pwc-archive/papers-with-abstracts huggingface dataset:
Marketron games: Self-propelling stocks vs dumb money and metastable dynamics of the Good, Bad and Ugly markets
Authors: I. Halperin, A. Itkin
Date: 2025-01-22 00:00:00

We present a model of price formation in an inelastic market whose dynamics are partially driven by both money flows and their impact on asset prices. The money flow to the market is viewed as an investment policy of outside investors. For the price impact effect, we use an impact function that incorporates the phenomena of market inelasticity and saturation from new money (the $dumb \; money$ effect). Due to the dependence of market investors' flows on market performance, the model implies a feedback mechanism that gives rise to nonlinear dynamics. Consequently, the market price dynamics are seen as a nonlinear diffusion of a particle (the $marketron$) in a two-dimensional space formed by the log-price $x$ and a memory variable $y$. The latter stores information about past money flows, so that the dynamics are non-Markovian in the log price $x$ alone, but Markovian in the pair $(x,y)$, bearing a strong resemblance to spiking neuron models in neuroscience. In addition to market flows, the model dynamics are partially driven by return predictors, modeled as unobservable Ornstein-Uhlenbeck processes. By using a new interpretation of predictive signals as $self$-$propulsion$ components of the price dynamics, we treat the marketron as an active particle, amenable to methods developed in the physics of active matter. We show that, depending on the choice of parameters, our model can produce a rich variety of interesting dynamic scenarios. In particular, it predicts three distinct regimes of the market, which we call the $Good$, the $Bad$, and the $Ugly$ markets. The latter regime describes a scenario of a total market collapse or, alternatively, a corporate default event, depending on whether our model is applied to the whole market or an individual stock.
(Non-Monotonic) Effects of Productivity and Credit Constraints on Equilibrium Aggregate Production in General Equilibrium Models with Heterogeneous Producers
Authors: Ngoc-Sang Pham
Date: 2025-01-22 00:00:00

In a market economy, the aggregate production level depends not only on the aggregate variables but also on the distribution of individual characteristics (e.g., productivity, credit limit, ...). We point out that, due to financial frictions, the equilibrium aggregate production may be non-monotonic in both individual productivity and credit limit. We provide conditions under which this phenomenon happens. By consequence, improving productivity or relaxing credit limit of firms may not necessarily be beneficial to economic development.
Defaultable bond liquidity spread estimation: an option-based approach
Authors: Pietro Rossi, Paolo Spezzati, Riccardo Tedeschi
Date: 2025-01-20 00:00:00

This paper extends an option-theoretic approach to estimate liquidity spreads for corporate bonds. Inspired by Longstaff's equity market framework and subsequent work by Koziol and Sauerbier on risk-free zero-coupon bonds, the model views liquidity as a look-back option. The model accounts for the interplay of risk-free rate volatility and credit risk. A numerical analysis highlights the impact of these factors on the liquidity spread, particularly for bonds with different maturities and credit ratings. The methodology is applied to estimate the liquidity spread for unquoted bonds, with a specific case study on the Republic of Italy's debt, leveraging market data to calibrate model parameters and classify liquid versus illiquid emissions. This approach provides a robust tool for pricing illiquid bonds, emphasizing the importance of marketability in debt security valuation.
A statistical technique for cleaning option price data
Authors: Jaco Visagie
Date: 2025-01-19 00:00:00

Recorded option pricing datasets are not always freely available. Additionally, these datasets often contain numerous prices which are either higher or lower than can reasonably be expected. Various reasons for these unexpected observations are possible, including human error in the recording of the details associated with the option in question. In order for the analyses performed on these datasets to be reliable, it is necessary to identify and remove these options from the dataset. In this paper, we list three distinct problems often found in recorded option price datasets alongside means of addressing these. The methods used are justified using sound statistical reasoning and remove option prices violating the standard assumption of no arbitrage. An attractive aspect of the proposed technique is that no option pricing model-based assumptions are used. Although the discussion is restricted to European options, the procedure is easily modified for use with exotic options as well. As a final contribution, the paper contains a link to six option pricing datasets which have already been cleaned using the proposed methods and can be freely used by researchers.
The role of FDI along transitional dynamics of the host country in an endogenous growth model
Authors: Ngoc-Sang Pham, Thanh Tam Nguyen-Huu
Date: 2025-01-21 00:00:00

We investigate the role of foreign direct investment (FDI) in the transitional dynamics of host countries by using an optimal growth model. FDI may be beneficial for the host country because local people can work for multinational firms to get a favorable salary. However, if the host country only focuses on FDI, it may face a middle-income trap. We show that if the host country invests in research and development, its economy may have sustained growth. Moreover, in this case, FDI helps the host country only at the first stages of its development process.
Optimizing Portfolios with Pakistan-Exposed ETFs: Risk and Performance Insight
Authors: Ali Jaffri, Abootaleb Shirvani, Ayush Jha, Svetlozar T. Rachev, Frank J. Fabozzi
Date: 2025-01-23 00:00:00
Tasks: Descriptive

This study examines the investment landscape of Pakistan as an emerging and frontier market, focusing on implications for international investors, particularly those in the United States, through exchange-traded funds (ETFs) with exposure to Pakistan. The analysis encompasses 30 ETFs with varying degrees of exposure to Pakistan, covering the period from January 1, 2016, to February 2024. This research highlights the potential benefits and risks associated with investing in these ETFs, emphasizing the importance of thorough risk assessments and portfolio performance comparisons. By providing descriptive statistics and performance metrics based on historical optimization, this paper aims to equip investors with the necessary insights to make informed decisions when optimizing their portfolios with Pakistan-exposed ETFs. The second part of the paper introduces and assesses dynamic optimization methodologies. This section is designed to explore the adaptability and performance metrics of dynamic optimization techniques in comparison with conventional historical optimization methods. By integrating dynamic optimization into the investigation, this research aims to offer insights into the efficacy of these contrasting methodologies in the context of Pakistan-exposed ETFs. The findings underscore the significance of Pakistan's market dynamics within the broader context of emerging markets, offering a pathway for diversification and potential growth in investment strategies.
Can optimal diversification beat the naive 1/N strategy in a highly correlated market? Empirical evidence from cryptocurrencies
Authors: Heming Chen
Date: 2025-01-22 00:00:00

This study systematically examines how several alternative approaches considered affect three aspects that determine portfolio performance (the gross return, the transaction costs and the portfolio risk). We find that it is difficult to exploit the possible predictability of asset returns. However, the predictability of asset return volatility produces obvious economic value, although in a highly correlated cryptocurrencies market.
Pontryagin-Guided Deep Learning for Large-Scale Constrained Dynamic Portfolio Choice
Authors: Jeonggyu Huh, Jaegi Jeon, Hyeng Keun Koo, Byung Hwa Lim
Date: 2025-01-22 00:00:00
Tasks: Deep Learning, Portfolio Optimization

We present a Pontryagin-Guided Direct Policy Optimization (PG-DPO) method for constrained dynamic portfolio choice - incorporating consumption and multi-asset investment - that scales to thousands of risky assets. By combining neural-network controls with Pontryagin's Maximum Principle (PMP), it circumvents the curse of dimensionality that renders dynamic programming (DP) grids intractable beyond a handful of assets. Unlike value-based PDE or BSDE approaches, PG-DPO enforces PMP conditions at each gradient step, naturally accommodating no-short-selling or borrowing constraints and optional consumption bounds. A "one-shot" variant rapidly computes Pontryagin-optimal controls after a brief warm-up, leading to substantially higher accuracy than naive baselines. On modern GPUs, near-optimal solutions often emerge within just one or two minutes of training. Numerical experiments confirm that, for up to 1,000 assets, PG-DPO accurately recovers the known closed-form solution in the unconstrained case and remains tractable under constraints -- far exceeding the longstanding DP-based limit of around seven assets.
Stochastic Optimal Control of Iron Condor Portfolios for Profitability and Risk Management
Authors: Hanyue Huang, Qiguo Sun, XiBei Yang
Date: 2025-01-06 00:00:00
Tasks: Management, Portfolio Optimization

Previous research on option strategies has primarily focused on their behavior near expiration, with limited attention to the transient value process of the portfolio. In this paper, we formulate Iron Condor portfolio optimization as a stochastic optimal control problem, examining the impact of the control process \( u(k_i, \tau) \) on the portfolio's potential profitability and risk. By assuming the underlying price process as a bounded martingale within $[K_1, K_2]$, we prove that the portfolio with a strike structure of $k_1 < k_2 = K_2 < S_t < k_3 = K_3 < k_4$ has a submartingale value process, which results in the optimal stopping time aligning with the expiration date $\tau = T$. Moreover, we construct a data generator based on the Rough Heston model to investigate general scenarios through simulation. The results show that asymmetric, left-biased Iron Condor portfolios with $\tau = T$ are optimal in SPX markets, balancing profitability and risk management. Deep out-of-the-money strategies improve profitability and success rates at the cost of introducing extreme losses, which can be alleviated by using an optimal stopping strategy. Except for the left-biased portfolios $\tau$ generally falls within the range of [50\%,75\%] of total duration. In addition, we validate these findings through case studies on the actual SPX market, covering bullish, sideways, and bearish market conditions.
Asset Pricing Model in Markets of Imperfect Information and Subjective Views
Authors: Hafid Lalioui, Amine Ben Amar, Makram Bellalah
Date: 2025-01-21 00:00:00

We provide closed-form market equilibrium formula consolidating informational imperfections and investors beliefs. Based on Merton's model, we characterize the equilibrium expected excess returns vector with incomplete information. We then derive the corresponding market portfolio as the solution to a non-linear system of equations and analyze the sensitivities of extra excess returns to shadow-costs and market weights. We derive the market reference model for excess returns under random shadow-costs. The conditional posterior distribution of excess returns integrates the pick-matrix and pick-vector of views and the vector of shadow-costs into a multivariate distribution with mean and covariance dependent on the reference model.
Loss of earning capacity in Denmark -- an actuarial perspective
Authors: C. Furrer, O. L. Sandqvist
Date: 2025-01-20 00:00:00

We describe challenges and opportunities related to risk assessment and mitigation for loss of earning capacity insurance with a special focus on Denmark. The presence of public benefits, claim settlement processes, and prevention initiatives introduces significant intricacy to the risk landscape. Accommodating this requires the development of innovative approaches from researchers and practitioners alike. Actuaries are uniquely positioned to lead the way, leveraging their domain knowledge and mathematical-statistical expertise to develop equitable, data-driven solutions that mitigate risk and enhance societal well-being.
Crossing penalised CAViaR
Authors: Tibor Szendrei
Date: 2025-01-17 00:00:00
Tasks: Time Series

Dynamic quantiles, or Conditional Autoregressive Value at Risk (CAViaR) models, have been extensively studied at the individual level. However, efforts to estimate multiple dynamic quantiles jointly have been limited. Existing approaches either sequentially estimate fitted quantiles or impose restrictive assumptions on the data generating process. This paper fills this gap by proposing an objective function for the joint estimation of all quantiles, introducing a crossing penalty to guide the process. Monte Carlo experiments and an empirical application on the FTSE100 validate the effectiveness of the method, offering a flexible and robust approach to modelling multiple dynamic quantiles in time-series data.
Do we actually understand the impact of renewables on electricity prices? A causal inference approach
Authors: Davide Cacciarelli, Pierre Pinson, Filip Panagiotopoulos, David Dixon, Lizzie Blaxland
Date: 2025-01-10 00:00:00
Tasks: Causal Inference

The energy transition is profoundly reshaping electricity market dynamics. It makes it essential to understand how renewable energy generation actually impacts electricity prices, among all other market drivers. These insights are critical to design policies and market interventions that ensure affordable, reliable, and sustainable energy systems. However, identifying causal effects from observational data is a major challenge, requiring innovative causal inference approaches that go beyond conventional regression analysis only. We build upon the state of the art by developing and applying a local partially linear double machine learning approach. Its application yields the first robust causal evidence on the distinct and non-linear effects of wind and solar power generation on UK wholesale electricity prices, revealing key insights that have eluded previous analyses. We find that, over 2018-2024, wind power generation has a U-shaped effect on prices: at low penetration levels, a 1 GWh increase in energy generation reduces prices by up to 7 GBP/MWh, but this effect gets close to none at mid-penetration levels (20-30%) before intensifying again. Solar power places substantial downward pressure on prices at very low penetration levels (up to 9 GBP/MWh per 1 GWh increase in energy generation), though its impact weakens quite rapidly. We also uncover a critical trend where the price-reducing effects of both wind and solar power have become more pronounced over time (from 2018 to 2024), highlighting their growing influence on electricity markets amid rising penetration. Our study provides both novel analysis approaches and actionable insights to guide policymakers in appraising the way renewables impact electricity markets.
Custom Loss Functions in Fuel Moisture Modeling
Authors: Jonathon Hirschi
Date: 2025-01-03 00:00:00

Fuel moisture content (FMC) is a key predictor for wildfire rate of spread (ROS). Machine learning models of FMC are being used more in recent years, augmenting or replacing traditional physics-based approaches. Wildfire rate of spread (ROS) has a highly nonlinear relationship with FMC, where small differences in dry fuels lead to large differences in ROS. In this study, custom loss functions that place more weight on dry fuels were examined with a variety of machine learning models of FMC. The models were evaluated with a spatiotemporal cross-validation procedure to examine whether the custom loss functions led to more accurate forecasts of ROS. Results show that the custom loss functions improved accuracy for ROS forecasts by a small amount. Further research would be needed to establish whether the improvement in ROS forecasts leads to more accurate real-time wildfire simulations.
An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks
Authors: Vivek Bharadwaj, Austin Glover, Aydin Buluc, James Demmel
Date: 2025-01-23 00:00:00
Tasks: GPU

Rotation equivariant graph neural networks, i.e. networks designed to guarantee certain geometric relations between their inputs and outputs, yield state of the art performance on spatial deep learning tasks. They exhibit high data efficiency during training and significantly reduced inference time for interatomic potential calculations compared to classical approaches. Key to these models is the Clebsch-Gordon (CG) tensor product, a kernel that contracts two dense feature vectors with a highly-structured sparse tensor to produce a dense output vector. The operation, which may be repeated millions of times for typical equivariant models, is a costly and inefficient bottleneck. We introduce a GPU sparse kernel generator for the CG tensor product that provides significant speedups over the best existing open and closed-source implementations. Our implementation achieves high performance by carefully managing the limited GPU shared memory through static analysis at model compile-time, minimizing reads and writes to global memory. We break the tensor product into a series of smaller kernels with operands that fit entirely into registers, enabling us to emit long arithmetic instruction streams that maximize instruction-level parallelism. By fusing the CG tensor product with a subsequent graph convolution, we reduce both intermediate storage and global memory traffic over naive approaches that duplicate input data. We also provide optimized kernels for the gradient of the CG tensor product and a novel identity for the higher partial derivatives required to predict interatomic forces. Our kernels offer up to 1.3x speedup over NVIDIA's closed-source cuEquivariance package, as well as 10x speedup over the widely-used e3nn package. In FP64 precision, we offer up to 6.2x inference-time speedup for the MACE chemistry foundation model over the original unoptimized version.
Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation
Authors: Shengzhe Zhang, Liyi Chen, Dazhong Shen, Chao Wang, Hui Xiong
Date: 2025-01-24 00:00:00
Tasks: Contrastive Learning, Mixture-of-Experts, Multi-Task Learning, Sequential Recommendation

Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more comprehensive item features and user preferences than traditional SR methods, which has become a critical topic in both academia and industry. Existing methods typically focus on enhancing multi-modal information utility through adaptive modality fusion to capture the evolving of user preference from user-item interaction sequences. However, most of them overlook the interference caused by redundant interest-irrelevant information contained in rich multi-modal data. Additionally, they primarily rely on implicit temporal information based solely on chronological ordering, neglecting explicit temporal signals that could more effectively represent dynamic user interest over time. To address these limitations, we propose a Hierarchical time-aware Mixture of experts for multi-modal Sequential Recommendation (HM4SR) with a two-level Mixture of Experts (MoE) and a multi-task learning strategy. Specifically, the first MoE, named Interactive MoE, extracts essential user interest-related information from the multi-modal data of each item. Then, the second MoE, termed Temporal MoE, captures user dynamic interests by introducing explicit temporal embeddings from timestamps in modality encoding. To further address data sparsity, we propose three auxiliary supervision tasks: sequence-level category prediction (CP) for item feature understanding, contrastive learning on ID (IDCL) to align sequence context with user interests, and placeholder contrastive learning (PCL) to integrate temporal information with modalities for dynamic interest modeling. Extensive experiments on four public datasets verify the effectiveness of HM4SR compared to several state-of-the-art approaches.
The Division of Surplus and the Burden of Proof
Authors: Deniz Kattwinkel, Justus Preusser
Date: 2025-01-24 00:00:00

A surplus must be divided between a principal and an agent. Only the agent knows the surplus' true size and decides how much of it to reveal initially. Both parties can exert costly effort to conclusively prove the surplus' true size. The agent's liability is bounded by the revealed surplus. The principal is equipped with additional funds. The principal designs a mechanism that allocates the burden of proof and divides the surplus. In principal-optimal mechanisms, the principal's effort to acquire proof decreases in the revealed surplus. The agent's effort initially decreases, but then the sign of its slope alternates across five intervals. Applications include wealth taxation, corporate finance, and public procurements.
A Comprehensive Framework for Semantic Similarity Analysis of Human and AI-Generated Text Using Transformer Architectures and Ensemble Techniques
Authors: Lifu Gao, Ziwei Liu, Qi Zhang
Date: 2025-01-24 00:00:00
Tasks: Data Augmentation, Domain Adaptation, Mamba, Semantic Similarity, Semantic Textual Similarity, Spelling Correction, text-classification, Text Classification, Text Detection

The rapid advancement of large language models (LLMs) has made detecting AI-generated text an increasingly critical challenge. Traditional methods often fail to capture the nuanced semantic differences between human and machine-generated content. We therefore propose a novel approach based on semantic similarity analysis, leveraging a multi-layered architecture that combines a pre-trained DeBERTa-v3-large model, Bi-directional LSTMs, and linear attention pooling to capture both local and global semantic patterns. To enhance performance, we employ advanced input and output augmentation techniques such as sector-level context integration and wide output configurations. These techniques enable the model to learn more discriminative features and generalize across diverse domains. Experimental results show that this approach works better than traditional methods, proving its usefulness for AI-generated text detection and other text comparison tasks.
Do LLMs Provide Consistent Answers to Health-Related Questions across Languages?
Authors: Ipek Baris Schlicht, Zhixue Zhao, Burcu Sayin, Lucie Flek, Paolo Rosso
Date: 2025-01-24 00:00:00
Tasks: Misinformation

Equitable access to reliable health information is vital for public health, but the quality of online health resources varies by language, raising concerns about inconsistencies in Large Language Models (LLMs) for healthcare. In this study, we examine the consistency of responses provided by LLMs to health-related questions across English, German, Turkish, and Chinese. We largely expand the HealthFC dataset by categorizing health-related questions by disease type and broadening its multilingual scope with Turkish and Chinese translations. We reveal significant inconsistencies in responses that could spread healthcare misinformation. Our main contributions are 1) a multilingual health-related inquiry dataset with meta-information on disease categories, and 2) a novel prompt-based evaluation workflow that enables sub-dimensional comparisons between two languages through parsing. Our findings highlight key challenges in deploying LLM-based tools in multilingual contexts and emphasize the need for improved cross-lingual alignment to ensure accurate and equitable healthcare information.