pwc-archive/papers-with-abstracts huggingface dataset:
Application of CTS (Computer to Screen) Machine in Printing Industries for Process Improvement & Material Optimization
Authors: Tarequl Islam
Date: 2025-04-09 00:00:00

The printing and labeling industries are struggling to meet the need for more complex and dynamic design requirements coming from the customers. It is now crucial to implement technological advancements to manage workflow, productivity, process optimization, and continual improvement. There has never been a time when the imagery and embellishments of apparel has been more commercially viable as it is now. Images and text are fused directly to fabric by heat transfer printing and labeling. For screen development which is required for heat transfer label mass production, many industries are still using the conventional method of screen development process. A CTS (computer-to-screen) innovates the printing and labeling industries by enhancing workflow, lowering consumable consumptions and chemical usage, speeding up setup, guaranteeing flawless design, and raising the print quality of the producing screens. The study's objective is to assess how CTS machines are used and how they affect existing heat transfer screen development processes in one of Bangladesh's leading printing and labeling companies. The study's primary goal is to highlight and analyze how the use of CTS machines reduces material and operational costs by optimizing the process. Costs for CapEx and OpEx are computed and compared for using CTS technology before and after adoption. Savings data such as material, consumable, and operating cost savings versus depreciation and machine payback period analysis were taken into consideration. It is clear from this study that CTS machines in the printing and labeling industries can guarantee profitability on top of Capital Expenditures.
Density Approximation of Affine Jump Diffusions via Closed-Form Moment Matching
Authors: Yan-Feng Wu, Jian-Qiang Hu
Date: 2025-04-09 00:00:00
Tasks: Computational Efficiency, Form

We develop a recursive approach for deriving closed-form solutions to both conditional and unconditional moments of affine jump diffusions with state-independent jump intensities. Using these moment solutions, we construct closed-form density approximations (up to a normalization constant) via moment matching for both conditional and unconditional distributions. Our framework enables important financial applications, including efficient option pricing and exact simulation for affine jump diffusions. Numerical experiments demonstrate the method's superior computational efficiency compared to existing simulation techniques, while preserving numerical precision.
Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure
Authors: Minshuo Chen, Renyuan Xu, Yumin Xu, Ruixun Zhang
Date: 2025-04-09 00:00:00
Tasks: Econometrics, Portfolio Optimization

Financial scenario simulation is essential for risk management and portfolio optimization, yet it remains challenging especially in high-dimensional and small data settings common in finance. We propose a diffusion factor model that integrates latent factor structure into generative diffusion processes, bridging econometrics with modern generative AI to address the challenges of the curse of dimensionality and data scarcity in financial simulation. By exploiting the low-dimensional factor structure inherent in asset returns, we decompose the score function--a key component in diffusion models--using time-varying orthogonal projections, and this decomposition is incorporated into the design of neural network architectures. We derive rigorous statistical guarantees, establishing nonasymptotic error bounds for both score estimation at O(d^{5/2} n^{-2/(k+5)}) and generated distribution at O(d^{5/4} n^{-1/2(k+5)}), primarily driven by the intrinsic factor dimension k rather than the number of assets d, surpassing the dimension-dependent limits in the classical nonparametric statistics literature and making the framework viable for markets with thousands of assets. Numerical studies confirm superior performance in latent subspace recovery under small data regimes. Empirical analysis demonstrates the economic significance of our framework in constructing mean-variance optimal portfolios and factor portfolios. This work presents the first theoretical integration of factor structure with diffusion models, offering a principled approach for high-dimensional financial simulation with limited data. Our code is available at https://github.com/xymmmm00/diffusion_factor_model.
Generative Market Equilibrium Models with Stable Adversarial Learning via Reinforcement
Authors: Anastasis Kratsios, Xiaofei Shi, Qiang Sun, Zhanhao Zhang
Date: 2025-04-05 00:00:00
Tasks: Deep Reinforcement Learning

We present a general computational framework for solving continuous-time financial market equilibria under minimal modeling assumptions while incorporating realistic financial frictions, such as trading costs, and supporting multiple interacting agents. Inspired by generative adversarial networks (GANs), our approach employs a novel generative deep reinforcement learning framework with a decoupling feedback system embedded in the adversarial training loop, which we term as the \emph{reinforcement link}. This architecture stabilizes the training dynamics by incorporating feedback from the discriminator. Our theoretically guided feedback mechanism enables the decoupling of the equilibrium system, overcoming challenges that hinder conventional numerical algorithms. Experimentally, our algorithm not only learns but also provides testable predictions on how asset returns and volatilities emerge from the endogenous trading behavior of market participants, where traditional analytical methods fall short. The design of our model is further supported by an approximation guarantee.
QubitSwap: The Informational Edge in Decentralised Exchanges
Authors: Oliver Tronn Scott-Simons, Chris Colman, FrostByte
Date: 2025-03-24 00:00:00

Decentralised exchanges (DEXs) have transformed trading by enabling trustless, permissionless transactions, yet they face significant challenges such as impermanent loss and slippage, which undermine profitability for liquidity providers and traders. In this paper, we introduce QubitSwap, an innovative DEX model designed to tackle these issues through a hybrid approach that integrates an external oracle price with internal pool dynamics. This is achieved via a parameter $z$, which governs the balance between these price sources, creating a flexible and adaptive pricing mechanism. Through rigorous mathematical analysis, we derive a novel reserve function and pricing model that substantially reduces impermanent loss and slippage compared to traditional DEX frameworks. Notably, our results show that as $z$ approaches 1, slippage approaches zero, enhancing trading stability. QubitSwap marks a novel approach in DEX design, delivering a more efficient and resilient platform. This work not only advances the theoretical foundations of decentralised finance but also provides actionable solutions for the broader DeFi ecosystem.
Causal Portfolio Optimization: Principles and Sensitivity-Based Solutions
Authors: Alejandro Rodriguez Dominguez
Date: 2025-04-08 00:00:00
Tasks: Portfolio Optimization, Sensitivity

Fundamental and necessary principles for achieving efficient portfolio optimization based on asset and diversification dynamics are presented. The Commonality Principle is a necessary and sufficient condition for identifying optimal drivers of a portfolio in terms of its diversification dynamics. The proof relies on the Reichenbach Common Cause Principle, along with the fact that the sensitivities of portfolio constituents with respect to the common causal drivers are themselves causal. A conformal map preserves idiosyncratic diversification from the unconditional setting while optimizing systematic diversification on an embedded space of these sensitivities. Causal methodologies for combinatorial driver selection are presented, such as the use of Bayesian networks and correlation-based algorithms from Reichenbach's principle. Limitations of linear models in capturing causality are discussed, and included for completeness alongside more advanced models such as neural networks. Portfolio optimization methods are presented that map risk from the sensitivity space to other risk measures of interest. Finally, the work introduces a novel risk management framework based on Common Causal Manifolds, including both theoretical development and experimental validation. The sensitivity space is predicted along the common causal manifold, which is modeled as a causal time system. Sensitivities are forecasted using SDEs calibrated to data previously extracted from neural networks to move along the manifold via its tangent bundles. An optimization method is then proposed that accumulates information across future predicted tangent bundles on the common causal time system manifold. It aggregates sensitivity-based distance metrics along the trajectory to build a comprehensive sensitivity distance matrix. This matrix enables trajectory-wide optimal diversification, taking into account future dynamics.
On the Efficacy of Shorting Corporate Bonds as a Tail Risk Hedging Solution
Authors: Travis Cable, Amir Mani, Wei Qi, Georgios Sotiropoulos, Yiyuan Xiong
Date: 2025-04-03 00:00:00

United States (US) IG bonds typically trade at modest spreads over US Treasuries, reflecting the credit risk tied to a corporation's default potential. During market crises, IG spreads often widen and liquidity tends to decrease, likely due to increased credit risk (evidenced by higher IG Credit Default Index spreads) and the necessity for asset holders like mutual funds to liquidate assets, including IG credits, to manage margin calls, bolster cash reserves, or meet redemptions. These credit and liquidity premia occur during market drawdowns and tend to move non-linearly with the market. The research herein refers to this non-linearity (during periods of drawdown) as downside convexity, and shows that this market behavior can effectively be captured through a short position established in IG Exchange Traded Funds (ETFs). The following document details the construction of three signals: Momentum, Liquidity, and Credit, that can be used in combination to signal entries and exits into short IG positions to hedge a typical active bond portfolio (such as PIMIX). A dynamic hedge initiates the short when signals jointly correlate and point to significant future hedged return. The dynamic hedge removes when the short position's predicted hedged return begins to mean revert. This systematic hedge largely avoids IG Credit drawdowns, lowers absolute and downside risk, increases annualised returns and achieves higher Sortino ratios compared to the benchmark funds. The method is best suited to high carry, high active risk funds like PIMIX, though it also generalises to more conservative funds similar to DODIX.
Foreign Signal Radar
Authors: Wei Jiao
Date: 2025-04-10 00:00:00

We introduce a new machine learning approach to detect value-relevant foreign information for both domestic and multinational companies. Candidate foreign signals include lagged returns of stock markets and individual stocks across 47 foreign markets. By training over 100,000 models, we capture stock-specific, time-varying relationships between foreign signals and U.S. stock returns. Foreign signals exhibit out-of-sample return predictability for a subset of U.S. stocks across domestic and multinational companies. Valuable foreign signals are not concentrated in those largest foreign markets nor foreign firms in the same industry as U.S. firms. Signal importance analysis reveals the price discovery of foreign information is significantly slower for information from emerging and low-media-coverage markets and among stocks with lower foreign institutional ownership but is accelerated during the COVID-19 crisis. Our study suggests that machine learning-based investment strategies leveraging foreign signals can emerge as important mechanisms to improve the market efficiency of foreign information.
DBOT: Artificial Intelligence for Systematic Long-Term Investing
Authors: Vasant Dhar, João Sedoc
Date: 2025-04-08 00:00:00

Long-term investing was previously seen as requiring human judgment. With the advent of generative artificial intelligence (AI) systems, automated systematic long-term investing is now feasible. In this paper, we present DBOT, a system whose goal is to reason about valuation like Aswath Damodaran, who is a unique expert in the investment arena in terms of having published thousands of valuations on companies in addition to his numerous writings on the topic, which provide ready training data for an AI system. DBOT can value any publicly traded company. DBOT can also be back-tested, making its behavior and performance amenable to scientific inquiry. We compare DBOT to its analytic parent, Damodaran, and highlight the research challenges involved in raising its current capability to that of Damodaran's. Finally, we examine the implications of DBOT-like AI agents for the financial industry, especially how they will impact the role of human analysts in valuation.
Deep Hedging with Options Using the Implied Volatility Surface
Authors: Pascal François, Geneviève Gauthier, Frédéric Godin, Carlos O. Pérez-Mendoza
Date: 2025-04-08 00:00:00
Tasks: Management

We propose an enhanced deep hedging framework for index option portfolios, grounded in a realistic market simulator that captures the joint dynamics of S&P 500 returns and the full implied volatility surface. Our approach integrates surface-informed decisions with multiple hedging instruments and explicitly accounts for transaction costs. The hedging strategy also considers the variance risk premium embedded in the hedging instruments, enabling more informed and adaptive risk management. In this setting, state-dependent no-trade regions emerge naturally, improving rebalancing efficiency and hedging performance. Tested across simulated and historical data from 1996 to 2020, our method consistently outperforms traditional delta and delta-gamma hedging, demonstrating superior adaptability and risk reduction.
Polyspectral Mean based Time Series Clustering of Indian Stock Market
Authors: Dhrubajyoti Ghosh
Date: 2025-04-09 00:00:00
Tasks: Clustering, Time Series, Time Series Clustering

In this study, we employ k-means clustering algorithm of polyspectral means to analyze 49 stocks in the Indian stock market. We have used spectral and bispectral information obtained from the data, by using spectral and bispectral means with different weight functions that will give us varying insights into the temporal patterns of the stocks. In particular, the higher order polyspectral means can provide significantly more information than what we can gather from power spectra, and can also unveil nonlinear trends in a time series. Through rigorous analysis, we identify five distinctive clusters, uncovering nuanced market structures. Notably, one cluster emerges as that of a conglomerate powerhouse, featuring ADANI, BIRLA, TATA, and unexpectedly, government-owned bank SBI. Another cluster spotlights the IT sector with WIPRO and TCS, while a third combines private banks, government entities, and RELIANCE. The final cluster comprises publicly traded companies with dispersed ownership. Such clustering of stocks sheds light on intricate financial relationships within the stock market, providing valuable insights for investors and analysts navigating the dynamic landscape of the Indian stock market.
Financial resilience of agricultural and food production companies in Spain: A compositional cluster analysis of the impact of the Ukraine-Russia war (2021-2023)
Authors: Mike Hernandez Romero, Germà Coenders
Date: 2025-04-08 00:00:00

This study analyzes the financial resilience of agricultural and food production companies in Spain amid the Ukraine-Russia war using cluster analysis based on financial ratios. This research utilizes centered log-ratios to transform financial ratios for compositional data analysis. The dataset comprises financial information from 1197 firms in Spain's agricultural and food sectors over the period 2021-2023. The analysis reveals distinct clusters of firms with varying financial performance, characterized by metrics of solvency and profitability. The results highlight an increase in resilient firms by 2023, underscoring sectoral adaptation to the conflict's economic challenges. These findings together provide insights for stakeholders and policymakers to improve sectorial stability and strategic planning.
Financial Analysis: Intelligent Financial Data Analysis System Based on LLM-RAG
Authors: Jingru Wang, Wen Ding, Xiaotong Zhu
Date: 2025-03-20 00:00:00
Tasks: Financial Analysis, RAG, Retrieval, Retrieval-augmented Generation

In the modern financial sector, the exponential growth of data has made efficient and accurate financial data analysis increasingly crucial. Traditional methods, such as statistical analysis and rule-based systems, often struggle to process and derive meaningful insights from complex financial information effectively. These conventional approaches face inherent limitations in handling unstructured data, capturing intricate market patterns, and adapting to rapidly evolving financial contexts, resulting in reduced accuracy and delayed decision-making processes. To address these challenges, this paper presents an intelligent financial data analysis system that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) technology. Our system incorporates three key components: a specialized preprocessing module for financial data standardization, an efficient vector-based storage and retrieval system, and a RAG-enhanced query processing module. Using the NASDAQ financial fundamentals dataset from 2010 to 2023, we conducted comprehensive experiments to evaluate system performance. Results demonstrate significant improvements across multiple metrics: the fully optimized configuration (gpt-3.5-turbo-1106+RAG) achieved 78.6% accuracy and 89.2% recall, surpassing the baseline model by 23 percentage points in accuracy while reducing response time by 34.8%. The system also showed enhanced efficiency in handling complex financial queries, though with a moderate increase in memory utilization. Our findings validate the effectiveness of integrating RAG technology with LLMs for financial analysis tasks and provide valuable insights for future developments in intelligent financial data processing systems.
Optimal Execution and Macroscopic Market Making
Authors: Ivan Guo, Shijia Jin
Date: 2025-04-09 00:00:00

We propose a stochastic game modelling the strategic interaction between market makers and traders of optimal execution type. For traders, the permanent price impact commonly attributed to them is replaced by quoting strategies implemented by market makers. For market makers, order flows become endogenous, driven by tactical traders rather than assumed exogenously. Using the forward-backward stochastic differential equation (FBSDE) characterization of Nash equilibria, we establish a local well-posedness result for the general game. In the specific Almgren-Chriss-Avellaneda-Stoikov model, a decoupling approach guarantees the global well-posedness of the FBSDE system via the well-posedness of an associated backward stochastic Riccati equation. Finally, by introducing small diffusion terms into the inventory processes, global well-posedness is achieved for the approximation game.
NNN: Next-Generation Neural Networks for Marketing Measurement
Authors: Thomas Mulc, Mike Anderson, Paul Cubre, Huikun Zhang, Ivy Liu, Saket Kumar
Date: 2025-04-08 00:00:00
Tasks: Marketing

We present NNN, an experimental Transformer-based neural network approach to marketing measurement. Unlike Marketing Mix Models (MMMs) which rely on scalar inputs and parametric decay functions, NNN uses rich embeddings to capture both quantitative and qualitative aspects of marketing and organic channels (e.g., search queries, ad creatives). This, combined with its attention mechanism, potentially enables NNN to model complex interactions, capture long-term effects, and improve sales attribution accuracy. We show that L1 regularization permits the use of such expressive models in typical data-constrained settings. Evaluating NNN on simulated and real-world data demonstrates its efficacy, particularly through considerable improvement in predictive power. In addition to marketing measurement, the NNN framework can provide valuable, complementary insights through model probing, such as evaluating keyword or creative effectiveness.
Beyond the Hype: Embeddings vs. Prompting for Multiclass Classification Tasks
Authors: Marios Kokkodis, Richard Demsyn-Jones, Vijay Raghavan
Date: 2025-04-05 00:00:00

Are traditional classification approaches irrelevant in this era of AI hype? We show that there are multiclass classification problems where predictive models holistically outperform LLM prompt-based frameworks. Given text and images from home-service project descriptions provided by Thumbtack customers, we build embeddings-based softmax models that predict the professional category (e.g., handyman, bathroom remodeling) associated with each problem description. We then compare against prompts that ask state-of-the-art LLM models to solve the same problem. We find that the embeddings approach outperforms the best LLM prompts in terms of accuracy, calibration, latency, and financial cost. In particular, the embeddings approach has 49.5% higher accuracy than the prompting approach, and its superiority is consistent across text-only, image-only, and text-image problem descriptions. Furthermore, it yields well-calibrated probabilities, which we later use as confidence signals to provide contextualized user experience during deployment. On the contrary, prompting scores are overly uninformative. Finally, the embeddings approach is 14 and 81 times faster than prompting in processing images and text respectively, while under realistic deployment assumptions, it can be up to 10 times cheaper. Based on these results, we deployed a variation of the embeddings approach, and through A/B testing we observed performance consistent with our offline analysis. Our study shows that for multiclass classification problems that can leverage proprietary datasets, an embeddings-based approach may yield unequivocally better results. Hence, scientists, practitioners, engineers, and business leaders can use our study to go beyond the hype and consider appropriate predictive models for their classification use cases.
Constructing the Truth: Text Mining and Linguistic Networks in Public Hearings of Case 03 of the Special Jurisdiction for Peace (JEP)
Authors: Juan Sosa, Alejandro Urrego-López, Cesar Prieto, Emma J. Camargo-Díaz
Date: 2025-04-06 00:00:00

Case 03 of the Special Jurisdiction for Peace (JEP), focused on the so-called false positives in Colombia, represents one of the most harrowing episodes of the Colombian armed conflict. This article proposes an innovative methodology based on natural language analysis and semantic co-occurrence models to explore, systematize, and visualize narrative patterns present in the public hearings of victims and appearing parties. By constructing skipgram networks and analyzing their modularity, the study identifies thematic clusters that reveal regional and procedural status differences, providing empirical evidence on dynamics of victimization, responsibility, and acknowledgment in this case. This computational approach contributes to the collective construction of both judicial and extrajudicial truth, offering replicable tools for other transitional justice cases. The work is grounded in the pillars of truth, justice, reparation, and non-repetition, proposing a critical and in-depth reading of contested memories.