Automatically discovering formulaic alpha factors is a central problem in quantitative finance. Existing methods often ignore syntactic and semantic constraints, relying on exhaustive search over unstructured and unbounded spaces. We present AlphaCFG, a grammar-based framework for defining and discovering alpha factors that are syntactically valid, financially interpretable, and computationally efficient. AlphaCFG uses an alpha-oriented context-free grammar to define a tree-structured, size-controlled search space, and formulates alpha discovery as a tree-structured linguistic Markov decision process, which is then solved using a grammar-aware Monte Carlo Tree Search guided by syntax-sensitive value and policy networks. Experiments on Chinese and U.S. stock market datasets show that AlphaCFG outperforms state-of-the-art baselines in both search efficiency and trading profitability. Beyond trading strategies, AlphaCFG serves as a general framework for symbolic factor discovery and refinement across quantitative finance, including asset pricing and portfolio construction.

This paper introduces a contest-theoretic simplified model of triathlon as a sequential two-stage game. In Stage 1 (post-swim), participants decide whether to continue or withdraw from the contest, thereby generating an endogenous participation decision. In Stage 2 (bike-run), competition is represented as a Tullock contest in which swim drafting acts as a multiplicative shifter of quadratic effort costs. Closed-form equilibrium strategies are derived in the two-player case, and existence, uniqueness, and comparative statics are shown in the asymmetric n-player case. The continuation decision yields athlete-specific cutoff rules in swim drafting intensity and induces subgame-perfect equilibria (SPEs) with endogenous participation sets. The analysis relates swim drafting benefits, exposure, and group size to heterogeneous effective cost parameters and equilibrium efforts.

We consider the supervised learning problem of learning the price of an option or the implied volatility given appropriate input data (model parameters) and corresponding output data (option prices or implied volatilities). The majority of articles in this literature considers a (plain) feed forward neural network architecture in order to connect the neurons used for learning the function mapping inputs to outputs. In this article, motivated by methods in image classification and recent advances in machine learning methods for PDEs, we investigate empirically whether and how the choice of network architecture affects the accuracy and training time of a machine learning algorithm. We find that the generalized highway network architecture achieves the best performance, when considering the mean squared error and the training time as criteria, within the considered parameter budgets for the Black-Scholes and Heston option pricing problems. Considering the transformed implied volatility problem, a simplified DGM variant achieves the lowest error among the tested architectures. We also carry out a capacity-normalised comparison for completeness, where all architectures are evaluated with an equal number of parameters. Finally, for the implied volatility problem, we additionally include experiments using real market data.

This paper examines the dynamic relationship between electoral polls and indicators of economic and financial uncertainty during the last two U.S. presidential elections (2020 and 2024). Using daily polling data on Donald Trump and measures such as the Aruoba-Diebold-Scotti Business Conditions Index, the 5-year Breakeven Inflation Rate, the Trade Policy Uncertainty index, and the VIX, we estimate conditional correlation models to capture time-varying interactions. The analysis reveals that in 2020, correlations between polls and uncertainty measures were highly dynamic and event-driven, reflecting the influence of exogenous shocks (COVID-19, oil price collapse) and political milestones (primaries, debates). In contrast, during the 2024 campaign, correlations remained close to zero, stable, and largely unresponsive to shocks, suggesting that entrenched polarization and non-economic events (e.g., assassination attempt, candidate changes) muted the economic channel. The study highlights how the interplay between voter sentiment, financial markets, and uncertainty varies across electoral contexts, offering a methodological contribution through the application of Dynamic Conditional Correlation models to political data and policy-relevant insights on the conditions under which economic fundamentals influence electoral dynamics.

This paper investigates the impact of Trade Policy Uncertainty (TPU) on stock-bond correlation dynamics in the United States. Using daily data on major U.S. stock indices and the 10-year Treasury bond from 2015 to 2025, we estimate correlation within a two-step GARCH-based framework, relying on multivariate specifications, including Constant Conditional Correlation (CCC), Smooth Transition Conditional Correlation (STCC), and Dynamic Conditional Correlation (DCC) models. We extend these frameworks by incorporating TPU index and a presidential dummy to capture effects of trade uncertainty and government cycles. The findings show that constant correlation models are strongly rejected in favor of time-varying specifications. Both STCC and DCC models confirm TPU's central role in driving correlation dynamics, with significant differences across political regimes. DCC models augmented with TPU and political effects deliver the best in-sample fit and strongest forecasting performance, as measured by statistical and economic loss functions.

Higher education has expanded worldwide, with women outpacing men in many regions. While educational attainment is consistently linked to better physical health, its mental health effects - particularly for women - remain underexplored, and causal evidence is limited. We estimate the impact of college completion on depression among middle-aged women in South Korea, leveraging the 1993 higher education reform, which raised women's college attainment by 45 percentage points (pp) over the following decade. We use two nationally representative datasets to triangulate evidence, including the Korea National Health and Nutrition Examination Survey (KNHANES, 2007-2021) for physician-diagnosed depression, and the Korean Longitudinal Survey of Women and Families (KLoWF, 2007-2022) to validate findings using self-reports of depressive symptoms. We implement two-stage least squares (2SLS) with a birth-cohort instrument based on exposure to the reform (within 3 years of the cutoff in KNHANES and within 1 to 3 years in KLoWF). In KNHANES, college completion lowers physician-diagnosed depression by 2.4 pp, attenuating to 1.6 pp after adjusting for income, employment, and physical health. In KLoWF, college completion improves self-reported mental health. The weekly depressive-symptoms composite declines by 17.4 pp, attenuating to 16.4 pp after covariate adjustment. Placebo tests on unaffected cohorts yield null results. This study contributes to the growing quasi-experimental literature on education and mental health with convergent evidence across clinical diagnoses and self-reported depressive symptoms in South Korea. By focusing on college education in a non-Western setting, it extends the external validity of existing findings and highlights educational policy as a potential lever to reduce the burden of midlife depression among women.

The introduction of artificial intelligence (AI) agents into human group settings raises essential questions about how these novel participants influence cooperative social norms. While previous studies on human-AI cooperation have primarily focused on dyadic interactions, little is known about how integrating AI agents affects the emergence and maintenance of cooperative norms in small groups. This study addresses this gap through an online experiment using a repeated four-player Public Goods Game (PGG). Each group consisted of three human participants and one bot, which was framed either as human or AI and followed one of three predefined decision strategies: unconditional cooperation, conditional cooperation, or free-riding. In our sample of 236 participants, we found that reciprocal group dynamics and behavioural inertia primarily drove cooperation. These normative mechanisms operated identically across conditions, resulting in cooperation levels that did not differ significantly between human and AI labels. Furthermore, we found no evidence of differences in norm persistence in a follow-up Prisoner's Dilemma, or in participants' normative perceptions. Participants' behaviour followed the same normative logic across human and AI conditions, indicating that cooperation depended on group behaviour rather than partner identity. This supports a pattern of normative equivalence, in which the mechanisms that sustain cooperation function similarly in mixed human-AI and all human groups. These findings suggest that cooperative norms are flexible enough to extend to artificial agents, blurring the boundary between humans and AI in collective decision-making.

We study whether Fintech disrupts the banking sector by intensifying competition for scarce deposits funds and raising deposit rates. Using difference-in-difference estimation around the exogenous removal of marketplace platform investing restrictions by US states, we show the cost of deposits increase by approximately 11.5% within small financial institutions. However, these price changes are effective in preventing a drain of liquidity. Size and geographical diversification through branch networks can mitigate the effects of Fintech competition by sourcing deposits from less competitive markets. The findings highlight the unintended consequences of the growing Fintech sector on banks and offer policy insights for regulators and managers into the ongoing development and impact of technology on the banking sector.

This paper reexamines the effects of the Latin Monetary Union (LMU) - a 19th century agreement among several European countries to standardize their currencies through a bimetallic system based on fixed gold and silver content - on trade. Unlike previous studies, this paper adopts the latest advances in gravity modeling and a more rigorous approach to defining the control group by accounting for the diversity of currency regimes during the early years of the LMU. My findings suggest that the LMU had a positive effect on trade between its members until the early 1870s, when bimetallism was still considered a viable monetary system. These effects then faded, converging to zero. Results are robust to the inclusion of additional potential confounders, the use of various samples spanning different countries and trade data sources, and alternative methodological choices.

We use the discrete Ollivier-Ricci graph curvature with Ricci flow to examine the intrinsic geometry of financial markets through the empirical correlation graph of the NASDAQ 100 index. Our main result is the development of a technique to perform surgery on the neckpinch singularities that form during the Ricci flow of the empirical graph, using the behavior and the lower bound of curvature of the fully connected graph as a starting point. We construct an algorithm that uses the curvature generated by intrinsic geometric flow of the graph to detect hidden hierarchies, community behavior, and clustering in financial markets despite the underlying challenges posed by a highly connected geometry.

We study distributionally robust optimization (DRO) problems with uncertainty sets consisting of high-dimensional random vectors that are close in the multivariate Wasserstein distance to a reference random vector. We give conditions when the images of these sets under scalar-valued aggregation functions are contained in and contain uncertainty sets of univariate random variables defined via a univariate Wasserstein distance. This provides lower and upper bounds for the solution to general multivariate DRO problems that are computationally tractable. Furthermore, we generalize the results to uncertainty sets characterized by Bregman-Wasserstein divergences, which allows for asymmetric deviations from the reference random vector. Moreover, for DRO problems with risk measure criterion in the class of signed Choquet integrals, we derive semi-analytic formulae for the upper and lower bounds and the distribution that attains these bounds.

This paper studies how the timing of NGO activism shapes its effectiveness in influencing corporate behavior. Using data on 2,500 campaigns targeting U.S. firms, we show that campaigns timed at annual general meetings (AGMs) generate large visibility gains but little contemporaneous influence, while campaigns launched before the AGM significantly increase shareholder proposal success and improve firms' environmental and social performance. We develop a dynamic model in which NGOs trade off awareness building and credibility formation, generating a lifecycle in activism from visibility-seeking to influence-oriented engagement. Therefore, NGOs' objectives evolve endogenously to coordinate stakeholder pressure and shape corporate behavior.

A debt swap is an elementary edge swap in a directed, weighted graph, where two edges with the same weight swap their targets. Debt swaps are a natural and appealing operation in financial networks, in which nodes are banks and edges represent debt contracts. They can improve the clearing payments and the stability of these networks. However, their algorithmic properties are not well-understood. We analyze the computational complexity of debt swapping. Our main interest lies in semi-positive swaps, in which no creditor strictly suffers and at least one strictly profits. These swaps lead to a Pareto-improvement in the entire network. We consider network optimization via sequences of v-improving debt swaps from which a given bank v strictly profits. For ranking-based clearing, we show that every sequence of semi-positive v-improving swaps has polynomial length. In contrast, for arbitrary v-improving swaps, the problem of reaching a network configuration that allows no further swaps is PLS-complete. In global optimization, the goal is to maximize the utility of a given bank $v$ by performing a sequence of debt swaps in the network. This problem is NP-hard to approximate for multiple types of swaps. Moreover, we study reachability problems -- deciding if a sequence of swaps exists between given initial and final networks. We design a polynomial-time algorithm to decide this question for arbitrary swaps and derive hardness results for several other types of swaps. Many of our results can be extended to networks with arbitrary monotone clearing.

Forecasts of multivariate probability distributions are required for a variety of applications. Scoring rules enable the evaluation of forecast accuracy, and comparison between forecasting methods. We propose a theoretical framework for scoring rules for multivariate distributions, which encompasses the existing quadratic score and multivariate continuous ranked probability score. We demonstrate how this framework can be used to generate new scoring rules. In some multivariate contexts, it is a forecast of a level set that is needed, such as a density level set for anomaly detection or the level set of the cumulative distribution as a measure of risk. This motivates consideration of scoring functions for such level sets. For univariate distributions, it is well-established that the continuous ranked probability score can be expressed as the integral over a quantile score. We show that, in a similar way, scoring rules for multivariate distributions can be decomposed to obtain scoring functions for level sets. Using this, we present scoring functions for different types of level set, including density level sets and level sets for cumulative distributions. To compute the scores, we propose a simple numerical algorithm. We perform a simulation study to support our proposals, and we use real data to illustrate usefulness for forecast combining and CoVaR estimation.