Event-Time Anchor Selection for Multi-Contract Quoting ArXiv ID: 2507.05749 “View on arXiv” Authors: Aditya Nittur Anantha, Shashi Jain, Shivam Goyal, Dhruv Misra Abstract When quoting across multiple contracts, the sequence of execution can be a key driver of implementation shortfall relative to the target spread~\cite{“bergault2022multi”}. We model the short-horizon execution risk from such quoting as variations in transaction prices between the initiation of the first leg and the completion of the position. Our quoting policy anchors the spread by designating one contract ex ante as a \emph{“reference contract”}. Reducing execution risk requires a predictive criterion for selecting that contract whose price is most stable over the execution interval. This paper develops a diagnostic framework for reference-contract selection that evaluates this stability by contrasting order-flow Hawkes forecasts with a Composite Liquidity Factor (CLF) of instantaneous limit order book (LOB) shape. We illustrate the framework on tick-by-tick data for a pair of NIFTY futures contracts. The results suggest that event-history and LOB-state signals offer complementary views of short-horizon execution risk for reference-contract selection. ...

Reinforcement Learning for Trade Execution with Market Impact ArXiv ID: 2507.06345 “View on arXiv” Authors: Patrick Cheridito, Moritz Weiss Abstract In this paper, we introduce a novel reinforcement learning framework for optimal trade execution in a limit order book. We formulate the trade execution problem as a dynamic allocation task whose objective is the optimal placement of market and limit orders to maximize expected revenue. By employing multivariate logistic-normal distributions to model random allocations, the framework enables efficient training of the reinforcement learning algorithm. Numerical experiments show that the proposed method outperforms traditional benchmark strategies in simulated limit order book environments featuring noise traders submitting random orders, tactical traders responding to order book imbalances, and a strategic trader seeking to acquire or liquidate an asset position. ...

Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges ArXiv ID: 2507.18577 “View on arXiv” Authors: Liyuan Chen, Shuoling Liu, Jiangpeng Yan, Xiaoyu Wang, Henglin Liu, Chuang Li, Kecheng Jiao, Jixuan Ying, Yang Veronica Liu, Qiang Yang, Xiu Li Abstract The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation. ...

F&O Expiry vs. First-Day SIPs: A 22-Year Analysis of Timing Advantages in India’s Nifty 50 ArXiv ID: 2507.04859 “View on arXiv” Authors: Siddharth Gavhale Abstract Systematic Investment Plans (SIPs) are a primary vehicle for retail equity participation in India, yet the impact of their intra-month timing remains underexplored. This study offers a 22-year (2003–2024) comparative analysis of SIP performance in the Nifty 50 index, contrasting the conventional first-trading-day (FTD-SIP) strategy with an alternative aligned to monthly Futures and Options expiry days (EXP-SIP). Using a multi-layered statistical framework – including non-parametric tests, effect size metrics, and stochastic dominance – we uncover two key findings. First, EXP-SIPs outperform FTD-SIPs by 0.5–2.5% annually over short-to-medium-term horizons (1–5 years), with Second-Order Stochastic Dominance (SSD) confirming the EXP-SIP as the preferred choice for all risk-averse investors. Second, we establish boundary conditions for this advantage, showing it decays and becomes economically negligible over longer horizons (10–20 years), where compounding and participation dominate. Additionally, the study challenges the prevalent ``12% return’’ narrative in Indian equity markets, finding that the 20-year pre-tax CAGR for Nifty 50 SIPs is closer to 6.7%. These findings carry significant implications for investor welfare, financial product design, and transparency in return reporting. ...

FinSurvival: A Suite of Large Scale Survival Modeling Tasks from Finance ArXiv ID: 2507.14160 “View on arXiv” Authors: Aaron Green, Zihan Nie, Hanzhen Qin, Oshani Seneviratne, Kristin P. Bennett Abstract Survival modeling predicts the time until an event occurs and is widely used in risk analysis; for example, it’s used in medicine to predict the survival of a patient based on censored data. There is a need for large-scale, realistic, and freely available datasets for benchmarking artificial intelligence (AI) survival models. In this paper, we derive a suite of 16 survival modeling tasks from publicly available transaction data generated by lending of cryptocurrencies in Decentralized Finance (DeFi). Each task was constructed using an automated pipeline based on choices of index and outcome events. For example, the model predicts the time from when a user borrows cryptocurrency coins (index event) until their first repayment (outcome event). We formulate a survival benchmark consisting of a suite of 16 survival-time prediction tasks (FinSurvival). We also automatically create 16 corresponding classification problems for each task by thresholding the survival time using the restricted mean survival time. With over 7.5 million records, FinSurvival provides a suite of realistic financial modeling tasks that will spur future AI survival modeling research. Our evaluation indicated that these are challenging tasks that are not well addressed by existing methods. FinSurvival enables the evaluation of AI survival models applicable to traditional finance, industry, medicine, and commerce, which is currently hindered by the lack of large public datasets. Our benchmark demonstrates how AI models could assess opportunities and risks in DeFi. In the future, the FinSurvival benchmark pipeline can be used to create new benchmarks by incorporating more DeFi transactions and protocols as the use of cryptocurrency grows. ...

Representation learning with a transformer by contrastive learning for money laundering detection ArXiv ID: 2507.08835 “View on arXiv” Authors: Harold Guéneau, Alain Celisse, Pascal Delange Abstract The present work tackles the money laundering detection problem. A new procedure is introduced which exploits structured time series of both qualitative and quantitative data by means of a transformer neural network. The first step of this procedure aims at learning representations of time series through contrastive learning (without any labels). The second step leverages these representations to generate a money laundering scoring of all observations. A two-thresholds approach is then introduced, which ensures a controlled false-positive rate by means of the Benjamini-Hochberg (BH) procedure. Experiments confirm that the transformer is able to produce general representations that succeed in exploiting money laundering patterns with minimal supervision from domain experts. It also illustrates the higher ability of the new procedure for detecting nonfraudsters as well as fraudsters, while keeping the false positive rate under control. This greatly contrasts with rule-based procedures or the ones based on LSTM architectures. ...

Behavioral Probability Weighting and Portfolio Optimization under Semi-Heavy Tails ArXiv ID: 2507.04208 “View on arXiv” Authors: Ayush Jha, Abootaleb Shirvani, Ali M. Jaffri, Svetlozar T. Rachev, Frank J. Fabozzi Abstract This paper develops a unified framework that integrates behavioral distortions into rational portfolio optimization by extracting implied probability weighting functions (PWFs) from optimal portfolios modeled under Gaussian and Normal-Inverse-Gaussian (NIG) return distributions. Using DJIA constituents, we construct mean-CVaR99 frontiers, alongwith Sharpe- and CVaR-maximizing portfolios, and estimate PWFs that capture nonlinear beliefs consistent with fear and greed. We show that increasing tail fatness amplifies these distortions and that shifts in the term structure of risk-free rates alter their curvature. The results highlight the importance of jointly modeling return asymmetry and belief distortions in portfolio risk management and capital allocation under extreme-risk environments. ...

Does Overnight News Explain Overnight Returns? ArXiv ID: 2507.04481 “View on arXiv” Authors: Paul Glasserman, Kriste Krstovski, Paul Laliberte, Harry Mamaysky Abstract Over the past 30 years, nearly all the gains in the U.S. stock market have been earned overnight, while average intraday returns have been negative or flat. We find that a large part of this effect can be explained through features of intraday and overnight news. Our analysis uses a collection of 2.4 million news articles. We apply a novel technique for supervised topic analysis that selects news topics based on their ability to explain contemporaneous market returns. We find that time variation in the prevalence of news topics and differences in the responses to news topics both contribute to the difference in intraday and overnight returns. In out-of-sample tests, our approach forecasts which stocks will do particularly well overnight and particularly poorly intraday. Our approach also helps explain patterns of continuation and reversal in intraday and overnight returns. We contrast the effect of news with other mechanisms proposed in the literature to explain overnight returns. ...

Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks ArXiv ID: 2507.03963 “View on arXiv” Authors: Yen Jui Chang, Wei-Ting Wang, Yun-Yuan Wang, Chen-Yu Liu, Kuan-Cheng Chen, Ching-Ray Chang Abstract Financial markets are noisy yet contain a latent graph-theoretic structure that can be exploited for superior risk-adjusted returns. We propose a quantum stochastic walk (QSW) optimizer that embeds assets in a weighted graph: nodes represent securities while edges encode the return-covariance kernel. Portfolio weights are derived from the walk’s stationary distribution. Three empirical studies support the approach. (i) For the top 100 S&P 500 constituents over 2016-2024, six scenario portfolios calibrated on 1- and 2-year windows lift the out-of-sample Sharpe ratio by up to 27% while cutting annual turnover from 480% (mean-variance) to 2-90%. (ii) A $5^{“4”}=625$-point grid search identifies a robust sweet spot, $α,λ\lesssim0.5$ and $ω\in[“0.2,0.4”]$, that delivers Sharpe $\approx0.97$ at $\le 5%$ turnover and Herfindahl-Hirschman index $\sim0.01$. (iii) Repeating the full grid on 50 random 100-stock subsets of the S&P 500 adds 31,350 back-tests: the best-per-draw QSW beats re-optimised mean-variance on Sharpe in 54% of cases and always wins on trading efficiency, with median turnover 36% versus 351%. Overall, QSW raises the annualized Sharpe ratio by 15% and cuts turnover by 90% relative to classical optimisation, all while respecting the UCITS 5/10/40 rule. These results show that hybrid quantum-classical dynamics can uncover non-linear dependencies overlooked by quadratic models and offer a practical, low-cost weighting engine for themed ETFs and other systematic mandates. ...

skfolio: Portfolio Optimization in Python ArXiv ID: 2507.04176 “View on arXiv” Authors: Carlo Nicolini, Matteo Manzi, Hugo Delatte Abstract Portfolio optimization is a fundamental challenge in quantitative finance, requiring robust computational tools that integrate statistical rigor with practical implementation. We present skfolio, an open-source Python library for portfolio construction and risk management that seamlessly integrates with the scikit-learn ecosystem. skfolio provides a unified framework for diverse allocation strategies, from classical mean-variance optimization to modern clustering-based methods, state-of-the-art financial estimators with native interfaces, and advanced cross-validation techniques tailored for financial time series. By adhering to scikit-learn’s fit-predict-transform paradigm, the library enables researchers and practitioners to leverage machine learning workflows for portfolio optimization, promoting reproducibility and transparency in quantitative finance. ...