pwc-archive/papers-with-abstracts huggingface dataset:
Kullback-Leibler cluster entropy to quantify volatility correlation and risk diversity
Authors: L. Ponta, A. Carbone
Date: 2024-09-01 00:00:00
Tasks: Diversity

The Kullback-Leibler cluster entropy $\mathcal{D_{C}}[P \| Q] $ is evaluated for the empirical and model probability distributions $P$ and $Q$ of the clusters formed in the realized volatility time series of five assets (SP\&500, NASDAQ, DJIA, DAX, FTSEMIB). The Kullback-Leibler functional $\mathcal{D_{C}}[P \| Q] $ provides complementary perspectives about the stochastic volatility process compared to the Shannon functional $\mathcal{S_{C}}[P]$. While $\mathcal{D_{C}}[P \| Q] $ is maximum at the short time scales, $\mathcal{S_{C}}[P]$ is maximum at the large time scales leading to complementary optimization criteria tracing back respectively to the maximum and minimum relative entropy evolution principles. The realized volatility is modelled as a time-dependent fractional stochastic process characterized by power-law decaying distributions with positive correlation ($H>1/2$). As a case study, a multiperiod portfolio built on diversity indexes derived from the Kullback-Leibler entropy measure of the realized volatility. The portfolio is robust and exhibits better performances over the horizon periods. A comparison with the portfolio built either according to the uniform distribution or in the framework of the Markowitz theory is also reported.
Simple robust two-stage estimation and inference for generalized impulse responses and multi-horizon causality
Authors: Jean-Marie Dufour, EnDong Wang
Date: 2024-09-17 00:00:00

This paper introduces a novel two-stage estimation and inference procedure for generalized impulse responses (GIRs). GIRs encompass all coefficients in a multi-horizon linear projection model of future outcomes of y on lagged values (Dufour and Renault, 1998), which include the Sims' impulse response. The conventional use of Least Squares (LS) with heteroskedasticity- and autocorrelation-consistent covariance estimation is less precise and often results in unreliable finite sample tests, further complicated by the selection of bandwidth and kernel functions. Our two-stage method surpasses the LS approach in terms of estimation efficiency and inference robustness. The robustness stems from our proposed covariance matrix estimates, which eliminate the need to correct for serial correlation in the multi-horizon projection residuals. Our method accommodates non-stationary data and allows the projection horizon to grow with sample size. Monte Carlo simulations demonstrate our two-stage method outperforms the LS method. We apply the two-stage method to investigate the GIRs, implement multi-horizon Granger causality test, and find that economic uncertainty exerts both short-run (1-3 months) and long-run (30 months) effects on economic activities.
GPT takes the SAT: Tracing changes in Test Difficulty and Math Performance of Students
Authors: Vikram Krishnaveti, Saannidhya Rawat
Date: 2024-09-16 00:00:00
Tasks: Math

Scholastic Aptitude Test (SAT) is crucial for college admissions but its effectiveness and relevance are increasingly questioned. This paper enhances Synthetic Control methods by introducing "Transformed Control", a novel method that employs Large Language Models (LLMs) powered by Artificial Intelligence to generate control groups. We utilize OpenAI's API to generate a control group where GPT-4, or ChatGPT, takes multiple SATs annually from 2008 to 2023. This control group helps analyze shifts in SAT math difficulty over time, starting from the baseline year of 2008. Using parallel trends, we calculate the Average Difference in Scores (ADS) to assess changes in high school students' math performance. Our results indicate a significant decrease in the difficulty of the SAT math section over time, alongside a decline in students' math performance. The analysis shows a 71-point drop in the rigor of SAT math from 2008 to 2023, with student performance decreasing by 36 points, resulting in a 107-point total divergence in average student math performance. We investigate possible mechanisms for this decline in math proficiency, such as changing university selection criteria, increased screen time, grade inflation, and worsening adolescent mental health. Disparities among demographic groups show a 104-point drop for White students, 84 points for Black students, and 53 points for Asian students. Male students saw a 117-point reduction, while female students had a 100-point decrease.
Experimental Evidence That Conversational Artificial Intelligence Can Steer Consumer Behavior Without Detection
Authors: Tobias Werner, Ivan Soraperra, Emilio Calvano, David C. Parkes, Iyad Rahwan
Date: 2024-09-18 00:00:00
Tasks: Information Retrieval, Retrieval

Conversational AI models are becoming increasingly popular and are about to replace traditional search engines for information retrieval and product discovery. This raises concerns about monetization strategies and the potential for subtle consumer manipulation. Companies may have financial incentives to steer users toward search results or products in a conversation in ways that are unnoticeable to consumers. Using a behavioral experiment, we show that conversational AI models can indeed significantly shift consumer preferences. We discuss implications and ask whether regulators are sufficiently prepared to combat potential consumer deception.
It depends: Varieties of defining growth dependence
Authors: Anja Janischewski, Katharina Bohnenberger, Matthias Kranke, Tobias Vogel, Riwan Driouich, Tobias Froese, Stefanie Gerold, Raphael Kaufmann, Lorenz Keyßer, Jannis Niethammer, Christopher Olk, Matthias Schmelzer, Aslı Yürük, Steffen Lange
Date: 2024-09-18 00:00:00

Many socio-economic systems require positive economic growth rates to function properly. Given uncertainty about future growth rates and increasing evidence that economic growth is a driver of social and environmental crises, these growth dependencies pose serious societal challenges. In recent years, more and more researchers have thus tried to identify growth-dependent systems and develop policies to reduce their growth dependence. However, the concept of 'growth dependence' still lacks a consistent definition and operationalization, which impedes more systematic empirical and theoretical research. This article proposes a simple but powerful framework for defining and operationalizing the concept of 'growth dependence' across socio-economic systems. We provide a general definition consisting of four components that can be specified for different empirical cases: (1) the system under investigation, (2) the unit of measurement of growth, (3) the level of growth and (4) the relevant functions or properties of the system under investigation. According to our general definition, a socio-economic system is growth-dependent if it requires a long-term positive growth rate in terms of a unit of economic measurement to maintain all its functions or properties that are relevant within the chosen normative framework. To illustrate the usefulness of our scheme, we apply it to three areas at the heart of the existing literature on growth dependence: employment, social insurance systems and public finance. These case studies demonstrate that whether or not a system is growth-dependent hinges not only on the empirical properties of the system itself but also on the specification of the concept of growth dependence. Our framework enables coherent, robust and effective definitions and research questions, fostering comparability of findings across different cases and disciplines.
Cognitive Hierarchy in Day-to-day Network Flow Dynamics
Authors: Minyu Shen, Feng Xiao, Weihua Gu, Hongbo Ye
Date: 2024-09-18 00:00:00

When making route decisions, travelers may engage in a certain degree of reasoning about what the others will do in the upcoming day, rendering yesterday's shortest routes less attractive. This phenomenon was manifested in a recent virtual experiment that mimicked travelers' repeated daily trip-making process. Unfortunately, prevailing day-to-day traffic dynamical models failed to faithfully reproduce the collected flow evolution data therein. To this end, we propose a day-to-day traffic behavior modeling framework based on the Cognitive Hierarchy theory, in which travelers with different levels of strategic-reasoning capabilities form their own beliefs about lower-step travelers' capabilities when choosing their routes. Two widely-studied day-to-day models, the Network Tatonnement Process dynamic and the Logit dynamic, are extended into the framework and studied as examples. Calibration of the virtual experiment is performed using the extended Network Tatonnement Process dynamic, which fits the experimental data reasonably well. We show that the two extended dynamics have multiple equilibria, one of which is the classical user equilibrium. While analyzing global stability is intractable due to the presence of multiple equilibria, local stabilities near equilibria are developed analytically and verified by numerical experiments. General insights on how key parameters affect the stability of user equilibria are unveiled.
The long-term human capital and health impacts of a pollution reduction programme
Authors: Nanna Fukushima, Stephanie von Hinke, Emil N. Sørensen
Date: 2024-09-18 00:00:00

This paper investigates the effects of the staggered roll-out of a pollution reduction programme introduced in the UK in the 1950s. The policy allowed local authorities to introduce so-called Smoke Control Areas (SCAs) which banned smoke emissions. We start by digitizing historical pollution data to show that the policy led to an immediate reduction in black smoke concentrations. We then merge data on the exact location, boundary and month of introduction of SCAs to individual-level outcomes in older age using individuals' year-month and location of birth. We show that exposure to the programme increased individuals' birth weights as well as height in adulthood. We find no impact on their years of education or fluid intelligence.
What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts
Authors: Shuaiyu Chen, T. Clifton Green, Huseyin Gulen, Dexin Zhou
Date: 2024-09-17 00:00:00

We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance similar to humans. LLM forecasts appear optimistic relative to historical and future realized returns. When prompted for 80% confidence interval predictions, LLM responses are better calibrated than survey evidence but are pessimistic about outliers, leading to skewed forecast distributions. The findings suggest LLMs manifest common behavioral biases when forecasting expected returns but are better at gauging risks than humans.
How (Not) to Incentivize Sustainable Mobility? Lessons from a Swiss Mobility Competition
Authors: Silvio Sticher, Hannes Wallimann, Noah Balthasar
Date: 2024-09-17 00:00:00

We investigate the impact of a gamified experiment designed to promote sustainable mobility among students and staff members of a Swiss higher-education institution. Despite transportation being a major contributor to domestic CO2 emissions, achieving behavioral change remains challenging. In our two-month mobility competition, structured as a randomized controlled trial with a 3x3 factorial design, neither monetary incentives nor norm-based nudging significantly influences mobility behavior. Our (null) results suggest that there is no "gamified quick fix" for making mobility substantially more sustainable. Also, we provide some lessons learned on how not to incentivize sustainable mobility by addressing potential shortcomings of our mobility competition.
Approximately Optimal Auctions With a Strong Bidder
Authors: Luca Anderlini, GaOn Kim
Date: 2024-09-17 00:00:00

We consider auctions with N+1 bidders. Of these, N are symmetric and N+1 is "sufficiently strong" relative to the others. The auction is a "tournament" in which the first N players bid to win the right to compete with N+1. The bids of the first N players are binding and the highest bidder proceeds to a second-price competition with N+1. When N+1's values converge in distribution to an atom above the upper end of the distribution of the N bidders and the rest of the distribution is drained away from low values sufficiently slowly, the auction's expected revenue is arbitrarily close to the one obtained in a Myerson (1981) optimal auction. The tournament design is "detail free" in the sense that no specific knowledge of the distributions is needed in addition to the fact that bidder N+1 is stronger than the others as required. In particular, no additional information about the value of the atom is needed. This is important since mis-calibrating by a small amount an attempt to implement the optimal auction can lead to large losses in revenue. We provide an interpretation of these results as possibly providing guidelines to a seller on how to strategically "populate" auctions with a single bidder even when only weaker bidders are available.
Expert Classification Aggregation
Authors: Federico Fioravanti
Date: 2024-09-17 00:00:00
Tasks: Classification

We consider the problem where a set of individuals has to classify $m$ objects into $p$ categories by aggregating the individual classifications, and no category can be left empty. An aggregator satisfies \emph{Expertise} if individuals are decisive either over the classification of a given object, or the classification into a given category. We show that requiring an aggregator to satisfy \emph{Expertise} (or variants of it) and be either unanimous or independent leads to numerous impossibility results.
Beyond Rationality: Unveiling the Role of Animal Spirits and Inflation Extrapolation in Central Bank Communication of the US
Authors: Arpan Chakraborty
Date: 2024-09-17 00:00:00
Tasks: Decision Making

Modern macroeconomic models, particularly those grounded in Rational Expectation Dynamic Stochastic General Equilibrium (DSGE), operate under the assumption of fully rational decision-making. This paper examines the impact of behavioral factors on the communication index/sentiment index of the US Federal Reserve. [Upon receiving the review comments, I found some technical errors in the paper. I shall update it accordingly. Please do not cite this paper without author's permission.]
The Extreme Points of Fusions
Authors: Andreas Kleiner, Benny Moldovanu, Philipp Strack, Mark Whitmeyer
Date: 2024-09-16 00:00:00

Our work explores fusions, the multidimensional counterparts of mean-preserving contractions and their extreme and exposed points. We reveal an elegant geometric/combinatorial structure for these objects. Of particular note is the connection between Lipschitz-exposed points (measures that are unique optimizers of Lipschitz-continuous objectives) and power diagrams, which are divisions of a space into convex polyhedral ``cells'' according to a weighted proximity criterion. These objects are frequently seen in nature--in cell structures in biological systems, crystal and plant growth patterns, and territorial division in animal habitats--and, as we show, provide the essential structure of Lipschitz-exposed fusions. We apply our results to several questions concerning categorization.
Exploring an Inter-Pausal Unit (IPU) based Approach for Indic End-to-End TTS Systems
Authors: Anusha Prakash, Hema A Murthy
Date: 2024-09-18 00:00:00
Tasks: Sentence, text-to-speech, Text to Speech

Sentences in Indian languages are generally longer than those in English. Indian languages are also considered to be phrase-based, wherein semantically complete phrases are concatenated to make up sentences. Long utterances lead to poor training of text-to-speech models and result in poor prosody during synthesis. In this work, we explore an inter-pausal unit (IPU) based approach in the end-to-end (E2E) framework, focusing on synthesising conversational-style text. We consider both autoregressive Tacotron2 and non-autoregressive FastSpeech2 architectures in our study and perform experiments with three Indian languages, namely, Hindi, Tamil and Telugu. With the IPU-based Tacotron2 approach, we see a reduction in insertion and deletion errors in the synthesised audio, providing an alternative approach to the FastSpeech(2) network in terms of error reduction. The IPU-based approach requires less computational resources and produces prosodically richer synthesis compared to conventional sentence-based systems.
World of Forms: Deformable Geometric Templates for One-Shot Surface Meshing in Coronary CT Angiography
Authors: Rudolf L. M. van Herten, Ioannis Lagogiannis, Jelmer M. Wolterink, Steffen Bruns, Eva R. Meulendijks, Damini Dey, Joris R. de Groot, José P. Henriques, R. Nils Planken, Simone Saitta, Ivana Išgum
Date: 2024-09-18 00:00:00
Tasks: Graph Neural Network, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge. This may lead to topological inconsistencies and suboptimal performance in low-data regimes. To address these challenges, we propose a data-efficient deep learning method for direct 3D anatomical object surface meshing using geometric priors. Our approach employs a multi-resolution graph neural network that operates on a prior geometric template which is deformed to fit object boundaries of interest. We show how different templates may be used for the different surface meshing targets, and introduce a novel masked autoencoder pretraining strategy for 3D spherical data. The proposed method outperforms nnUNet in a one-shot setting for segmentation of the pericardium, left ventricle (LV) cavity and the LV myocardium. Similarly, the method outperforms other lumen segmentation operating on multi-planar reformatted images. Results further indicate that mesh quality is on par with or improves upon marching cubes post-processing of voxel mask predictions, while remaining flexible in the choice of mesh triangulation prior, thus paving the way for more accurate and topologically consistent 3D medical object surface meshing.
Noise-aware Dynamic Image Denoising and Positron Range Correction for Rubidium-82 Cardiac PET Imaging via Self-supervision
Authors: Huidong Xie, Liang Guo, Alexandre Velo, Zhao Liu, Qiong Liu, Xueqi Guo, Bo Zhou, Xiongchao Chen, Yu-Jung Tsai, Tianshun Miao, Menghua Xia, Yi-Hwa Liu, Ian S. Armstrong, Ge Wang, Richard E. Carson, Albert J. Sinusas, Chi Liu
Date: 2024-09-17 00:00:00
Tasks: Denoising, Image Denoising

Rb-82 is a radioactive isotope widely used for cardiac PET imaging. Despite numerous benefits of 82-Rb, there are several factors that limits its image quality and quantitative accuracy. First, the short half-life of 82-Rb results in noisy dynamic frames. Low signal-to-noise ratio would result in inaccurate and biased image quantification. Noisy dynamic frames also lead to highly noisy parametric images. The noise levels also vary substantially in different dynamic frames due to radiotracer decay and short half-life. Existing denoising methods are not applicable for this task due to the lack of paired training inputs/labels and inability to generalize across varying noise levels. Second, 82-Rb emits high-energy positrons. Compared with other tracers such as 18-F, 82-Rb travels a longer distance before annihilation, which negatively affect image spatial resolution. Here, the goal of this study is to propose a self-supervised method for simultaneous (1) noise-aware dynamic image denoising and (2) positron range correction for 82-Rb cardiac PET imaging. Tested on a series of PET scans from a cohort of normal volunteers, the proposed method produced images with superior visual quality. To demonstrate the improvement in image quantification, we compared image-derived input functions (IDIFs) with arterial input functions (AIFs) from continuous arterial blood samples. The IDIF derived from the proposed method led to lower AUC differences, decreasing from 11.09% to 7.58% on average, compared to the original dynamic frames. The proposed method also improved the quantification of myocardium blood flow (MBF), as validated against 15-O-water scans, with mean MBF differences decreased from 0.43 to 0.09, compared to the original dynamic frames. We also conducted a generalizability experiment on 37 patient scans obtained from a different country using a different scanner.
Using Physics Informed Generative Adversarial Networks to Model 3D porous media
Authors: Zihan Ren, Sanjay Srinivasan
Date: 2024-09-17 00:00:00
Tasks: Image Generation

Micro-CT scanning of rocks significantly enhances our understanding of pore-scale physics in porous media. With advancements in pore-scale simulation methods, such as pore network models, it is now possible to accurately simulate multiphase flow properties, including relative permeability, from CT-scanned rock samples. However, the limited number of CT-scanned samples and the challenge of connecting pore-scale networks to field-scale rock properties often make it difficult to use pore-scale simulated properties in realistic field-scale reservoir simulations. Deep learning approaches to create synthetic 3D rock structures allow us to simulate variations in CT rock structures, which can then be used to compute representative rock properties and flow functions. However, most current deep learning methods for 3D rock structure synthesis don't consider rock properties derived from well observations, lacking a direct link between pore-scale structures and field-scale data. We present a method to construct 3D rock structures constrained to observed rock properties using generative adversarial networks (GANs) with conditioning accomplished through a gradual Gaussian deformation process. We begin by pre-training a Wasserstein GAN to reconstruct 3D rock structures. Subsequently, we use a pore network model simulator to compute rock properties. The latent vectors for image generation in GAN are progressively altered using the Gaussian deformation approach to produce 3D rock structures constrained by well-derived conditioning data. This GAN and Gaussian deformation approach enables high-resolution synthetic image generation and reproduces user-defined rock properties such as porosity, permeability, and pore size distribution. Our research provides a novel way to link GAN-generated models to field-derived quantities.
Blind Deconvolution on Graphs: Exact and Stable Recovery
Authors: Chang Ye, Gonzalo Mateos
Date: 2024-09-18 00:00:00

We study a blind deconvolution problem on graphs, which arises in the context of localizing a few sources that diffuse over networks. While the observations are bilinear functions of the unknown graph filter coefficients and sparse input signals, a mild requirement on invertibility of the diffusion filter enables an efficient convex relaxation leading to a linear programming formulation that can be tackled with off-the-shelf solvers. Under the Bernoulli-Gaussian model for the inputs, we derive sufficient exact recovery conditions in the noise-free setting. A stable recovery result is then established, ensuring the estimation error remains manageable even when the observations are corrupted by a small amount of noise. Numerical tests with synthetic and real-world network data illustrate the merits of the proposed algorithm, its robustness to noise as well as the benefits of leveraging multiple signals to aid the (blind) localization of sources of diffusion. At a fundamental level, the results presented here broaden the scope of classical blind deconvolution of (spatio-)temporal signals to irregular graph domains.