pwc-archive/papers-with-abstracts huggingface dataset:
Superinfection and the hypnozoite reservoir for Plasmodium vivax: a multitype branching process approximation
Authors: Somya Mehra, Peter G. Taylor
Date: 2025-02-24 00:00:00
Tasks: Attribute

Plasmodium vivax malaria is a mosquito-borne disease of significant public health importance. A defining feature of the within-host biology of P. vivax is the accrual of a hypnozoite reservoir, comprising a bank of quiescent parasites in the liver that are capable of causing relapsing blood-stage infections upon activation. Superinfection, characterised by composite blood-stage infections with parasites derived from multiple mosquito inoculation or hypnozoite activation events, is another important attribute. We have previously developed a stochastic epidemic model of P. vivax malaria, formulated as a Markov population process with countably infinitely-many types, that is adjusted for both hypnozoite accrual and blood-stage superinfection. Here, we construct a Markovian branching process with countably infinitely-many types to approximate the early stages of this epidemic model. With $P_M$ denoting the mosquito population size, we consider the limit $P_M \to \infty$ when the ratio of the mosquito and human populations is held fixed. We use a classical coupling argument to obtain a total variation bound of order $O(P_M^{2 \kappa - 1})$ that is valid until $o(P_M^\kappa)$ human-to-mosquito and mosquito-to-human transmission events have occurred, where $\kappa < 1/2$ is an arbitrary constant. We characterise the probability of global disease extinction under the branching process to approximate the probability of elimination, as opposed to sustained endemic transmission, when the epidemic model is initialised with low-level human and/or mosquito infection. We apply our model to two scenarios of epidemiological interest, namely the re-introduction of P. vivax malaria in a region where elimination has previously been achieved; and a mass drug administration campaign with population-wide depletion of the hypnozoite reservoir.
Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome
Authors: Adolfo Ruiz-Sanmartín, Vicent Ribas, David Suñol, Luis Chiscano-Camón, Laura Martín, Iván Bajaña, Juliana Bastida, Nieves Larrosa, Juan José González, M Dolores Carrasco, Núria Canela, Ricard Ferrer, Juan Carlos Ruiz-Rodrígue
Date: 2025-02-25 00:00:00
Tasks: Specificity

Background: The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and non-infectious systemic inflammatory response syndrome (NISIRS). Methods: Prospective observational study of a cohort of septic patients activated by the Sepsis Code and patients admitted with NISIRS, during the period 2016-2017. A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects. Subsequently, using recursive feature elimination (RFE) classification and cross-validation with a vector classifier, an association of these proteins in patients with sepsis compared to patients with NISIRS. The protein-protein interaction network was analyzed with String software. Results: A total of 277 patients (141 with sepsis and 136 with NISIRS) were included. After performing RFE, 25 proteins in the study patient cohort showed statistical significance, with an accuracy of 0.960, specificity of 0.920, sensitivity of 0.973, and an AUC of 0.985. Of these, 14 proteins (vWF, PPBP, C5, C1RL, FCN3, SAA2, ORM1, ITIH3, GSN, C1QA, CA1, CFB, C3, LBP) have a greater relationship with sepsis while 11 proteins (FN1, IGFALS, SERPINA4, APOE, APOH, C6, SERPINA3, AHSG, LUM, ITIH2, SAA1) are more expressed in NISIRS.
Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning
Authors: Runzhong Wang, Rui-Xi Wang, Mrunali Manjrekar, Connor W. Coley
Date: 2025-02-25 00:00:00
Tasks: Graph Matching, Retrieval, Retrieval-augmented Generation

Molecular machine learning has gained popularity with the advancements of geometric deep learning. In parallel, retrieval-augmented generation has become a principled approach commonly used with language models. However, the optimal integration of retrieval augmentation into molecular machine learning remains unclear. Graph neural networks stand to benefit from clever matching to understand the structural alignment of retrieved molecules to a query molecule. Neural graph matching offers a compelling solution by explicitly modeling node and edge affinities between two structural graphs while employing a noise-robust, end-to-end neural network to learn affinity metrics. We apply this approach to mass spectrum simulation and introduce MARASON, a novel model that incorporates neural graph matching to enhance a fragmentation-based neural network. Experimental results highlight the effectiveness of our design, with MARASON achieving 28% top-1 accuracy, a substantial improvement over the non-retrieval state-of-the-art accuracy of 19%. Moreover, MARASON outperforms both naive retrieval-augmented generation methods and traditional graph matching approaches.
Integrating protein sequence embeddings with structure via graph-based deep learning for the prediction of single-residue properties
Authors: Kevin Michalewicz, Mauricio Barahona, Barbara Bravi
Date: 2025-02-24 00:00:00
Tasks: Large Language Model

Understanding the intertwined contributions of amino acid sequence and spatial structure is essential to explain protein behaviour. Here, we introduce INFUSSE (Integrated Network Framework Unifying Structure and Sequence Embeddings), a Deep Learning framework that combines sequence embeddings, generated by a Large Language Model (LLM), with graph-based representations of protein structures, integrated through a diffusive Graph Convolutional Network (diff-GCN), to predict single-residue properties within proteins. Our approach follows two steps. First, we fine-tune LLM sequence embeddings obtained from bidirectional transformers to make predictions from protein sequence alone. Second, we combine these enriched sequence representations with a geometric graph Laplacian within diff-GCN to refine the initial predictions. This approach leads to improved predictions while allowing us to systematically disentangle the contribution of sequence and structure. We illustrate our framework by applying it to the prediction of local residue flexibility (B-factors) of antibody-antigen complexes, and show that it provides improved performance compared to current Machine Learning (ML) approaches. The addition of structural information via geometric graphs is shown to enhance predictions especially for intrinsically disordered regions, protein-protein interaction sites, and highly variable amino acid positions.
Simplified model of immunotherapy for glioblastoma multiforme: cancer stem cells hypothesis perspective
Authors: Wiktor Jochymczyk, Urszula Foryś
Date: 2025-02-09 00:00:00

Despite ongoing efforts in cancer research, a fully effective treatment for glioblastoma multiforme (GBM) is still unknown. Since adoptive cell transfer immunotherapy is one of the potential cure candidates, efforts have been made to assess its effectiveness using mathematical modeling. In this paper, we consider a model of GBM immunotherapy proposed by Abernathy and Burke (2016), which also takes into account the dynamics of cancer stem cells, i.e., the type of cancer cells that are hypothesized to be largely responsible for cancer recurrence. We modify the initial ODE system by applying simplifying assumptions and analyze the existence and stability of steady states of the obtained simplified model depending on the treatment levels.
Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management
Authors: Lei Zhao, Lin Cai, Wu-Sheng Lu
Date: 2025-02-25 00:00:00
Tasks: Distributional Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning

In the field of financial derivatives trading, managing volatility risk is crucial for protecting investment portfolios from market changes. Traditional Vega hedging strategies, which often rely on basic and rule-based models, are hard to adapt well to rapidly changing market conditions. We introduce a new framework for dynamic Vega hedging, the Adaptive Nesterov Accelerated Distributional Deep Hedging (ANADDH), which combines distributional reinforcement learning with a tailored design based on adaptive Nesterov acceleration. This approach improves the learning process in complex financial environments by modeling the hedging efficiency distribution, providing a more accurate and responsive hedging strategy. The design of adaptive Nesterov acceleration refines gradient momentum adjustments, significantly enhancing the stability and speed of convergence of the model. Through empirical analysis and comparisons, our method demonstrates substantial performance gains over existing hedging techniques. Our results confirm that this innovative combination of distributional reinforcement learning with the proposed optimization techniques improves financial risk management and highlights the practical benefits of implementing advanced neural network architectures in the finance sector.
Event-Based Limit Order Book Simulation under a Neural Hawkes Process: Application in Market-Making
Authors: Luca Lalor, Anatoliy Swishchuk
Date: 2025-02-24 00:00:00
Tasks: Deep Reinforcement Learning

In this paper, we propose an event-driven Limit Order Book (LOB) model that captures twelve of the most observed LOB events in exchange-based financial markets. To model these events, we propose using the state-of-the-art Neural Hawkes process, a more robust alternative to traditional Hawkes process models. More specifically, this model captures the dynamic relationships between different event types, particularly their long- and short-term interactions, using a Long Short-Term Memory neural network. Using this framework, we construct a midprice process that captures the event-driven behavior of the LOB by simulating high-frequency dynamics like how they appear in real financial markets. The empirical results show that our model captures many of the broader characteristics of the price fluctuations, particularly in terms of their overall volatility. We apply this LOB simulation model within a Deep Reinforcement Learning Market-Making framework, where the trading agent can now complete trade order fills in a manner that closely resembles real-market trade execution. Here, we also compare the results of the simulated model with those from real data, highlighting how the overall performance and the distribution of trade order fills closely align with the same analysis on real data.
Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis
Authors: Evans Kiptoo Korir, Zsolt Vizi
Date: 2025-02-24 00:00:00
Tasks: Clustering, Dynamic Time Warping, Time Series, Time Series Analysis

This study examines insurance companies' financial performance and reporting trends within the medical sector using advanced clustering techniques to identify distinct patterns. Four clusters were identified by analyzing financial ratios and time series data, each representing unique financial performance and reporting consistency combinations. Dynamic Time Warping (DTW) and KMeans clustering were employed to capture temporal variations and uncover key insights into company behaviors. The findings reveal that resilient performers consistently report and have financial stability, making them reliable options for policyholders. In contrast, clusters of underperforming companies and those with reporting gaps highlight operational challenges and issues related to data consistency. These insights emphasize the importance of transparency and timely reporting to ensure the sector's resilience. This study contributes to the literature by integrating time series analysis into financial clustering, offering practical recommendations for improving data governance and financial stability in the insurance sector. Future research could further investigate non-financial indicators and explore alternative clustering methods to provide a deeper understanding of performance dynamics.
Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies
Authors: Zheli Xiong
Date: 2025-02-23 00:00:00
Tasks: Decision Making, Reinforcement Learning (RL)

This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging classifier models to enhance performance. By combining RL algorithms such as A2C, PPO, and SAC with traditional classifiers like Support Vector Machines (SVM), Decision Trees, and Logistic Regression, we investigate how different classifier groups can be integrated to improve risk-return trade-offs. The study evaluates the effectiveness of various ensemble methods, comparing them with individual RL models across key financial metrics, including Cumulative Returns, Sharpe Ratios (SR), Calmar Ratios, and Maximum Drawdown (MDD). Our results demonstrate that ensemble methods consistently outperform base models in terms of risk-adjusted returns, providing better management of drawdowns and overall stability. However, we identify the sensitivity of ensemble performance to the choice of variance threshold {\tau}, highlighting the importance of dynamic {\tau} adjustment to achieve optimal performance. This study emphasizes the value of combining RL with classifiers for adaptive decision-making, with implications for financial trading, robotics, and other dynamic environments.
Pursuing Top Growth with Novel Loss Function
Authors: Ruoyu Guo, Haochen Qiu
Date: 2025-02-20 00:00:00

Making consistently profitable financial decisions in a continuously evolving and volatile stock market has always been a difficult task. Professionals from different disciplines have developed foundational theories to anticipate price movement and evaluate securities such as the famed Capital Asset Pricing Model (CAPM). In recent years, the role of artificial intelligence (AI) in asset pricing has been growing. Although the black-box nature of deep learning models lacks interpretability, they have continued to solidify their position in the financial industry. We aim to further enhance AI's potential and utility by introducing a return-weighted loss function that will drive top growth while providing the ML models a limited amount of information. Using only publicly accessible stock data (open/close/high/low, trading volume, sector information) and several technical indicators constructed from them, we propose an efficient daily trading system that detects top growth opportunities. Our best models achieve 61.73% annual return on daily rebalancing with an annualized Sharpe Ratio of 1.18 over 1340 testing days from 2019 to 2024, and 37.61% annual return with an annualized Sharpe Ratio of 0.97 over 1360 testing days from 2005 to 2010. The main drivers for success, especially independent of any domain knowledge, are the novel return-weighted loss function, the integration of categorical and continuous data, and the ML model architecture. We also demonstrate the superiority of our novel loss function over traditional loss functions via several performance metrics and statistical evidence.
Currency Arbitrage Optimization using Quantum Annealing, QAOA and Constraint Mapping
Authors: Sangram Deshpande, Elin Ranjan Das, Frank Mueller
Date: 2025-02-08 00:00:00
Tasks: Combinatorial Optimization

Currency arbitrage capitalizes on price discrepancies in currency exchange rates between markets to produce profits with minimal risk. By employing a combinatorial optimization problem, one can ascertain optimal paths within directed graphs, thereby facilitating the efficient identification of profitable trading routes. This research investigates the methodologies of quantum annealing and gate-based quantum computing in relation to the currency arbitrage problem. In this study, we implement the Quantum Approximate Optimization Algorithm (QAOA) utilizing Qiskit version 1.2. In order to optimize the parameters of QAOA, we perform simulations utilizing the AerSimulator and carry out experiments in simulation. Furthermore, we present an NchooseK-based methodology utilizing D-Wave's Ocean suite. This methodology enables a comparison of the effectiveness of quantum techniques in identifying optimal arbitrage paths. The results of our study enhance the existing literature on the application of quantum computing in financial optimization challenges, emphasizing both the prospective benefits and the present limitations of these developing technologies in real-world scenarios.
Dynamic Factor Model-Based Multiperiod Mean-Variance Portfolio Selection with Portfolio Constraints
Authors: Jianjun Gao, Chengneng Jin, Yun Shi, Xiangyu Cui
Date: 2025-02-25 00:00:00

Motivated by practical applications, we explore the constrained multi-period mean-variance portfolio selection problem within a market characterized by a dynamic factor model. This model captures predictability in asset returns driven by state variables and incorporates cone-type portfolio constraints that are crucial in practice. The model is broad enough to encompass various dynamic factor frameworks, including practical considerations such as no-short-selling and cardinality constraints. We derive a semi-analytical optimal solution using dynamic programming, revealing it as a piecewise linear feedback policy to wealth, with all factors embedded within the allocation vectors. Additionally, we demonstrate that the portfolio policies are determined by two specific stochastic processes resulting from the stochastic optimizations, for which we provide detailed algorithms. These processes reflect the investor's assessment of future investment opportunities and play a crucial role in characterizing the time consistency and efficiency of the optimal policy through the variance-optimal signed supermartingale measure of the market. We present numerical examples that illustrate the model's application in various settings. Using real market data, we investigate how the factors influence portfolio policies and demonstrate that incorporating the factor structure may enhance out-of-sample performance.
Comparative Study of Monte Carlo and Quasi-Monte Carlo Techniques for Enhanced Derivative Pricing
Authors: Giacomo Case
Date: 2025-02-24 00:00:00

This study presents a comparative analysis of Monte Carlo (MC) and quasi-Monte Carlo (QMC) methods in the context of derivative pricing, emphasizing convergence rates and the curse of dimensionality. After a concise overview of traditional Monte Carlo techniques for evaluating expectations of derivative securities, the paper introduces quasi-Monte Carlo methods, which leverage low-discrepancy sequences to achieve more uniformly distributed sample points without relying on randomness. Theoretical insights highlight that QMC methods can attain superior convergence rates of $O(1/n^{1-\epsilon})$ compared to the standard MC rate of $O(1/\sqrt{n})$, where $\epsilon>0$. Numerical experiments are conducted on two types of options: geometric basket call options and Asian call options. For the geometric basket options, a five-dimensional setting under the Black-Scholes framework is utilized, comparing the performance of Sobol' and Faure low-discrepancy sequences against standard Monte Carlo simulations. Results demonstrate a significant reduction in root mean square error for QMC methods as the number of sample points increases. Similarly, for Asian call options, incorporating a Brownian bridge construction with RQMC further enhances accuracy and convergence efficiency. The findings confirm that quasi-Monte Carlo methods offer substantial improvements over traditional Monte Carlo approaches in derivative pricing, particularly in scenarios with moderate dimensionality.
Robust and Efficient Deep Hedging via Linearized Objective Neural Network
Authors: Lei Zhao, Lin Cai
Date: 2025-02-25 00:00:00
Tasks: Management

Deep hedging represents a cutting-edge approach to risk management for financial derivatives by leveraging the power of deep learning. However, existing methods often face challenges related to computational inefficiency, sensitivity to noisy data, and optimization complexity, limiting their practical applicability in dynamic and volatile markets. To address these limitations, we propose Deep Hedging with Linearized-objective Neural Network (DHLNN), a robust and generalizable framework that enhances the training procedure of deep learning models. By integrating a periodic fixed-gradient optimization method with linearized training dynamics, DHLNN stabilizes the training process, accelerates convergence, and improves robustness to noisy financial data. The framework incorporates trajectory-wide optimization and Black-Scholes Delta anchoring, ensuring alignment with established financial theory while maintaining flexibility to adapt to real-world market conditions. Extensive experiments on synthetic and real market data validate the effectiveness of DHLNN, demonstrating its ability to achieve faster convergence, improved stability, and superior hedging performance across diverse market scenarios.
Recurrent Neural Networks for Dynamic VWAP Execution: Adaptive Trading Strategies with Temporal Kolmogorov-Arnold Networks
Authors: Remi Genet
Date: 2025-02-25 00:00:00
Tasks: Computational Efficiency, Kolmogorov-Arnold Networks

The execution of Volume Weighted Average Price (VWAP) orders remains a critical challenge in modern financial markets, particularly as trading volumes and market complexity continue to increase. In my previous work arXiv:2502.13722, I introduced a novel deep learning approach that demonstrated significant improvements over traditional VWAP execution methods by directly optimizing the execution problem rather than relying on volume curve predictions. However, that model was static because it employed the fully linear approach described in arXiv:2410.21448, which is not designed for dynamic adjustment. This paper extends that foundation by developing a dynamic neural VWAP framework that adapts to evolving market conditions in real time. We introduce two key innovations: first, the integration of recurrent neural networks to capture complex temporal dependencies in market dynamics, and second, a sophisticated dynamic adjustment mechanism that continuously optimizes execution decisions based on market feedback. The empirical analysis, conducted across five major cryptocurrency markets, demonstrates that this dynamic approach achieves substantial improvements over both traditional methods and our previous static implementation, with execution performance gains of 10 to 15% in liquid markets and consistent outperformance across varying conditions. These results suggest that adaptive neural architectures can effectively address the challenges of modern VWAP execution while maintaining computational efficiency suitable for practical deployment.
Analysis of the Impact of the Union Budget Announcements on the Indian Stock Market: A Fractal Perspective
Authors: Mridul Patel, Geetika Verma, Andrew Eberhard, Asha Rao, Pankaj Kumar
Date: 2025-02-18 00:00:00

The stock market closely monitors macroeconomic policy announcements, such as annual budget events, due to their substantial influence on various economic participants. These events tend to impact the stock markets initially before affecting the real sector. Our study aims to analyze the effects of the budget on the Indian stock market, specifically focusing on the announcement for the year 2024. We will compare this with the years 2023, 2022, and 2020, assessing its impact on the NIFTY50 index using average abnormal return (AAR) and cumulative average abnormal return (CAAR) over a period of -15 and +15 days, including the budget day. This study utilizes an innovative approach involving the fractal interpolation function, paired with fractal dimensional analysis, to study the fluctuations arising from budget announcements. The fractal perspective on the data offers an effective framework for understanding complex variations.