BibTeX] (2025)
17. Yutong Lu, Ning Zhang, Gesine Reinert, Mihai Cucuringu, A tug of war across the market: overnight-vs-daytime lead-lag networks and clustering-based portfolio strategies (SSRN), [BibTeX](2025)
18. Milena Vuletić, Mihai Cucuringu, GraFiN-Gen: Graph-based ensemble generative modelling for multi-asset forecasting, SSRN, [BibTeX] (2025)
19. Weixian Waylon Li, Hyeonjun Kim, Mihai Cucuringu, Tiejun Ma, Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?, arXiv, to appear in KDD 2026 Datasets and Benchmarks Track, [BibTeX] [Oral presentation; 7.5% acceptance rate], [Press Coverage: Wall Street Journal] (2026)
20. Mihai Cucuringu, Kang Li, Chao Zhang, Forecasting Intraday Volume in Equity Markets with Machine Learning, arXiv, [BibTeX] (2025)
21. Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren ClusterLOB: Enhancing Trading Strategies by Clustering Orders in Limit Order Books, Quantitative Finance, 1–25 (2026), [BibTeX] [Press Coverage: Risk.net] (2026)
22. Rafaël Brutti, Maciej Marowka, Mihai Cucuringu Noise-Robust Orthogonal Clustering and Applications to Equity Markets, Quantitative Finance, 1–28 (2026), [BibTeX] (2026)
23. Daniel Cunha Oliveira, Dylan Sandfelder, André Fujita, Xiaowen Dong, Mihai Cucuringu Tactical Asset Allocation with Macroeconomic Regime Detection, Quantitative Finance, 1–27 (2026). [BibTeX]
24. Álvaro Cartea, Mihai Cucuringu, Qi Jin, Mungo Ivor Wilson Volume Shocks and Overnight Returns, SSRN [BibTeX] (2025)
25. Marcos Tapia Costa, Mihai Cucuringu, Guy Nason, Higher-Order Dynamic Network Linear Models for Covariance Forecasting, SSRN, [BibTeX] (2025)
26. Zichuan Guo, Mihai Cucuringu, Alexander Shestopaloff, Generalized Factor Neural Network Model for High-dimensional Regression, SSRN, [BibTeX] (2025)
27. Jakob Albers, Mihai Cucuringu, Sam Howison, Alexander Shestopaloff, To Make, or to Take, That Is the Question: Impact of LOB Mechanics on Natural Trading Strategies, SSRN, [BibTeX] (2024)
28. Emmanuel Djanga, Mihai Cucuringu, Chao Zhang, Cryptocurrency Volatility Forecasting with Applications in Trading, SSRN, [BibTeX] (2024)
29. Nail Khelifa, Jerome Allier, Mihai Cucuringu, Cluster-driven Hierarchical Representation of Large Asset Universes for Optimal Portfolio Construction, ICAIF 2024, Association for Computing Machinery, New York, NY, USA (2024), Best Paper Honorary Mention, [BibTeX] (2024)
30. Kang Li, Mihai Cucuringu, Leandro Sánchez-Betancourt, Timon Willi, Mixtures of Experts for Scaling up Neural Networks in Order Execution, ICAIF 2024, Association for Computing Machinery, New York, NY, USA (2024), [BibTeX] (2024)
31. Anastasia Mantziou, Kerstin Hotte, Mihai Cucuringu, Gesine Reinert, GDP nowcasting with large-scale inter-industry payment data in real time - A network approach, to appear in the Journal of the Royal Statistical Society, Series A, arXiv, [BibTeX] (2024)
32. Nikolas Michael, Mihai Cucuringu, Sam Howison, A GCN-LSTM Approach For ES-Mini And VX Futures Forecasting, SSRN, under revision at Applied Mathematical Finance, [BibTeX] (2024)
33. Brendan Martin, Francesco Sanna Passino, Mihai Cucuringu, Alessandra Luati, NIRVAR: Network Informed Restricted Vector Autoregression, arXiv, [Code (GitHub)] [BibTeX] (2024)
34. Daniel Cunha Oliveira, Yutong Lu, Xi Lin, Mihai Cucuringu, Andre Fujita, Causality-Inspired Models for Financial Time Series Forecasting, arXiv, [BibTeX] (2024)
35. Chang Luo, Tiejun Ma, Mihai Cucuringu, Spatial-Temporal Stock Movement Prediction and Portfolio Selection based on the Semantic Company Relationship Graph, SSRN [BibTeX] (2024)
36. Jakob Albers, Mihai Cucuringu, Sam Howison, Alexander Shestopaloff, The Good, the Bad, and Latency: Exploratory Trading on Bybit and Binance, Quantitative Finance, 1-29 SSRN [BibTeX] (2025)
37. Nikolas Michael, Mihai Cucuringu, Sam Howison, Options-driven Volatility Forecasting, (SSRN), Quantitative Finance, 25(3), 443--470 [BibTeX] (2025)
38. Álvaro Cartea, Mihai Cucuringu, Mark Jennings, Chao Zhang, A Similarity-based Approach to Covariance Forecasting, SSRN (2023)
39. Álvaro Cartea, Mihai Cucuringu, Qi Jin, Detecting Lead-Lag Relationships in Stock Returns and Portfolio Strategies, SSRN [BibTeX] (2023)
40. Emmanuel Djanga, Mihai Cucuringu, and Chao Zhang, Cryptocurrency volatility forecasting using commonality in intraday volatility, ICAIF 2023, Association for Computing Machinery, New York, NY, USA (2023)
41. Bogdan Sitaru, Anisoara Calinescu, Mihai Cucuringu, Order Flow Decomposition for Price Impact Analysis in Equity Limit Order Books, ICAIF 2023, Association for Computing Machinery, New York, NY, USA, SSRN, (2023)
42. Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren, Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models, ICAIF 2023, Association for Computing Machinery, New York, NY, USA, SSRN, (2023)
43. Álvaro Cartea, Mihai Cucuringu, Qi Jin, Correlation Matrix Clustering for Statistical Arbitrage Portfolios, ICAIF 2023, Association for Computing Machinery, New York, NY, USA, SSRN [BibTeX] (2023)
44. Edward Turner, Mihai Cucuringu, Graph Denoising Networks: A Deep Learning Framework for Equity Portfolio Construction, ICAIF 2023, Association for Computing Machinery, New York, NY, USA, 193--201 [BibTeX] (2023)
45. Danni Shi, Jan-Peter Calliess, Mihai Cucuringu Multireference Alignment for Lead-Lag Detection in Multivariate Time Series and Equity Trading, ICAIF 2023, Association for Computing Machinery, New York, NY, USA, SSRN, (2023)
46. Rama Cont, Mihai Cucuringu, Jonathan Kochems, Felix Prenzel, Limit Order Book Simulation with Generative Adversarial Networks, SSRN, under revision at Quantitative Finance, [BibTeX] (2023)
47. Anastasia Mantziou, Mihai Cucuringu, Victor Meirinhos, Gesine Reinert, The GNAR-edge model: A network autoregressive model for networks with time-varying edge weights, Journal of Complex Networks, Volume 11, Issue 6 arXiv, [BibTeX] (2023)
48. Deborah Miori, Mihai Cucuringu, DeFi: Modeling and Forecasting Trading Volume on Uniswap v3 Liquidity Pools, SSRN (2023)
49. Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren, Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models, Applied Mathematical Finance, 32(2): 91-127, [BibTeX] (2025)
50. Nikolas Michael, Mihai Cucuringu, Sam Howison, OFTER: An Online Pipeline for Time Series Forecasting, SSRN (2023)
51. Chao Zhang, Xingyue Pu, Mihai Cucuringu, Xiaowen Dong, Forecasting Realized Volatility with Spillover Effects: Perspectives from Graph Neural Networks, SSRN, to appear in International Journal of Forecasting (2023)
52. Yutong Lu, Gesine Reinert, Mihai Cucuringu, Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets, SSRN, (arXiv), major revision at Management Science (2023)
53. Milena Vuletić, Felix Prenzel, Mihai Cucuringu, Fin-GAN: Forecasting and Classifying Financial Time Series via Generative Adversarial Networks, SSRN, Quantitative Finance, [code FinGAN], [BibTeX] (2024)
54. Deborah Miori, Mihai Cucuringu, Clustering Uniswap v3 traders from their activity on multiple liquidity pools, via novel graph embeddings, Digital Finance 6, 113--143, SSRN, [BibTeX] (2024)
55. Qiong Wu, Jian Li, Zhenming Liu, Yanhua Li, Mihai Cucuringu, Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models, Proceedings of the AAAI Conference on Artificial Intelligence 2023, [BibTeX] (2022)
56. Chao Zhang, Xingyue Pu, Mihai Cucuringu, Xiaowen Dong, Graph-based Methods for Forecasting Realized Covariances, Journal of Financial Econometrics, nbae026, SSRN, [BibTeX] (2024)
57. Yutong Lu, Gesine Reinert, Mihai Cucuringu, Trade Co-occurrence,Trade Flow Decomposition, and Conditional Order Imbalance in Equity Markets, (arXiv), Quantitative Finance, 24(6), 779--809, [BibTeX] (2024)
58. Deborah Miori, Mihai Cucuringu, Returns-Driven Macro Regimes and Characteristic Lead-Lag Behaviour between Asset Classes, (arXiv) ICAIF (2022)
59. Felix Prenzel, Rama Cont, Mihai Cucuringu, Jonathan Kochems, Dynamic Calibration of Order Flow Models with Generative Adversarial Networks, ICAIF '22: 3rd ACM International Conference on AI in Finance, November 2022, pages 446--453 (Best Paper Award) (2022)
60. Deborah Miori, Mihai Cucuringu, Securities and Exchange Commission Form 13F Holdings Report: statistical investigation of trading imbalances and profitability analysis, Journal of Investment Strategies, Volume 12, Number 4 (arXiv) (2023)
61. Deborah Sulem, Henry Kenlay, Mihai Cucuringu, Xiaowen Dong, Graph similarity learning for change-point detection in dynamic networks, Machine Learning 2023, (arXiv) [BibTeX] (2023)
62. Rama Cont, Mihai Cucuringu, Chao Zhang, Renyuan Xu, Tail-GAN: Learning to Simulate Tail Risk Scenarios, Management Science, [Code (GitHub)] [BibTeX] (2025)
63. Chao Zhang, Yihuang Zhang, Mihai Cucuringu, Zhongmin Qian, Volatility forecasting with machine learning and intraday commonality, Journal of Financial Econometrics, Volume 22, Issue 2, Spring 2024, Pages 492--530, [BibTeX] (2024)
64. Nikolas Michael, Mihai Cucuringu, Sam Howison, Option Volume Imbalance as a predictor for equity market returns, accepted to Market Microstructure and Liquidity, arXiv 2201.09319 (2022)
65. Rama Cont, Mihai Cucuringu, Chao Zhang, Cross-impact of order flow imbalance in equity markets Quantitative Finance, 0: 1-21 (2023)
66. Stefanos Bennett, Mihai Cucuringu and Gesine Reinert, Lead-lag detection and network clustering for multivariate time series with an application to the US equity market, Machine Learning 111, 4497-4538 (2022); [BibTeX] (2022). Workshop version: KDD Workshop on mining and learning from time series (2021) KDD MiLeTs (2021)
67. Rama Cont, Mihai Cucuringu, Vacslav Glukhov, Felix Prenzel, Analysis and modeling of client order flow in limit order markets, Quantitative Finance, 23:2, 187-205, [BibTeX] (2023)
68. Chao Zhang, Zihao Zhang, Mihai Cucuringu, Stefan Zohren, A Universal End-to-End Approach to Portfolio Optimization via Deep Learning, arXiv 2111.09170 (2021)
69. J. Albers, M. Cucuringu, S. Howison, A. Y. Shestopaloff, Fragmentation, Price Formation, and Cross-Impact in Bitcoin Markets, Applied Mathematical Finance, 28:5, 395-448, (arXiv), [BibTex] (2022)
70. Qiong Wu, Christopher G. Brinton, Zheng Zhang, Andrea Pizzoferrato, Zhenming Liu, Mihai Cucuringu, Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing, International Conference on AI in Finance (ICAIF 2021), [BibTex] (2021)

© 2025 Mihai Cucuringu