pwc-archive/papers-with-abstracts huggingface dataset:
Characterizing asymmetric and bimodal long-term financial return distributions through quantum walks
Authors: Stijn De Backer, Luis E. C. Rocha, Jan Ryckebusch, Koen Schoors
Date: 2025-05-19 00:00:00

The analysis of logarithmic return distributions defined over large time scales is crucial for understanding the long-term dynamics of asset price movements. For large time scales of the order of two trading years, the anticipated Gaussian behavior of the returns often does not emerge, and their distributions often exhibit a high level of asymmetry and bimodality. These features are inadequately captured by the majority of classical models to address financial time series and return distributions. In the presented analysis, we use a model based on the discrete-time quantum walk to characterize the observed asymmetry and bimodality. The quantum walk distinguishes itself from a classical diffusion process by the occurrence of interference effects, which allows for the generation of bimodal and asymmetric probability distributions. By capturing the broader trends and patterns that emerge over extended periods, this analysis complements traditional short-term models and offers opportunities to more accurately describe the probabilistic structure underlying long-term financial decisions.
Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
Authors: Kalyan Cherukuri, Aarav Lala, Yash Yardi
Date: 2025-05-17 00:00:00
Tasks: Reinforcement Learning (RL)

We propose Q-Policy, a hybrid quantum-classical reinforcement learning (RL) framework that mathematically accelerates policy evaluation and optimization by exploiting quantum computing primitives. Q-Policy encodes value functions in quantum superposition, enabling simultaneous evaluation of multiple state-action pairs via amplitude encoding and quantum parallelism. We introduce a quantum-enhanced policy iteration algorithm with provable polynomial reductions in sample complexity for the evaluation step, under standard assumptions. To demonstrate the technical feasibility and theoretical soundness of our approach, we validate Q-Policy on classical emulations of small discrete control tasks. Due to current hardware and simulation limitations, our experiments focus on showcasing proof-of-concept behavior rather than large-scale empirical evaluation. Our results support the potential of Q-Policy as a theoretical foundation for scalable RL on future quantum devices, addressing RL scalability challenges beyond classical approaches.
Redefining Neural Operators in $d+1$ Dimensions
Authors: Haoze Song, Zhihao LI, Xiaobo Zhang, Zecheng Gan, Zhilu Lai, Wei Wang
Date: 2025-05-17 00:00:00
Tasks: Super-Resolution

Neural Operators have emerged as powerful tools for learning mappings between function spaces. Among them, the kernel integral operator has been widely validated on universally approximating various operators. Although recent advancements following this definition have developed effective modules to better approximate the kernel function defined on the original domain (with $d$ dimensions, $d=1, 2, 3...$), the unclarified evolving mechanism in the embedding spaces blocks our view to design neural operators that can fully capture the target system evolution. Drawing on recent breakthroughs in quantum simulation of partial differential equations (PDEs), we elucidate the linear evolution process in neural operators. Based on that, we redefine neural operators on a new $d+1$ dimensional domain. Within this framework, we implement our proposed Schr\"odingerised Kernel Neural Operator (SKNO) aligning better with the $d+1$ dimensional evolution. In experiments, our $d+1$ dimensional evolving linear block performs far better than others. Also, we test SKNO's SOTA performance on various benchmark tests and also the zero-shot super-resolution task. In addition, we analyse the impact of different lifting and recovering operators on the prediction within the redefined NO framework, reflecting the alignment between our model and the underlying $d+1$ dimensional evolution.
Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series
Authors: Wajdi Hammami, Soumaya Cherkaoui, Shengrui Wang
Date: 2025-05-16 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network, Time Series, Time Series Anomaly Detection

Quantum computing may offer new approaches for advancing machine learning, including in complex tasks such as anomaly detection in network traffic. In this paper, we introduce a quantum generative adversarial network (QGAN) architecture for multivariate time-series anomaly detection that leverages variational quantum circuits (VQCs) in combination with a time-window shifting technique, data re-uploading, and successive data injection (SuDaI). The method encodes multivariate time series data as rotation angles. By integrating both data re-uploading and SuDaI, the approach maps classical data into quantum states efficiently, helping to address hardware limitations such as the restricted number of available qubits. In addition, the approach employs an anomaly scoring technique that utilizes both the generator and the discriminator output to enhance the accuracy of anomaly detection. The QGAN was trained using the parameter shift rule and benchmarked against a classical GAN. Experimental results indicate that the quantum model achieves a accuracy high along with high recall and F1-scores in anomaly detection, and attains a lower MSE compared to the classical model. Notably, the QGAN accomplishes this performance with only 80 parameters, demonstrating competitive results with a compact architecture. Tests using a noisy simulator suggest that the approach remains effective under realistic noise-prone conditions.
The Post Double LASSO for Efficiency Analysis
Authors: Christopher Parmeter, Artem Prokhorov, Valentin Zelenyuk
Date: 2025-05-20 00:00:00

Big data and machine learning methods have become commonplace across economic milieus. One area that has not seen as much attention to these important topics yet is efficiency analysis. We show how the availability of big (wide) data can actually make detection of inefficiency more challenging. We then show how machine learning methods can be leveraged to adequately estimate the primitives of the frontier itself as well as inefficiency using the `post double LASSO' by deriving Neyman orthogonal moment conditions for this problem. Finally, an application is presented to illustrate key differences of the post-double LASSO compared to other approaches.
Valid Post-Contextual Bandit Inference
Authors: Ramon van den Akker, Bas J. M. Werker, Bo Zhou
Date: 2025-05-20 00:00:00
Tasks: Translation, valid

We establish an asymptotic framework for the statistical analysis of the stochastic contextual multi-armed bandit problem (CMAB), which is widely employed in adaptively randomized experiments across various fields. While algorithms for maximizing rewards or, equivalently, minimizing regret have received considerable attention, our focus centers on statistical inference with adaptively collected data under the CMAB model. To this end we derive the limit experiment (in the Hajek-Le Cam sense). This limit experiment is highly nonstandard and, applying Girsanov's theorem, we obtain a structural representation in terms of stochastic differential equations. This structural representation, and a general weak convergence result we develop, allow us to obtain the asymptotic distribution of statistics for the CMAB problem. In particular, we obtain the asymptotic distributions for the classical t-test (non-Gaussian), Adaptively Weighted tests, and Inverse Propensity Weighted tests (non-Gaussian). We show that, when comparing both arms, validity of these tests requires the sampling scheme to be translation invariant in a way we make precise. We propose translation-invariant versions of Thompson, tempered greedy, and tempered Upper Confidence Bound sampling. Simulation results corroborate our asymptotic analysis.
Characterization of Efficient Influence Function for Off-Policy Evaluation Under Optimal Policies
Authors: Haoyu Wei
Date: 2025-05-20 00:00:00
Tasks: counterfactual, Off-policy evaluation

Off-policy evaluation (OPE) provides a powerful framework for estimating the value of a counterfactual policy using observational data, without the need for additional experimentation. Despite recent progress in robust and efficient OPE across various settings, rigorous efficiency analysis of OPE under an estimated optimal policy remains limited. In this paper, we establish a concise characterization of the efficient influence function (EIF) for the value function under optimal policy within canonical Markov decision process models. Specifically, we provide the sufficient conditions for the existence of the EIF and characterize its expression. We also give the conditions under which the EIF does not exist.
CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction
Authors: Yingjie Kuang, Tianchen Zhang, Zhen-Wei Huang, Zhongjie Zeng, Zhe-Yuan Li, Ling Huang, Yuefang Gao
Date: 2025-05-19 00:00:00
Tasks: Time Series, Time Series Forecasting

Accurately predicting customers' purchase intentions is critical to the success of a business strategy. Current researches mainly focus on analyzing the specific types of products that customers are likely to purchase in the future, little attention has been paid to the critical factor of whether customers will engage in repurchase behavior. Predicting whether a customer will make the next purchase is a classic time series forecasting task. However, in real-world purchasing behavior, customer groups typically exhibit imbalance - i.e., there are a large number of occasional buyers and a small number of loyal customers. This head-to-tail distribution makes traditional time series forecasting methods face certain limitations when dealing with such problems. To address the above challenges, this paper proposes a unified Clustering and Attention mechanism GRU model (CAGRU) that leverages multi-modal data for customer purchase intention prediction. The framework first performs customer profiling with respect to the customer characteristics and clusters the customers to delineate the different customer clusters that contain similar features. Then, the time series features of different customer clusters are extracted by GRU neural network and an attention mechanism is introduced to capture the significance of sequence locations. Furthermore, to mitigate the head-to-tail distribution of customer segments, we train the model separately for each customer segment, to adapt and capture more accurately the differences in behavioral characteristics between different customer segments, as well as the similar characteristics of the customers within the same customer segment. We constructed four datasets and conducted extensive experiments to demonstrate the superiority of the proposed CAGRU approach.
Multivariate Affine GARCH with Heavy Tails: A Unified Framework for Portfolio Optimization and Option Valuation
Authors: Ayush Jha, Abootaleb Shirvani, Ali Jaffri, Svetlozar T. Rachev, Frank J. Fabozzi
Date: 2025-05-18 00:00:00
Tasks: Portfolio Optimization

This paper develops and estimates a multivariate affine GARCH(1,1) model with Normal Inverse Gaussian innovations that captures time-varying volatility, heavy tails, and dynamic correlation across asset returns. We generalize the Heston-Nandi framework to a multivariate setting and apply it to 30 Dow Jones Industrial Average stocks. The model jointly supports three core financial applications: dynamic portfolio optimization, wealth path simulation, and option pricing. Closed-form solutions are derived for a Constant Relative Risk Aversion (CRRA) investor's intertemporal asset allocation, and we implement a forward-looking risk-adjusted performance comparison against Merton-style constant strategies. Using the model's conditional volatilities, we also construct implied volatility surfaces for European options, capturing skew and smile features. Empirically, we document substantial wealth-equivalent utility losses from ignoring time-varying correlation and tail risk. These findings underscore the value of a unified econometric framework for analyzing joint asset dynamics and for managing portfolio and derivative exposures under non-Gaussian risks.
(Visualizing) Plausible Treatment Effect Paths
Authors: Simon Freyaldenhoven, Christian Hansen
Date: 2025-05-17 00:00:00

We consider point estimation and inference for the treatment effect path of a policy. Examples include dynamic treatment effects in microeconomics, impulse response functions in macroeconomics, and event study paths in finance. We present two sets of plausible bounds to quantify and visualize the uncertainty associated with this object. Both plausible bounds are often substantially tighter than traditional confidence intervals, and can provide useful insights even when traditional (uniform) confidence bands appear uninformative. Our bounds can also lead to markedly different conclusions when there is significant correlation in the estimates, reflecting the fact that traditional confidence bands can be ineffective at visualizing the impact of such correlation. Our first set of bounds covers the average (or overall) effect rather than the entire treatment path. Our second set of bounds imposes data-driven smoothness restrictions on the treatment path. Post-selection Inference (Berk et al. [2013]) provides formal coverage guarantees for these bounds. The chosen restrictions also imply novel point estimates that perform well across our simulations.
A New Bayesian Bootstrap for Quantitative Trade and Spatial Models
Authors: Bas Sanders
Date: 2025-05-17 00:00:00
Tasks: counterfactual

Economists use quantitative trade and spatial models to make counterfactual predictions. Because such predictions often inform policy decisions, it is important to communicate the uncertainty surrounding them. Three key challenges arise in this setting: the data are dyadic and exhibit complex dependence; the number of interacting units is typically small; and counterfactual predictions depend on the data in two distinct ways-through the estimation of structural parameters and through their role as inputs into the model's counterfactual equilibrium. I address these challenges by proposing a new Bayesian bootstrap procedure tailored to this context. The method is simple to implement and provides both finite-sample Bayesian and asymptotic frequentist guarantees. Revisiting the results in Waugh (2010), Caliendo and Parro (2015), and Artu\c{c} et al. (2010) illustrates the practical advantages of the approach.
Distribution Regression with Censored Selection
Authors: Ivan Fernandez-Val, Seoyun Hong
Date: 2025-05-16 00:00:00
Tasks: regression

We develop a distribution regression model with a censored selection rule, offering a semi-parametric generalization of the Heckman selection model. Our approach applies to the entire distribution, extending beyond the mean or median, accommodates non-Gaussian error structures, and allows for heterogeneous effects of covariates on both the selection and outcome distributions. By employing a censored selection rule, our model can uncover richer selection patterns according to both outcome and selection variables, compared to the binary selection case. We analyze identification, estimation, and inference of model functionals such as sorting parameters and distributions purged of sample selection. An application to labor supply using data from the UK reveals different selection patterns into full-time and overtime work across gender, marital status, and time. Additionally, decompositions of wage distributions by gender show that selection effects contribute to a decrease in the observed gender wage gap at low quantiles and an increase in the gap at high quantiles for full-time workers. The observed gender wage gap among overtime workers is smaller, which may be driven by different selection behaviors into overtime work across genders.
Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?
Authors: Martin Baily, David Byrne, Aidan Kane, Paul Soto
Date: 2025-05-20 00:00:00

With the advent of generative AI (genAI), the potential scope of artificial intelligence has increased dramatically, but the future effect of genAI on productivity remains uncertain with the effect of the technology on the innovation process a crucial open question. Some inventions, such as the light bulb, temporarily raise productivity growth as adoption spreads, but the effect fades when the market is saturated; that is, the level of output per hour is permanently higher but the growth rate is not. In contrast, two types of technologies stand out as having longer-lived effects on productivity growth. First, there are technologies known as general-purpose technologies (GPTs). GPTs (1) are widely adopted, (2) spur abundant knock-on innovations (new goods and services, process efficiencies, and business reorganization), and (3) show continual improvement, refreshing this innovation cycle; the electric dynamo is an example. Second, there are inventions of methods of invention (IMIs). IMIs increase the efficiency of the research and development process via improvements to observation, analysis, communication, or organization; the compound microscope is an example. We show that GenAI has the characteristics of both a GPT and an IMI -- an encouraging sign. Even so, for genAI to boost productivity growth, its contribution will have to exceed the fading growth effects of the past IT innovations baked into the trend, including predecessor AI technologies.
Keeping in Place After the Storm-Emergency Assistance and Evictions
Authors: Bilal Islah, Ahmed Zoulati
Date: 2025-05-20 00:00:00

We offer evidence that federal emergency assistance (FEMA) in the days following natural disasters mitigate evictions in comparison to similar emergency scenarios where FEMA aid is not provided. We find a 16% increase in overall evictions after hurricane natural disaster events that can be decomposed to a 36% increase for those that do not receive FEMA rental assitance and only a 11% for hurricane events that do receive FEMA aid. Furthermore, we also show that FEMA aid acts as a liquidity buffer to other forms of emergency credit, specifically we find that both transactions volumes remain stable and result in a decrease in defaults by 19% in payday loans during hurricane events in locations that do receive FEMA aid. This effect largely reverses in areas that do not receive FEMA aid, where transaction volumes drop by 12\% and default rates remain similar relative to the baseline. Overall, this suggests that the availability of emergency liquidity during natural disaster events is indeed a binding constraint with real household financial consequences, in particular through our documented channel of evictions and in usage of high-cost credit.
Effects of system-blind prosumers in energy models
Authors: Adeline Guéret, Wolf-Peter Schill, Felix Schmidt
Date: 2025-05-20 00:00:00

Prosumer households that generate and store electricity from rooftop PV installations play an increasing role in electricity markets around the world. As retail tariffs usually do not convey time-varying wholesale price signals to households and the rollout of smart meters is low in many countries, prosumers do not necessarily self-consume and feed-in solar electricity in a system-friendly way. The effects of such system-blind behaviours are typically neglected in energy system models, which rarely account for prosumers. In this paper, we embed a calibrated self-generation constraint into a linear capacity expansion model to approximate the incentives of prosumers to minimise their electricity bills. We apply our method to a German case study for 2030 featuring sector coupling with battery electric vehicles. We show that parametrising the self-generation constraint such that the prosumer electricity bill is as low as possible approximates prosumer decisions well for a broad range of tariff schemes. Based on this, we quantify distortions that might arise in energy models that do not account for prosumers. For our case study, we find that the optimal battery storage capacity increases by up to 200% if prosumer constraints are included. The main driver is the imperfect substitutability between home and utility-scale batteries. We conclude that energy system models could benefit from implementing this straightforward method.
Conglomerates, Liquidity Shocks, and Innovation-Led Growth
Authors: Payne Hennigan
Date: 2025-05-20 00:00:00

I develop a dynamic model of how internal capital markets in conglomerates respond to liquidity shocks when affiliated firms vary in innovation potential. A two-stage framework defines cutoff rules for when the conglomerate should liquidate low-productivity firms, coerce intermediate types into short-termist strategies, or preserve high-potential firms for long-horizon R&D. Embedding these margins into an endogenous growth model, I show how the optimal policy evolves: early in development, coercion preserves liquidity while sustaining broad innovation; as the economy nears the frontier and short-term returns decline, the optimal strategy shifts toward binary reallocation between liquidation and long-termism. I characterize two policy failures: a "coercion trap," where short-termism persists too long, and a "liquidation fallacy," where viable firms are discarded prematurely. The framework provides microfoundations for dynamic reallocation in conglomerate systems and offers policy insights for crisis-era restructuring.
Designing a Multi-Period Model for Economic and Low-Carbon Hydrogen Transportation in Texas
Authors: Yixuan Huang, Kailai Wang, Jian Shi
Date: 2025-05-20 00:00:00

The transition to hydrogen powered transportation requires regionally tailored yet scalable infrastructure planning. This study presents the first Texas specific, multi-period mixed integer optimization model for hydrogen transportation from 2025 to 2050, addressing challenges in infrastructure phasing, asset coordination, and multimodal logistics. The framework introduces three innovations: (1) phased deployment with delayed investment constraints, (2) dynamic modeling of fleet aging and replacement, and (3) a clustering-based hub structure enabling adaptive two-stage hydrogen delivery. Simulations show pipeline deployment supports up to 94.8% of hydrogen flow by 2050 under high demand, reducing transport costs by 23% compared to vehicle-based systems. However, one-year construction delays reduce pipeline coverage by over 60%, shifting reliance to costlier road transport. While the study focuses on Texas, its modular design and adaptable inputs apply to other regions. It provides a tool for policy makers and stakeholders to manage hydrogen transitions under logistical and economic constraints.