LLMs struggle with financial reasoning but can outperform the market with human oversight.
problem Financial reasoning failures in LLM-generated stock market predictions.
method Evaluated four LLMs using three prompting strategies and compared to human oversight.
result LLMs require human oversight to fully realize their potential in financial markets.
Machine learning uses crowdworkers; determining their status as human subjects is tricky.
problem Determining the appropriate status of ML crowdworkers as human subjects.
method Investigation of natural language processing studies to expose challenges and propose solutions.
result Potential loophole in the U.S. Common Rule for ML research oversight.
Digital money could reduce germ spread during coronavirus.
problem Spreading of germs via paper money during coronavirus.
method Policy recommendations for mobile wallets, digital currencies, and data protection.
result Adopting digital money can help reduce germ spread.
New framework detects crypto wash trading using liquidity measures.
problem Detecting and monitoring wash trading in crypto assets.
method Developed a new framework to detect wash trading through real-time liquidity fluctuation measures.
result Joint elevation in liquidity jump and diffusion indicates wash trading in crypto assets.
Model proposes how regulators should oversee complex algorithms in high-stakes applications.
problem Regulating complex algorithms used in high-stakes applications like lending, testing, and hiring.
method Proposes a model where regulators are limited in learning about complex algorithms with misaligned preferences, and explores different regulatory approaches.
result Complex algorithms can improve welfare, but regulation should focus on the source of incentive misalignment for optimal results.
The 2007--2008 financial crisis has paved the way for the use of macroprudential policies in supervising the financial system as a whole. This paper views macroprudential oversight in Europe as a process, a sequence of activities with the ultimate aim of safeguarding financial stability. To conceptualize a process in t…
Paper introduces PHI to identify structurally distinct payment patterns in UK municipal procurement.
problem Vulnerability of public procurement to error, fraud, and corruption in high-volume transactions.
method Introduces Payment Heterogeneity Index (PHI) using Gaussian Mixture Model (GMM) and non-parametric statistics.
result Identifies a significant cohort with structurally distinct payment patterns, improving procurement oversight.
Study shows GPT's earnings forecasts are human-like but not always accurate.
problem Information friction in AI-generated financial analysis.
method Examined GPT's earnings forecasts following corporate earnings releases and proposed a diagnostic framework.
result GPT's narrative attention is consistent and human-like but not always associated with higher forecast accuracy.
AI agents manage portfolios, improving on human oversight.
problem Improving strategic asset allocation for institutional investors.
method 50 specialized agents produce capital market assumptions, construct portfolios, critique, and vote on each other's output.
result Meta-agent compares forecasts with realized returns and improves agent performance.
New AI governance framework tackles risks in finance.
problem Risks from evolving AI models in finance.
method Agent-based framework with modular governance architecture.
result Controls quarantine harmful behavior in real time.
Adaptive AI delegation framework for dynamic decision authority allocation.
problem Dynamic allocation of decision authority to AI-generated recommendations under evolving evidence quality and uncertainty.
method Formulated as a Governance-Aware POMDP, using Bayesian inference for informational state estimation and sequential optimization for authority allocation.
result Sequential Bayesian governance provides the strongest general-purpose policy across AI-quality regimes, adapting to evolving evidence.
A new mathematical framework simplifies securitization structuring.
problem Challenges in structuring asset-backed securities.
method PEAL Method: a 10-step mathematical framework.
result Enhances risk characterization and market transparency.
Over the last 23 years, the U.S. Securities and Exchange Commission has required over 34,000 companies to file over 165,000 annual reports. These reports, the so-called "Form 10-Ks," contain a characterization of a company's financial performance and its risks, including the regulatory environment in which a company op…
We prove a structure theorem for closed topological manifolds of cohomogeneity one; this result corrects an oversight in the literature. We complete the equivariant classification of closed, simply connected cohomogeneity one topological manifolds in dimensions 5, 6, and 7 and obtain topological characterizations…
Generative model learns investment strategies without explicit utility specification.
problem Challenges in modeling complex, multi-objective fund optimization.
method Generative adversarial network (GAN) framework that learns latent strategy representations.
result Framework captures diverse investment styles and realizations of optimization parameters.
This paper examines market misconduct in DeFi and proposes regulatory solutions.
problem Novel forms of market misconduct in DeFi.
method Comprehensive analysis, comparative study, empirical measurements, and tailored regulatory framework investigation.
result Identification of key areas for regulatory enhancement in DeFi.
Paper develops a framework to discover bioprocessing regulatory mechanisms using symbolic and statistical learning.
problem Challenges in modeling complex intracellular regulation, stochastic system behavior, and limited experimental data.
method Symbolic and statistical learning framework based on stochastic differential equations and Bayesian learning.
result Improved sample efficiency and robust model selection compared to state-of-the-art approaches.
Algorithmic insurance tackles financial risks from AI errors, proving CVaR-optimal thresholds reduce tail risk.
problem High-stakes AI errors lead to heterogeneous losses, challenging traditional insurance assumptions.
method Analyzed binary classification performance to tail risk exposure, using CVaR to quantify extreme losses.
result CVaR-optimal thresholds reduce tail risk up to 13-fold compared to accuracy maximization.
regvis.net offers a visual survey of regulatory visualization.
problem Lack of a comprehensive resource for regulatory visualization.
method Collection and manual tagging of 80+ publications, creation of a searchable webpage.
result First publication set tailored for regulatory visualization.
Optimizes insurance profits under regulatory constraints.
problem Maximizing profits while adhering to regulatory and risk policies.
method Developed a formalism for in-force business profit optimisation.
result Identified optimal asset allocation and annual opportunity cost.
TNDE quantifies dynamic gene drivers from single-cell snapshots.
problem Reconstructing time-resolved regulatory effects in biological processes.
method Time-varying Network Driver Estimation (TNDE) using shared graph attention encoder and partial optimal transport.
result TNDE identifies stage-specific driver genes in mouse erythropoiesis.
Study analyzes crypto asset risk exposures using a divide-and-conquer approach.
problem Lack of high-frequency macro-financial proxies for estimating risk.
method Two-stage divide-and-conquer approach: first stage estimates idiosyncratic and market risk, second stage identifies latent economy-wide factors.
result Heterogeneous exposures to idiosyncratic and systematic risk across crypto assets.
This paper discusses the role of risk communication in macroprudential oversight and of visualization in risk communication. Beyond the soar in data availability and precision, the transition from firm-centric to system-wide supervision imposes vast data needs. Moreover, except for internal communication as in any orga…
In a market system, regulations are designed to prevent or rectify market failures that inhibit fair exchange, such as monopoly or transactions with hidden costs. Because regulations reduce profits to those possessing unfair advantage, these advantaged corporations (whether individuals, companies, or other collective o…
Paper constructs a CRRIX index to assess cryptocurrency market risks from regulatory changes.
problem Lack of indices quantifying regulatory risks in cryptocurrencies.
method CRRIX index based on news coverage frequency, using Latent Dirichlet Allocation and Hellinger distance.
result CRRIX successfully captures major policy-changing moments and synchronizes with market volatility.
InfoSEM infers gene regulatory networks without GT labels, improving performance.
problem Inferring GRNs from gene expression data with high accuracy and avoiding biases.
method InfoSEM uses deep generative models with informative priors (textual gene embeddings).
result InfoSEM outperforms existing models by 38.5% across four datasets.
New method constructs confidence bands for ODE models with unknown regulatory effects.
problem Building confidence bands for ODE models with unknown regulatory relations is challenging.
method Localized kernel learning approach combined with de-biasing method.
result The constructed confidence band has the desired asymptotic coverage probability and accurately recovers the regulatory network.
Survey of alignment techniques for large language models.
problem Ensuring large language models align with human values.
method Analysis of diverse alignment methods and training paradigms.
result Preference-based methods offer more flexibility for nuanced alignment.
Proposes a new method for determining LGD discount rates based on cost of capital.
problem Determining an appropriate discount rate for LGD estimation.
method Market-consistent pricing of defaulted loan portfolios to infer discount rates.
result Discount rates reflect both undiversifiable risk and time value of money.
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. Many of these computational methods are designed to infer individual …
Survey on finite group actions on CW-complexes homotopy to spheres.
problem Understanding finite group actions on CW-complexes homotopy equivalent to spheres.
method Survey of extensive literature on finite G-CW-complexes homotopy equivalent to spheres. result Finite G-CW-complexes homotopy equivalent to spheres have finite group actions. Funding is a cost to trading desks that they see as an input. Current FVA-related literature reflects this by also taking funding costs as an input, usually constant, and always risk-neutral. However, this funding curve is the output from a Treasury point of view. Treasury must consider Regulatory-required liquidity bu…
Simple method calculates WWR for regulatory and accounting purposes.
problem Estimating WWR for regulatory and accounting capital requirements.
method Model-independent approach using integral expressions and component calibration.
result WWR effects for FVA are significantly more material than for CVA.
No policy can simultaneously be fully autonomous, optimally calibrated, and helpful, proving a trilemma.
problem Proving impossibility of a policy achieving maximum helpfulness, optimal calibration, and full autonomy.
method Geometric proof showing that adding any non-affine autonomy incentive to a strictly proper scoring rule destroys strict properness.
result The Behavioral Credibility Trilemma: no policy can achieve all three goals simultaneously.
DASH simplifies neural networks for gene regulatory dynamics using domain knowledge.
problem Pruning neural networks for gene regulatory dynamics lacks biologically meaningful structure learning.
method DASH uses domain-specific structural information to guide network pruning, leading to sparser, better interpretable models.
result DASH outperforms general pruning methods in gene regulatory network inference, yielding deeper insights.
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. A typical approach consists of a series of conditional independence t…
Cryptocurrency markets show similar returns but different volatility responses to infrastructure and regulatory shocks.
problem Understanding how cryptocurrency markets differentiate between infrastructure and regulatory shocks.
method Event-level block bootstrap inference on 31 cryptocurrency events across Bitcoin, Ethereum, Solana, and Cardano (2019-2025).
result No statistically significant difference in cumulative abnormal returns between infrastructure failures and regulatory enforcement.
New framework for adaptive clinical trials to address real-world challenges.
problem Real-world challenges in post-regulatory clinical trials.
method RFAN framework integrating regulatory constraints and treatment policy value.
result Empirical evaluation of RFAN's performance.
Efficiently infers gene regulatory networks from spatial data.
problem Inferring spatially-varying gene regulatory networks.
method Proposed an efficient optimization problem for SV-GMRFs.
result Solves large-scale SV-GMRF problems in minutes.
SHARC explains machine learning risk models for regulatory capital, linking outputs to scenarios.
problem Inability to explain machine learning model outputs to regulatory bodies.
method SHAP-based explainability framework for Hybrid GPR-HS architecture and SVaR stress-testing.
result SHARC links SVaR outputs to scenario inputs, providing auditable traceability.
Robust machine learning models improve DNA regulatory sequence prediction under various shifts.
problem Real-world applications of DNA regulatory sequence prediction involve shifts not captured by standard i.i.d. assumptions.
method Introduces a robustness framework combining simulation benchmarks and real data analysis.
result Models remain accurate and calibrated under mild shifts but show higher error and miscalibration under strong shifts.
Develops probabilistic models for gene regulatory network inference.
problem Challenges in reconstructing gene regulatory networks from genome-wide data.
method Two complementary frameworks: PMF-GRN and GLM-Prior.
result Probabilistic inference refines regulatory estimates with quantified uncertainty.
Study examines how business units can benefit from group cohesion under regulatory constraints.
problem Regulatory constraints limit business units' ability to form a single cohesive group.
method Defined and analyzed cohesive risk measures to minimize capital costs.
result Cohesive risk measures allow groups to achieve minimal capital costs without altering individual liabilities.
LR-Robot automates SLRs with AI, expert oversight, and multidimensional analysis.
problem Efficient but contextually limited outputs from existing SLR frameworks.
method Human-in-the-loop process, structured knowledge sources, retrieval-augmented generation.
result Empirical demonstration of AI-driven literature synthesis in option pricing.
The DAO Report led to a significant shift of ICO activity to Europe.
problem The impact of U.S. regulatory changes on global ICO activity.
method Analysis of a global dataset of ICOs from 2014 to 2021, focusing on the DAO Report's effects.
result A substantial and persistent reallocation of ICO activity to Europe following the DAO Report.
This paper uses deep learning to detect money laundering in cross-border transactions.
problem Detecting money laundering in cross-border transactions is challenging due to complexity and scale.
method Unsupervised learning models, including CNNs and hybrid CNNGRU architectures, were tested for anomaly detection.
result Hybrid Convolutional-Recurrent Neural Integration Model (CRNIM) showed superior performance.
Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data. In this work, we present NO-BEARS, a novel algorithm for estimating gene regulatory networks. The NO-BEARS algorithm is built on the basis of the NOTEARS algorithm with two improvements. First, we propose …
Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acqu…