pwc-archive/papers-with-abstracts huggingface dataset:
Managing Basis Risks in Weather Parametric Insurance: A Quantitative Study of Diversification and Key Influencing Factors
Authors: Hang Gao, Shuohua Yang, Xinli Liu
Date: 2024-09-25 00:00:00
Tasks: Fairness

Weather parametric insurance relies on weather indices rather than actual loss assessments, enhancing claims efficiency, reducing moral hazard, and improving fairness. In the context of increasing climate change risks, despite growing interest and demand,, weather parametric insurance's market share remains limited due to inherent basis risk, which is the mismatch between actual loss and payout, leading to loss without payout or payout without loss. This paper proposes a novel empirical research using Monte Carlo simulations to test whether basis risk can be managed through diversification and hedged like other risks. Key findings include: Firstly, portfolio basis risk and volatility decrease as the number of contracts increases. Secondly, spatial relationships significantly impact basis risk, with risk levels correlating with the ratio between insured location, weather station, and disaster footprint radius, and thirdly, event severity does not significantly impact basis risk, suggesting that catastrophic disaster severity should not hinder parametric insurance development.
Improving Estimation of Portfolio Risk Using New Statistical Factors
Authors: Xialu Liu, John Guerard, Rong Chen, Ruey Tsay
Date: 2024-09-24 00:00:00
Tasks: Econometrics

Searching for new effective risk factors on stock returns is an important research topic in asset pricing. Factor modeling is an active research topic in statistics and econometrics, with many new advances. However, these new methods have not been fully utilized in asset pricing application. In this paper, we adopt the factor models, especially matrix factor models in various forms, to construct new statistical factors that explain the variation of stock returns. Furthermore, we evaluate the contribution of these statistical factors beyond the existing factors available in the asset pricing literature. To demonstrate the power of the new factors, U.S. monthly stock data are analyzed and the partial F test and double selection LASSO method are conducted. The results show that the new statistical factors bring additional information and add explanatory power in asset pricing. Our method opens a new direction for portfolio managers to seek additional risk factors to improve the estimation of portfolio returns.
Impermanent loss and loss-vs-rebalancing I: some statistical properties
Authors: Abe Alexander, Lars Fritz
Date: 2024-10-01 00:00:00
Tasks: Sentence

There are two predominant metrics to assess the performance of automated market makers and their profitability for liquidity providers: 'impermanent loss' (IL) and 'loss-versus-rebalance' (LVR). In this short paper we shed light on the statistical aspects of both concepts and show that they are more similar than conventionally appreciated. Our analysis uses the properties of a random walk and some analytical properties of the statistical integral combined with the mechanics of a constant function market maker (CFMM). We consider non-toxic or rather unspecific trading in this paper. Our main finding can be summarized in one sentence: For Brownian motion with a given volatility, IL and LVR have identical expectation values but vastly differing distribution functions.
Multi-Factor Polynomial Diffusion Models and Inter-Temporal Futures Dynamics
Authors: Peilun He, Nino Kordzakhia, Gareth W. Peters, Pavel V. Shevchenko
Date: 2024-09-28 00:00:00

In stochastic multi-factor commodity models, it is often the case that futures prices are explained by two latent state variables which represent the short and long term stochastic factors. In this work, we develop the family of stochastic models using polynomial diffusion to obtain the unobservable spot price to be used for modelling futures curve dynamics. The polynomial family of diffusion models allows one to incorporate a variety of non-linear, higher-order effects, into a multi-factor stochastic model, which is a generalisation of Schwartz and Smith (2000) two-factor model. Two filtering methods are used for the parameter and the latent factor estimation to address the non-linearity. We provide a comparative analysis of the performance of the estimation procedures. We discuss the parameter identification problem present in the polynomial diffusion case, regardless, the futures prices can still be estimated accurately. Moreover, we study the effects of different methods of calculating matrix exponential in the polynomial diffusion model. As the polynomial order increases, accurately and efficiently approximating the high-dimensional matrix exponential becomes essential in the polynomial diffusion model.
PDSim: A Shiny App for Polynomial Diffusion Model Simulation and Estimation
Authors: Peilun He, Nino Kordzakhia, Gareth W. Peters, Pavel V. Shevchenko
Date: 2024-09-28 00:00:00

PDSim is an R package that enables users to simulate commodity futures prices using the polynomial diffusion model introduced in Filipovic and Larsson (2016) through both a Shiny web application and R scripts. It also provides state variables and contract estimations via the Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF). With its user-friendly interface, PDSim makes the features of simulations and estimations accessible to all users. To date, it is the only package specifically designed for the simulation and estimation of the polynomial diffusion model. Additionally, the package integrates the Schwartz and Smith two-factor model (Schwartz & Smith, 2000) as an alternative approach. PDSim offers versatile deployment options, including running locally, via the Shiny server, or through Docker.
SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest
Authors: Saber Talazadeh, Dragan Perakovic
Date: 2024-09-22 00:00:00
Tasks: Sentiment Analysis, Stock Market Prediction

Stock trend forecasting, a challenging problem in the financial domain, involves ex-tensive data and related indicators. Relying solely on empirical analysis often yields unsustainable and ineffective results. Machine learning researchers have demonstrated that the application of random forest algorithm can enhance predictions in this context, playing a crucial auxiliary role in forecasting stock trends. This study introduces a new approach to stock market prediction by integrating sentiment analysis using FinGPT generative AI model with the traditional Random Forest model. The proposed technique aims to optimize the accuracy of stock price forecasts by leveraging the nuanced understanding of financial sentiments provided by FinGPT. We present a new methodology called "Sentiment-Augmented Random Forest" (SARF), which in-corporates sentiment features into the Random Forest framework. Our experiments demonstrate that SARF outperforms conventional Random Forest and LSTM models with an average accuracy improvement of 9.23% and lower prediction errors in pre-dicting stock market movements.
A Comparison between Financial and Gambling Markets
Authors: Haoyu Liu, Carl Donovan, Valentin Popov
Date: 2024-09-20 00:00:00

Financial and gambling markets are ostensibly similar and hence strategies from one could potentially be applied to the other. Financial markets have been extensively studied, resulting in numerous theorems and models, while gambling markets have received comparatively less attention and remain relatively undocumented. This study conducts a comprehensive comparison of both markets, focusing on trading rather than regulation. Five key aspects are examined: platform, product, procedure, participant and strategy. The findings reveal numerous similarities between these two markets. Financial exchanges resemble online betting platforms, such as Betfair, and some financial products, including stocks and options, share speculative traits with sports betting. We examine whether well-established models and strategies from financial markets could be applied to the gambling industry, which lacks comparable frameworks. For example, statistical arbitrage from financial markets has been effectively applied to gambling markets, particularly in peer-to-peer betting exchanges, where bettors exploit odds discrepancies for risk-free profits using quantitative models. Therefore, exploring the strategies and approaches used in both markets could lead to new opportunities for innovation and optimization in trading and betting activities.
Joint multifractality in the cross-correlations between grains \& oilseeds indices and external uncertainties
Authors: Ying-Hui Shao, Xing-Lu Gao, Yan-Hong Yang, Wei-Xing Zhou
Date: 2024-09-18 00:00:00
Tasks: GPR

This study investigates the relationships between agricultural spot markets and external uncertainties via the multifractal detrending moving-average cross-correlation analysis (MF-X-DMA). The dataset contains the Grains \& Oilseeds Index (GOI) and its five sub-indices of wheat, maize, soyabeans, rice, and barley. Moreover, we use three uncertainty proxies, namely, economic policy uncertainty (EPU), geopolitical risk (GPR), and volatility Index (VIX). We observe the presence of multifractal cross-correlations between agricultural markets and uncertainties. Further, statistical tests show that maize has intrinsic joint multifractality with all the uncertainty proxies, exhibiting a high degree of sensitivity. Additionally, intrinsic multifractality among GOI-GPR, wheat-GPR and soyabeans-VIX is illustrated. However, other series have apparent multifractal cross-correlations with high possibilities. Moreover, our analysis suggests that among the three kinds of external uncertainties, geopolitical risk has a relatively stronger association with grain prices.
Value of Information in the Mean-Square Case and its Application to the Analysis of Financial Time-Series Forecast
Authors: Roman Belavkin, Panos Pardalos, Jose Principe
Date: 2024-09-17 00:00:00
Tasks: Time Series

The advances and development of various machine learning techniques has lead to practical solutions in various areas of science, engineering, medicine and finance. The great choice of algorithms, their implementations and libraries has resulted in another challenge of selecting the right algorithm and tuning their parameters in order to achieve optimal or satisfactory performance in specific applications. Here we show how the value of information (V(I)) can be used in this task to guide the algorithm choice and parameter tuning process. After estimating the amount of Shannon's mutual information between the predictor and response variables, V(I) can define theoretical upper bound of performance of any algorithm. The inverse function I(V) defines the lower frontier of the minimum amount of information required to achieve the desired performance. In this paper, we illustrate the value of information for the mean-square error minimization and apply it to forecasts of cryptocurrency log-returns.
Cross-Lingual News Event Correlation for Stock Market Trend Prediction
Authors: Sahar Arshad, Nikhar Azhar, Sana Sajid, Seemab Latif, Rabia Latif
Date: 2024-09-16 00:00:00
Tasks: Articles, Financial Analysis, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Semantic Textual Similarity, Sentiment Analysis

In the modern economic landscape, integrating financial services with Financial Technology (FinTech) has become essential, particularly in stock trend analysis. This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset and proposing a cross-lingual Natural Language-based Financial Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing sentiment analysis, Named Entity Recognition (NER), and semantic textual similarity, we conducted an analytical examination of news articles to extract, map, and visualize financial event timelines, uncovering the correlation between news events and stock market trends. Our method demonstrated a meaningful correlation between stock price movements and cross-linguistic news sentiments, validated by processing two-year cross-lingual news data on two prominent sectors of the Pakistan Stock Exchange. This study offers significant insights into key events, ensuring a substantial decision margin for investors through effective visualization and providing optimal investment opportunities.
The Impact of Designated Market Makers on Market Liquidity and Competition: A Simulation Approach
Authors: Cong Zhou
Date: 2024-09-25 00:00:00
Tasks: Diversity

This paper conducts an empirical investigation into the effects of Designated Market Makers (DMMs) on key market quality indicators, such as liquidity, bid-ask spreads, and order fulfillment ratios. Through agent-based simulations, this study explores the impact of varying competition levels and incentive structures among DMMs on market dynamics. It aims to demonstrate that DMMs are crucial for enhancing market liquidity and stabilizing price spreads, thereby affirming their essential role in promoting market efficiency. Our findings confirm the impact of the number of Designated Market Makers (DMMs) and asset diversity on market liquidity. The result also suggests that an optimal level of competition among DMMs can maximize liquidity benefits while minimizing negative impacts on price discovery. Additionally, the research indicates that the benefits of increased number of DMMs diminish beyond a certain threshold, implying that excessive incentives may not further improve market quality metrics.
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
Authors: Gang Xu, Xin Zhou, Molin Wang, Boya Zhang, Wenhao Jiang, Francine Laden, Helen H. Suh, Adam A. Szpiro, Donna Spiegelman, Zuoheng Wang
Date: 2024-09-22 00:00:00
Tasks: Causal Inference, regression

One way to quantify exposure to air pollution and its constituents in epidemiologic studies is to use an individual's nearest monitor. This strategy results in potential inaccuracy in the actual personal exposure, introducing bias in estimating the health effects of air pollution and its constituents, especially when evaluating the causal effects of correlated multi-pollutant constituents measured with correlated error. This paper addresses estimation and inference for the causal effect of one constituent in the presence of other PM2.5 constituents, accounting for measurement error and correlations. We used a linear regression calibration model, fitted with generalized estimating equations in an external validation study, and extended a double/debiased machine learning (DML) approach to correct for measurement error and estimate the effect of interest in the main study. We demonstrated that the DML estimator with regression calibration is consistent and derived its asymptotic variance. Simulations showed that the proposed estimator reduced bias and attained nominal coverage probability across most simulation settings. We applied this method to assess the causal effects of PM2.5 constituents on cognitive function in the Nurses' Health Study and identified two PM2.5 constituents, Br and Mn, that showed a negative causal effect on cognitive function after measurement error correction.
Adaptive Mixture Importance Sampling for Automated Ads Auction Tuning
Authors: Yimeng Jia, Kaushal Paneri, Rong Huang, Kailash Singh Maurya, Pavan Mallapragada, Yifan Shi
Date: 2024-09-20 00:00:00
Tasks: Decision Making, Diversity, Recommendation Systems

This paper introduces Adaptive Mixture Importance Sampling (AMIS) as a novel approach for optimizing key performance indicators (KPIs) in large-scale recommender systems, such as online ad auctions. Traditional importance sampling (IS) methods face challenges in dynamic environments, particularly in navigating through complexities of multi-modal landscapes and avoiding entrapment in local optima for the optimization task. Instead of updating importance weights and mixing samples across iterations, as in canonical adaptive IS and multiple IS, our AMIS framework leverages a mixture distribution as the proposal distribution and dynamically adjusts both the mixture parameters and their mixing rates at each iteration, thereby enhancing search diversity and efficiency. Through extensive offline simulations, we demonstrate that AMIS significantly outperforms simple Gaussian Importance Sampling (GIS), particularly in noisy environments. Moreover, our approach is validated in real-world scenarios through online A/B experiments on a major search engine, where AMIS consistently identifies optimal tuning points that are more likely to be adopted as mainstream configurations. These findings indicate that AMIS enhances convergence in noisy environments, leading to more accurate and reliable decision-making in the context of importance sampling off-policy estimators.
Classification of Buried Objects from Ground Penetrating Radar Images by using Second Order Deep Learning Models
Authors: Douba Jafuno, Ammar Mian, Guillaume Ginolhac, Nickolas Stelzenmuller
Date: 2024-09-20 00:00:00
Tasks: GPR

In this paper, a new classification model based on covariance matrices is built in order to classify buried objects. The inputs of the proposed models are the hyperbola thumbnails obtained with a classical Ground Penetrating Radar (GPR) system. These thumbnails are then inputs to the first layers of a classical CNN, which then produces a covariance matrix using the outputs of the convolutional filters. Next, the covariance matrix is given to a network composed of specific layers to classify Symmetric Positive Definite (SPD) matrices. We show in a large database that our approach outperform shallow networks designed for GPR data and conventional CNNs typically used in computer vision applications, particularly when the number of training data decreases and in the presence of mislabeled data. We also illustrate the interest of our models when training data and test sets are obtained from different weather modes or considerations.
DeepScore: A Comprehensive Approach to Measuring Quality in AI-Generated Clinical Documentation
Authors: Jon Oleson
Date: 2024-09-10 00:00:00

Medical practitioners are rapidly adopting generative AI solutions for clinical documentation, leading to significant time savings and reduced stress. However, evaluating the quality of AI-generated documentation is a complex and ongoing challenge. This paper presents an overview of DeepScribe's methodologies for assessing and managing note quality, focusing on various metrics and the composite "DeepScore", an overall index of quality and accuracy. These methodologies aim to enhance the quality of patient care documentation through accountability and continuous improvement.
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
Authors: Alec F. Diallo, Vaishak Belle, Paul Patras
Date: 2024-10-07 00:00:00
Tasks: Causal Inference

Despite their success and widespread adoption, the opaque nature of deep neural networks (DNNs) continues to hinder trust, especially in critical applications. Current interpretability solutions often yield inconsistent or oversimplified explanations, or require model changes that compromise performance. In this work, we introduce TRACER, a novel method grounded in causal inference theory designed to estimate the causal dynamics underpinning DNN decisions without altering their architecture or compromising their performance. Our approach systematically intervenes on input features to observe how specific changes propagate through the network, affecting internal activations and final outputs. Based on this analysis, we determine the importance of individual features, and construct a high-level causal map by grouping functionally similar layers into cohesive causal nodes, providing a structured and interpretable view of how different parts of the network influence the decisions. TRACER further enhances explainability by generating counterfactuals that reveal possible model biases and offer contrastive explanations for misclassifications. Through comprehensive evaluations across diverse datasets, we demonstrate TRACER's effectiveness over existing methods and show its potential for creating highly compressed yet accurate models, illustrating its dual versatility in both understanding and optimizing DNNs.
Refining Counterfactual Explanations With Joint-Distribution-Informed Shapley Towards Actionable Minimality
Authors: Lei You, Yijun Bian, Lele Cao
Date: 2024-10-07 00:00:00
Tasks: counterfactual

Counterfactual explanations (CE) identify data points that closely resemble the observed data but produce different machine learning (ML) model outputs, offering critical insights into model decisions. Despite the diverse scenarios, goals and tasks to which they are tailored, existing CE methods often lack actionable efficiency because of unnecessary feature changes included within the explanations that are presented to users and stakeholders. We address this problem by proposing a method that minimizes the required feature changes while maintaining the validity of CE, without imposing restrictions on models or CE algorithms, whether instance- or group-based. The key innovation lies in computing a joint distribution between observed and counterfactual data and leveraging it to inform Shapley values for feature attributions (FA). We demonstrate that optimal transport (OT) effectively derives this distribution, especially when the alignment between observed and counterfactual data is unclear in used CE methods. Additionally, a counterintuitive finding is uncovered: it may be misleading to rely on an exact alignment defined by the CE generation mechanism in conducting FA. Our proposed method is validated on extensive experiments across multiple datasets, showcasing its effectiveness in refining CE towards greater actionable efficiency.
Output Scouting: Auditing Large Language Models for Catastrophic Responses
Authors: Andrew Bell, Joao Fonseca
Date: 2024-10-04 00:00:00

Recent high profile incidents in which the use of Large Language Models (LLMs) resulted in significant harm to individuals have brought about a growing interest in AI safety. One reason LLM safety issues occur is that models often have at least some non-zero probability of producing harmful outputs. In this work, we explore the following scenario: imagine an AI safety auditor is searching for catastrophic responses from an LLM (e.g. a "yes" responses to "can I fire an employee for being pregnant?"), and is able to query the model a limited number times (e.g. 1000 times). What is a strategy for querying the model that would efficiently find those failure responses? To this end, we propose output scouting: an approach that aims to generate semantically fluent outputs to a given prompt matching any target probability distribution. We then run experiments using two LLMs and find numerous examples of catastrophic responses. We conclude with a discussion that includes advice for practitioners who are looking to implement LLM auditing for catastrophic responses. We also release an open-source toolkit (https://github.com/joaopfonseca/outputscouting) that implements our auditing framework using the Hugging Face transformers library.