pwc-archive/papers-with-abstracts huggingface dataset:
Deep Multi-modal Breast Cancer Detection Network
Authors: Noor Ul Huda Shah, Tanveer Hussain, Amr Ahmed, Yonghuai Liu, Usman Ali, Ardhendu Behera
Date: 2025-04-22 00:00:00
Tasks: Breast Cancer Detection, Data Integration, Diagnostic

Automated breast cancer detection via computer vision techniques is challenging due to the complex nature of breast tissue, the subtle appearance of cancerous lesions, and variations in breast density. Mainstream techniques primarily focus on visual cues, overlooking complementary patient-specific textual features that are equally important and can enhance diagnostic accuracy. To address this gap, we introduce Multi-modal Cancer Detection Network (MMDCNet) that integrates visual cues with clinical data to improve breast cancer detection. Our approach processes medical images using computer vision techniques while structured patient metadata patterns are learned through a custom fully connected network. The extracted features are fused to form a comprehensive representation, allowing the model to leverage both visual and clinical information. The final classifier is trained based on the joint features embedding space of visual and clinical cues and experiments prove enhanced performance, improving accuracy from 79.38\% to 90.87\% on a Mini-DDSM dataset. Additionally, our approach achieves 97.05\% accuracy on an image-only dataset, highlighting the robustness and effectiveness of visual feature extraction. These findings emphasise the potential of multi-modal learning in medical diagnostics, paving the way for future research on optimising data integration strategies and refining AI-driven clinical decision support systems.
Last-layer committee machines for uncertainty estimations of benthic imagery
Authors: H. Martin Gillis, Isaac Xu, Benjamin Misiuk, Craig J. Brown, Thomas Trappenberg
Date: 2025-04-22 00:00:00

Automating the annotation of benthic imagery (i.e., images of the seafloor and its associated organisms, habitats, and geological features) is critical for monitoring rapidly changing ocean ecosystems. Deep learning approaches have succeeded in this purpose; however, consistent annotation remains challenging due to ambiguous seafloor images, potential inter-user annotation disagreements, and out-of-distribution samples. Marine scientists implementing deep learning models often obtain predictions based on one-hot representations trained using a cross-entropy loss objective with softmax normalization, resulting with a single set of model parameters. While efficient, this approach may lead to overconfident predictions for context-challenging datasets, raising reliability concerns that present risks for downstream tasks such as benthic habitat mapping and marine spatial planning. In this study, we investigated classification uncertainty as a tool to improve the labeling of benthic habitat imagery. We developed a framework for two challenging sub-datasets of the recently publicly available BenthicNet dataset using Bayesian neural networks, Monte Carlo dropout inference sampling, and a proposed single last-layer committee machine. This approach resulted with a > 95% reduction of network parameters to obtain per-sample uncertainties while obtaining near-identical performance compared to computationally more expensive strategies such as Bayesian neural networks, Monte Carlo dropout, and deep ensembles. The method proposed in this research provides a strategy for obtaining prioritized lists of uncertain samples for human-in-the-loop interventions to identify ambiguous, mislabeled, out-of-distribution, and/or difficult images for enhancing existing annotation tools for benthic mapping and other applications.
A species of Coprococcus is related to BMI in patients who underwent malabsorptive bariatric surgery and its abundance is modified by magnesium and thiamin intake
Authors: Fernando Suárez-Sánchez, Evelyn Pérez-Ruiz, Claudia Ivonne Ramírez-Silva, Mario Antonio Molina-Ayala, Sandra Rivera-Gutiérrez, Lizbel León-Solís, Lázaro García-Morales, Arturo Rodríguez-González, César Martínez-Ortiz, Luis Axiel Meneses-Tapia, Miguel Cruz-López
Date: 2025-04-21 00:00:00
Tasks: Diversity

Background: Morbid obesity is associated with metabolic alterations and the onset of type 2 diabetes. Patients who undergo a malabsorptive bariatric surgery show an important improvement in several clinical variables and a modification in the gut microbiota balance. In this study, we aimed to identify bacteria related to changes in the body mass index of patients who underwent a bariatric surgery and their relationship with nutrients intake. Results: There were differences in bacterial diversity in the gut microbiota of patients that underwent a bariatric surgery. The Shannon and Simpson indexes decrease after the surgery (p < 0.001) and the beta diversity indexes (Bray-Curtis, Weighted and Unweighted UniFrac) showed differences when comparing pre- and post-surgery (p = 0.001). The abundance of a species in the genus Coprococcus correlated positively with the intake of magnesium and thiamin in post-surgery individuals (rho = 0.816, pFDR = 0.029 and rho = 0.812, pFDR = 0.029, respectively) and was related to BMI in both groups (p = 0.043 pre-surgery and p = 0.036 post-surgery). The abundances of several bacteria belonging to the order Clostridiales, as well as an enrichment of vitamin B1 (thiamin) biosynthesis, sugar degradation, acetate production and some amino acids biosynthesis were higher before the surgery. Conclusions: The abundance of a species of the genus Coprococcus that showed inverse relationships with BMI in pre-surgery and post-surgery patients correlates with the intake of magnesium and thiamin in individuals that underwent a malabsorptive bariatric surgery. It indicates that the well-established beneficial effects of bariatric surgery on BMI may be amplified by modulating the intake of micronutrients and its effect on the gut bacterial.
Topological model selection: a case-study in tumour-induced angiogenesis
Authors: Robert A McDonald, Helen M Byrne, Heather A Harrington, Thomas Thorne, Bernadette J Stolz
Date: 2025-04-21 00:00:00
Tasks: Model Selection, Topological Data Analysis

Comparing mathematical models offers a means to evaluate competing scientific theories. However, exact methods of model calibration are not applicable to many probabilistic models which simulate high-dimensional spatio-temporal data. Approximate Bayesian Computation is a widely-used method for parameter inference and model selection in such scenarios, and it may be combined with Topological Data Analysis to study models which simulate data with fine spatial structure. We develop a flexible pipeline for parameter inference and model selection in spatio-temporal models. Our pipeline identifies topological summary statistics which quantify spatio-temporal data and uses them to approximate parameter and model posterior distributions. We validate our pipeline on models of tumour-induced angiogenesis, inferring four parameters in three established models and identifying the correct model in synthetic test-cases.
Multilayer Perceptron Neural Network Models in Asset Pricing: An Empirical Study on Large-Cap US Stocks
Authors: Shanyan Lai
Date: 2025-05-03 00:00:00

In this study, MLP models with dynamic structure are applied to factor models for asset pricing tasks. Concretely, the MLP pyramid model structure was employed on firm-characteristic-sorted portfolio factors for modelling the large-capital US stocks. It was further developed as a practicable factor investing strategy based on the predictions. The main findings in this chapter were evaluated from two angles: model performance and investing performance, which were compared from the periods with and without COVID-19. The empirical results indicated that with the restrictions of the data size, the MLP models no longer perform "deeper, better", while the proposed MLP models with two and three hidden layers have higher flexibility to model the factors in this case. This study also verified the idea of previous works that MLP models for factor investing have more meaning in the downside risk control than in pursuing the absolute annual returns.
Systemic Risk in the European Insurance Sector
Authors: Giovanni Bonaccolto, Nicola Borri, Andrea Consiglio, Giorgio Di Giorgio
Date: 2025-05-05 00:00:00

This paper investigates the dynamic interdependencies between the European insurance sector and key financial markets-equity, bond, and banking-by extending the Generalized Forecast Error Variance Decomposition framework to a broad set of performance and risk indicators. Our empirical analysis, based on a comprehensive dataset spanning January 2000 to October 2024, shows that the insurance market is not a passive receiver of external shocks but an active contributor in the propagation of systemic risk, particularly during periods of financial stress such as the subprime crisis, the European sovereign debt crisis, and the COVID-19 pandemic. Significant heterogeneity is observed across subsectors, with diversified multiline insurers and reinsurance playing key roles in shock transmission. Moreover, our granular company-level analysis reveals clusters of systemically central insurance companies, underscoring the presence of a core group that consistently exhibits high interconnectivity and influence in risk propagation.
Numerical analysis on locally risk-minimizing strategies for Barndorff-Nielsen and Shephard models
Authors: Takuji Arai
Date: 2025-05-01 00:00:00

We develop a numerical method for locally risk-minimizing (LRM) strategies for Barndorff-Nielsen and Shephard (BNS) models. Arai et al. (2017) derived a mathematical expression for LRM strategies in BNS models using Malliavin calculus for L\'evy processes and presented some numerical results only for the case where the asset price process is a martingale. Subsequently, Arai and Imai (2024) developed the first Monte Carlo (MC) method available for non-martingale BNS models with infinite active jumps. Here, we modify the expression obtained by Arai et al. (2017) into a numerically tractable form, and, using the MC method developed by Arai and Imai (2024), propose a numerical method of LRM strategies available for non-martingale BNS models with infinite active jumps. In the final part of this paper, we will conduct some numerical experiments.
Deep Declarative Risk Budgeting Portfolios
Authors: Manuel Parra-Diaz, Carlos Castro-Iragorri
Date: 2025-04-28 00:00:00
Tasks: Portfolio Optimization, Sensitivity

Recent advances in deep learning have spurred the development of end-to-end frameworks for portfolio optimization that utilize implicit layers. However, many such implementations are highly sensitive to neural network initialization, undermining performance consistency. This research introduces a robust end-to-end framework tailored for risk budgeting portfolios that effectively reduces sensitivity to initialization. Importantly, this enhanced stability does not compromise portfolio performance, as our framework consistently outperforms the risk parity benchmark.
Multi-Horizon Echo State Network Prediction of Intraday Stock Returns
Authors: Giovanni Ballarin, Jacopo Capra, Petros Dellaportas
Date: 2025-04-28 00:00:00
Tasks: Prediction

Stock return prediction is a problem that has received much attention in the finance literature. In recent years, sophisticated machine learning methods have been shown to perform significantly better than ''classical'' prediction techniques. One downside of these approaches is that they are often very expensive to implement, for both training and inference, because of their high complexity. We propose a return prediction framework for intraday returns at multiple horizons based on Echo State Network (ESN) models, wherein a large portion of parameters are drawn at random and never trained. We show that this approach enjoys the benefits of recurrent neural network expressivity, inherently efficient implementation, and strong forecasting performance.
Bridging Econometrics and AI: VaR Estimation via Reinforcement Learning and GARCH Models
Authors: Fredy Pokou, Jules Sadefo Kamdem, François Benhmad
Date: 2025-04-23 00:00:00
Tasks: Deep Reinforcement Learning, Econometrics, imbalanced classification

In an environment of increasingly volatile financial markets, the accurate estimation of risk remains a major challenge. Traditional econometric models, such as GARCH and its variants, are based on assumptions that are often too rigid to adapt to the complexity of the current market dynamics. To overcome these limitations, we propose a hybrid framework for Value-at-Risk (VaR) estimation, combining GARCH volatility models with deep reinforcement learning. Our approach incorporates directional market forecasting using the Double Deep Q-Network (DDQN) model, treating the task as an imbalanced classification problem. This architecture enables the dynamic adjustment of risk-level forecasts according to market conditions. Empirical validation on daily Eurostoxx 50 data covering periods of crisis and high volatility shows a significant improvement in the accuracy of VaR estimates, as well as a reduction in the number of breaches and also in capital requirements, while respecting regulatory risk thresholds. The ability of the model to adjust risk levels in real time reinforces its relevance to modern and proactive risk management.
Automated Market Makers: A Stochastic Optimization Approach for Profitable Liquidity Concentration
Authors: Simon Caspar Zeller, Paul-Niklas Ken Kandora, Daniel Kirste, Niclas Kannengießer, Steffen Rebennack, Ali Sunyaev
Date: 2025-04-23 00:00:00
Tasks: Stochastic Optimization

Concentrated liquidity automated market makers (AMMs), such as Uniswap v3, enable liquidity providers (LPs) to earn liquidity rewards by depositing tokens into liquidity pools. However, LPs often face significant financial losses driven by poorly selected liquidity provision intervals and high costs associated with frequent liquidity reallocation. To support LPs in achieving more profitable liquidity concentration, we developed a tractable stochastic optimization problem that can be used to compute optimal liquidity provision intervals for profitable liquidity provision. The developed problem accounts for the relationships between liquidity rewards, divergence loss, and reallocation costs. By formalizing optimal liquidity provision as a tractable stochastic optimization problem, we support a better understanding of the relationship between liquidity rewards, divergence loss, and reallocation costs. Moreover, the stochastic optimization problem offers a foundation for more profitable liquidity concentration.
Towards a fast and robust deep hedging approach
Authors: Fabienne Schmid, Daniel Oeltz
Date: 2025-04-23 00:00:00

We present a robust Deep Hedging framework for the pricing and hedging of option portfolios that significantly improves training efficiency and model robustness. In particular, we propose a neural model for training model embeddings which utilizes the paths of several advanced equity option models with stochastic volatility in order to learn the relationships that exist between hedging strategies. A key advantage of the proposed method is its ability to rapidly and reliably adapt to new market regimes through the recalibration of a low-dimensional embedding vector, rather than retraining the entire network. Moreover, we examine the observed Profit and Loss distributions on the parameter space of the models used to learn the embeddings. The results show that the proposed framework works well with data generated by complex models and can serve as a construction basis for an efficient and robust simulation tool for the systematic development of an entirely model-independent hedging strategy.
Modelling Financial Market Imperfection Using Open Quantum Systems
Authors: Will Hicks
Date: 2025-05-02 00:00:00

We start with the idea that open quantum systems can be used to represent financial markets by modelling events from the external environment and their impact on the market price. We show how to characterize distinct orbits of the time evolution, and look at the development of the reduced density matrix, that represents the state of the market, over long time frames. In particular we distinguish between classical and non-classical modes of time evolution. We show that whilst both tend to the same set of maximum entropy states, this occurs faster in classical systems, with a knock on effect on the resulting probability distributions. We demonstrate how non-classical modes of time-evolution can be used to incorporate factors such as illiquid trades and imperfect trading mechanisms, and distinguish between different mechanisms of non-classical time evolution.
Compounding Effects in Leveraged ETFs: Beyond the Volatility Drag Paradigm
Authors: Chung-Han Hsieh, Jow-Ran Chang, Hui Hsiang Chen
Date: 2025-04-28 00:00:00

A common belief is that leveraged ETFs (LETFs) suffer long-term performance decay due to \emph{volatility drag}. We show that this view is incomplete: LETF performance depends fundamentally on return autocorrelation and return dynamics. In markets with independent returns, LETFs exhibit positive expected compounding effects on their target multiples. In serially correlated markets, trends enhance returns, while mean reversion induces underperformance. With a unified framework incorporating AR(1) and AR-GARCH models, continuous-time regime switching, and flexible rebalancing frequencies, we demonstrate that return dynamics -- including return autocorrelation, volatility clustering, and regime persistence -- determine whether LETFs outperform or underperform their targets. Empirically, using about 20 years of SPDR S\&P~500 ETF and Nasdaq-100 ETF data, we confirm these theoretical predictions. Daily-rebalanced LETFs enhance returns in momentum-driven markets, whereas infrequent rebalancing mitigates losses in mean-reverting regimes.
The Memorization Problem: Can We Trust LLMs' Economic Forecasts?
Authors: Alejandro Lopez-Lira, Yuehua Tang, Mingyin Zhu
Date: 2025-04-20 00:00:00
Tasks: Memorization

Large language models (LLMs) cannot be trusted for economic forecasts during periods covered by their training data. We provide the first systematic evaluation of LLMs' memorization of economic and financial data, including major economic indicators, news headlines, stock returns, and conference calls. Our findings show that LLMs can perfectly recall the exact numerical values of key economic variables from before their knowledge cutoff dates. This recall appears to be randomly distributed across different dates and data types. This selective perfect memory creates a fundamental issue -- when testing forecasting capabilities before their knowledge cutoff dates, we cannot distinguish whether LLMs are forecasting or simply accessing memorized data. Explicit instructions to respect historical data boundaries fail to prevent LLMs from achieving recall-level accuracy in forecasting tasks. Further, LLMs seem exceptional at reconstructing masked entities from minimal contextual clues, suggesting that masking provides inadequate protection against motivated reasoning. Our findings raise concerns about using LLMs to forecast historical data or backtest trading strategies, as their apparent predictive success may merely reflect memorization rather than genuine economic insight. Any application where future knowledge would change LLMs' outputs can be affected by memorization. In contrast, consistent with the absence of data contamination, LLMs cannot recall data after their knowledge cutoff date.
Radner equilibrium with population growth
Authors: Jin Hyuk Choi, Kim Weston
Date: 2025-04-25 00:00:00

We prove the existence of a Radner equilibrium in a model with population growth and analyze the effects on asset prices. A finite population of agents grows indefinitely at a Poisson rate, while receiving unspanned income and choosing between consumption and investing into an annuity with infinitely-lived exponential preferences. After establishing the existence of an equilibrium for a truncated number of agents, we prove that an equilibrium exists for the model with unlimited population growth. Our numerics show that increasing the birth rate reduces oscillations in the equilibrium annuity price, and when younger agents prioritize the present more than older agents, the equilibrium annuity price rises compared to a uniform demographic.
Asian Basket Spread Options: A New Approximation Based on Stochastic Taylor Expansions
Authors: Fabien Le Floc'h
Date: 2025-04-22 00:00:00
Tasks: Numerical Integration

We present closed analytical approximations for the pricing of Asian basket spread options under the Black-Scholes model. The formulae are obtained by using a stochastic Taylor expansion around a log-normal proxy model and are found to be highly accurate for Asian and spread options in practice. Unlike other approaches, they do not require any numerical integration or root solving.
Collective Defined Contribution Schemes Without Intergenerational Cross-Subsidies
Authors: John Armstrong, James Dalby, Rohan Hobbs
Date: 2025-04-23 00:00:00

We present an architecture for managing Collective Defined Contribution (CDC) schemes. The current approach to UK CDC can be described as shared-indexation, where the nominal benefit of every member in a scheme receives the same level of indexation each year. The design of such schemes rely on the use of approximate discounting methodologies to value liabilities, and this leads to intergenerational cross-subsidies which can be large and unpredictable. We present an alternative approach which we call Collective-Drawdown CDC. This approach does not result in intergenerational cross-subsidies since all pooling is performed by explicit insurance contracts. It is therefore completely fair. Moreover, this scheme results in better pension outcomes when compared to shared-indexation CDC under the same model parameters.
Correlation and Beyond: Positive Definite Dependence Measures for Robust Inference, Flexible Scenarios, and Stress Testing for Financial Portfolios
Authors: JD Opdyke
Date: 2025-04-21 00:00:00
Tasks: valid

We live in a multivariate world, and effective modeling of financial portfolios, including their construction, allocation, forecasting, and risk analysis, simply is not possible without explicitly modeling the dependence structure of their assets. Dependence structure can drive portfolio results more than the combined effects of other parameters in investment and risk models, but the literature provides relatively little to define the finite-sample distributions of dependence measures in useable and useful ways under challenging, real-world financial data conditions. Yet this is exactly what is needed to make valid inferences about their estimates, and to use these inferences for essential purposes such as hypothesis testing, dynamic monitoring, realistic and granular scenario and reverse scenario analyses, and mitigating the effects of correlation breakdowns during market upheavals. This work develops a new and straightforward method, Nonparametric Angles-based Correlation (NAbC), for defining the finite-sample distributions of any dependence measure whose matrix of pairwise associations is positive definite (e.g. Pearsons, Kendalls, Spearmans, Tail Dependence Matrix, and others). The solution remains valid under marginal asset distributions characterized by notably different and varying degrees of serial correlation, non-stationarity, heavy-tailedness, and asymmetry. Importantly, NAbCs p-values and confidence intervals remain analytically consistent at both the matrix level and the pairwise cell level. Finally, NAbC maintains validity even when selected cells in the matrix are frozen for a given scenario or stress test, thus enabling flexible, granular, and realistic scenarios. NAbC stands alone in providing all of these capabilities simultaneously, and should prove to be a very useful means by which we can better understand and manage financial portfolios in our multivariate world.