pwc-archive/papers-with-abstracts huggingface dataset:
Residual Income Valuation and Stock Returns: Evidence from a Value-to-Price Investment Strategy
Authors: Ahmad Haboub, Aris Kartsaklas, Vasilis Sarafidis
Date: 2025-05-30 00:00:00

We hypothesize that portfolio sorts based on the V/P ratio generate excess returns and consist of companies that are undervalued for prolonged periods. Results, for the US market show that high V/P portfolios outperform low V/P portfolios across horizons extending from one to three years. The V/P ratio is positively correlated to future stock returns after controlling for firm characteristics, which are well known risk proxies. Findings also indicate that profitability and investment add explanatory power to the Fama and French three factor model and for stocks with V/P ratio close to 1. However, these factors cannot explain all variation in excess returns especially for years two and three and for stocks with high V/P ratio. Finally, portfolios with the highest V/P stocks select companies that are significantly mispriced relative to their equity (investment) and profitability growth persistence in the future.
Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective
Authors: Erfan Loghmani
Date: 2025-05-30 00:00:00

Large language models are being widely used across industries to generate content that contributes directly to key performance metrics, such as conversion rates. Pretrained models, however, often fall short when it comes to aligning with human preferences or optimizing for business objectives. As a result, fine-tuning with good-quality labeled data is essential to guide models to generate content that achieves better results. Controlled experiments, like A/B tests, can provide such data, but they are often expensive and come with significant engineering and logistical challenges. Meanwhile, companies have access to a vast amount of historical (observational) data that remains underutilized. In this work, we study the challenges and opportunities of fine-tuning LLMs using observational data. We show that while observational outcomes can provide valuable supervision, directly fine-tuning models on such data can lead them to learn spurious correlations. We present empirical evidence of this issue using various real-world datasets and propose DeconfoundLM, a method that explicitly removes the effect of known confounders from reward signals. Using simulation experiments, we demonstrate that DeconfoundLM improves the recovery of causal relationships and mitigates failure modes found in fine-tuning methods that ignore or naively incorporate confounding variables. Our findings highlight that while observational data presents risks, with the right causal corrections, it can be a powerful source of signal for LLM alignment. Please refer to the project page for code and related resources.
Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series
Authors: Jarek Duda
Date: 2025-05-20 00:00:00
Tasks: Time Series

Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving estimator, which estimates parameters separately for every time $t$: optimizing $F_t=\sum_{\tau<t} (1-\eta)^{t-\tau} \ln(\rho_\theta (x_\tau))$ local log-likelihood with exponentially weakening weights of the old values. In practice such moving estimates can be found by EMA (exponential moving average) of some parameters, like $m_p=E[|x-\mu|^p]$ absolute central moments, updated by $m_{p,t+1} = m_{p,t} + \eta (|x_t-\mu_t|^p-m_{p,t})$. We will focus here on its applications for alpha-Stable distribution, which also influences Hurst exponent, hence can be used for its adaptive estimation. Its application will be shown on financial data as DJIA time series - beside standard estimation of evolution of center $\mu$ and scale parameter $\sigma$, there is also estimated evolution of $\alpha$ parameter allowing to continuously evaluate market stability - tails having $\rho(x) \sim 1/|x|^{\alpha+1}$ behavior, controlling probability of potentially dangerous extreme events.
Asymmetric price adjustment over the business cycle
Authors: Daniel Levy, Haipeng, Chen, Sourav Ray, Elliot Charette, Xiao Ling, Weihong Zhao, Mark Bergen, Avichai Snir
Date: 2025-06-12 00:00:00

Studies of micro-level price datasets find more frequent small price increases than decreases, which can be explained by consumer inattention because time-constrained shoppers might ignore small price changes. Recent empirical studies of the link between shopping behavior and price attention over the business cycle find that consumers are more attentive to prices during economic downturns, and less attentive during economic booms. These two sets of findings have a testable implication. The asymmetry in small price changes should vary over the business cycle. It should diminish during recessions and strengthen during expansions. We test this prediction using a large US store-level dataset with more than 98 million weekly price observations for the years 1989-1997, which includes an 8-month recession period, as defined by the NBER. We compare price adjustments between periods of recession - high unemployment, and expansion - low unemployment. Focusing on small price changes, we find, consistent with our hypothesis, that there is a greater asymmetry in small price changes during periods of low unemployment compared to the periods of high unemployment, implying that firms price-setting behavior varies over the business cycle.
Getting Explicit Instruction Right
Authors: Richard Holden, Fabio I. Martinenghi
Date: 2025-06-12 00:00:00

There has been substantial public debate about the potentially deleterious effects of the long-run move to ``inquiry-based learning'' in which students are placed at the center of an educational journey and arrive at their own understanding of what is being taught. There have been numerous calls for a return to ``direct'' or ``explicit'' instruction. This paper focuses on identifying the causal effect of correctly implementing explicit instruction on student performance in standardized tests. We utilise a unique setting in Australia\textemdash a country in which all students in grades 3, 5, 7, and 9 undergo annual basic skills tests (``NAPLAN''). We use a synthetic control approach to study the effect of introducing Explicit Instruction in Charlestown South Public School\textemdash a median-performing school\textemdash on Year-3 and Year-5 NAPLAN scores in Reading and Numeracy. Importantly, this is achieved via peer modelling, with Charlestown teaching staff sitting-in during the classes of a high-performing explicit-instruction school. We find that the performance gains are substantial and persistent. JEL: I20, I21. Keywords: Education, Explicit Instruction, Pedagogy.
An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
Authors: Wishnu Badrawani
Date: 2025-06-12 00:00:00
Tasks: Ensemble Learning, Interpretable Machine Learning, regression

This paper evaluates the performance of prominent machine learning (ML) algorithms in predicting Indonesia's inflation using the payment system, capital market, and macroeconomic data. We compare the forecasting performance of each ML model, namely shrinkage regression, ensemble learning, and super vector regression, to that of the univariate time series ARIMA and SARIMA models. We examine various out-of-bag sample periods in each ML model to determine the appropriate data-splitting ratios for the regression case study. This study indicates that all ML models produced lower RMSEs and reduced average forecast errors by 45.16 percent relative to the ARIMA benchmark, with the Extreme Gradient Boosting model outperforming other ML models and the benchmark. Using the Shapley value, we discovered that numerous payment system variables significantly predict inflation. We explore the ML forecast using local Shapley decomposition and show the relationship between the explanatory variables and inflation for interpretation. The interpretation of the ML forecast highlights some significant findings and offers insightful recommendations, enhancing previous economic research that uses a more established econometric method. Our findings advocate ML models as supplementary tools for the central bank to predict inflation and support monetary policy.
The COVID-19 Inflation Weighting in Israel
Authors: Jonathan Benchimol, Itamar Caspi, Yuval Levin
Date: 2025-06-11 00:00:00
Proceeding: The Economists’ Voice 2022 6

Significant shifts in the composition of consumer spending as a result of the COVID-19 crisis can complicate the interpretation of official inflation data, which are calculated by the Central Bureau of Statistics (CBS) based on a fixed basket of goods. We focus on Israel as a country that experienced three lockdowns, additional restrictions that significantly changed consumer behavior, and a successful vaccination campaign that has led to the lifting of most of these restrictions. We use credit card spending data to construct a consumption basket of goods representing the composition of household consumption during the COVID-19 period. We use this synthetic COVID-19 basket to calculate the adjusted inflation rate that should prevail during the pandemic period. We find that the differences between COVID-19-adjusted and CBS (unadjusted) inflation measures are transitory. Only the contribution of certain goods and services, particularly housing and transportation, to inflation changed significantly, especially during the first and second lockdowns. Although lockdowns and restrictions in developed countries created a significant bias in inflation weighting, the inflation bias remained unexpectedly small and transitory during the COVID-19 period in Israel.
TrendFolios: A Portfolio Construction Framework for Utilizing Momentum and Trend-Following In a Multi-Asset Portfolio
Authors: Joseph Lu, Randall R Rojas, Fiona C. Yeung, Patrick D. Convery
Date: 2025-06-11 00:00:00

We design a portfolio construction framework and implement an active investment strategy utilizing momentum and trend-following signals across multiple asset classes and asset class risk factors. We quantify the performance of this strategy to demonstrate its ability to create excess returns above industry standard benchmarks, as well as manage volatility and drawdown risks over a 22+ year period.
Export proceeds repatriation policies: A shield against exchange rate volatility in emerging markets?
Authors: Sondang Marsinta Uli Panggabean, Mahjus Ekananda, Beta Yulianita Gitaharie, Leslie Djuranovik
Date: 2025-06-10 00:00:00
Tasks: Sensitivity

We examine the impact of mandatory export proceeds repatriation on exchange rate stability in three emerging markets, Iran, Sri Lanka, and Turkey, using the Generalized Synthetic Control framework. By modeling exchange rate stochastic volatility as our outcome of interest and controlling for interest rate differentials, exchange rate regime shifts, and inflation rate gaps, we address both unobserved time-varying confounders and heterogeneous treatment effects. Our estimates reveal no statistically significant impact of repatriation mandates on exchange-rate volatility across the three countries. We also find that we cannot reject the possibility of a non-zero impact. These results remain robust to an extensive range of sensitivity analyses, including alternative covariate specifications and placebo tests, thereby confirming their reliability.
Can knowledge reclassification accelerate technological innovation?
Authors: Peter Persoon
Date: 2025-06-10 00:00:00

Technological knowledge evolves not only through the generation of new ideas, but also through the reinterpretation of existing ones. Reinterpretations lead to changes in the classification of knowledge, that is, reclassification. This study investigates how reclassified inventions can serve as renewed sources of innovation, thereby accelerating technological progress. Drawing on patent data as a proxy for technological knowledge, I discuss two empirical patterns: (i) more recent patents are more likely to get reclassified and (ii) larger technological classes acquire proportionally more reclassified patents. Using these patterns, I develop a model that explains how reclassified inventions contribute to faster innovation. The predictions of the model are supported across all major technology domains, suggesting a strong link between reclassification and the pace of technological advancement. More generally, the model connects various, seemingly unrelated knowledge quantities, providing a basis for knowledge intrinsic explanations of growth patterns.
Valuing Diffuse Global Public Goods from Satellite Constellations: Evidence from GPS and Airline Delays
Authors: Lev Ricanati
Date: 2025-06-09 00:00:00

This paper studies the welfare impact of discrete improvements to global public goods in the context of the Global Positioning System (GPS). Specifically, I find that by disabling Selective Availability in May, 2000, and thus significantly increasing the accuracy of GPS, the United States generated at least \$268 million (2000 dollars) of additional welfare gains. To quantify this welfare impact, I apply a difference-in-differences model to the Bureau of Transportation Statistics's Airline On-Time Performance Data in the years 1999 and 2000. I use this model to estimate the time saved per flight attributable to the improved GPS and multiply these time savings by the number of passengers in the ensuing year and their values of time. I conclude by estimating the economic loss from current threats to the provision of satellite-based global public goods.
Unmasking inequility: socio-economic determinants and gender disparities in Maharashtra and India's health outcomes -- Insights from NFHS-5
Authors: Sharmishtha Raghuvanshi, Supriya Sanjay Nikam, Manisha Karne, Satyanarayan Kishan Kothe
Date: 2025-06-09 00:00:00

This research examines the persistent challenge of health inequalities in India, departing from the conventional focus on aggregate improvements in mortality rates. While India has achieved progress in overall health indicators since independence, the distribution of health outcomes remains uneven, a fact starkly highlighted by the COVID-19 pandemic. This study investigates the socio-economic determinants of health disparities using the National Family and Health Survey (NFHS)-5 data from 2019-20, focusing on both national and state-level analyses, specifically for Maharashtra. Employing a health economics framework, the analysis delves into individual-level data, population shares, self-reported morbidity prevalence, and treatment patterns across diverse socio-economic groups. Regression analyses, stratified by gender, are conducted to quantify the impact of socio-economic factors on reported morbidity. Furthermore, a Fairlie decomposition, an extension of the Oaxaca decomposition, is utilised to dissect the gender gap in morbidity, assessing the extent to which observed differences are attributable to explanatory variables. The findings reveal a significant burden of self-reported morbidity, with approximately one in nine individuals in India and one in eight in Maharashtra reporting morbidity. Notably, women exhibit nearly double the morbidity rate compared to men. The decomposition analysis identifies key drivers of gender disparities. In India, marital status exacerbates these differences, while insurance coverage, caste, urban residence, and wealth mitigate them. In Maharashtra, urban residence and marital status widen the gap, whereas religion, caste, and insurance coverage narrow it. This research underscores the importance of targeted policy interventions to address the complex interplay of socio-economic factors driving health inequalities in India.
Balanced Area Deprivation Index (bADI): Enhancing social determinants of health indices to strengthen their association with healthcare clinical outcomes, utilization and costs
Authors: Mohammad Amin Morid, Robert E Tillman, Eran Halperin
Date: 2025-06-09 00:00:00

Background: As value-based care expands across the U.S. healthcare system, reducing health disparities has become a priority. Social determinants of health (SDoH) indices, like the widely used Area Deprivation Index (ADI), guide efforts to manage patient health and costs. However, the ADI's reliance on housing-related variables (e.g., median home value) may reduce its effectiveness, especially in high-cost regions, by masking inequalities and poor health outcomes. Methods: To overcome these limitations, we developed the balanced ADI (bADI), a new SDoH index that reduces dependence on housing metrics through standardized construction. We evaluated the bADI using data from millions of Medicare Fee-for-Service and Medicare Advantage beneficiaries. Correlation analyses measured its association with clinical outcomes, life expectancy, healthcare use, and cost, and compared results to the ADI. Results: The bADI showed stronger correlations with clinical outcomes and life expectancy than the ADI. It was less influenced by housing costs in expensive regions and more accurately predicted healthcare use and costs. While ADI-based research suggested both the most and least disadvantaged groups had higher healthcare costs, the bADI revealed a more nuanced pattern, showing more accurate cost differences across groups. Conclusions: The bADI offers stronger predictive power for healthcare outcomes and spending, making it a valuable tool for accountable care organizations. By reallocating resources from less to more disadvantaged areas, ACOs could use the bADI to promote equity and cost-effective care within population health initiatives.
The impact of extracurricular education on socioeconomic mobility in Japan: an application of causal machine learning
Authors: Yang Qiang
Date: 2025-06-09 00:00:00

This paper explores the socioeconomic impacts of extracurricular education, specifically private tutoring, on social mobility in Japan. Using data from the 2015 National Survey on Social Stratification and Social Mobility (SSM), we employed a causal machine learning approach to evaluate this educational intervention on income, educational attainment, and occupational prestige. Our research suggests that while shadow education holds the potential for positive socioeconomic impacts, its benefits are undermined by the economic disparities among households, resulting in minimal overall improvement. This highlights the complex mechanisms between individual demographics and educational interventions, revealing promising machine learning applications in this field.
Do conditional cash transfers in childhood increase economic resilience in adulthood? Evidence from the COVID-19 pandemic shock in Ecuador
Authors: José-Ignacio Antón, Ruthy Intriago, Juan Ponce
Date: 2025-06-07 00:00:00

The primary goal of conditional cash transfers (CCTs) is to alleviate short-term poverty while preventing the intergenerational transmission of deprivation by promoting the accumulation of human capital among children. Although a substantial body of research has evaluated the short-run impacts of CCTs, studies on their long-term effects are relatively scarce, and evidence regarding their influence on resilience to future economic shocks is limited. As human capital accumulation is expected to enhance individuals' ability to cope with risk and uncertainty during turbulent periods, we investigate whether receiving a conditional cash transfer -- specifically, the Human Development Grant (HDG) in Ecuador -- during childhood improves the capacity to respond to unforeseen exogenous economic shocks in adulthood, such as the COVID-19 pandemic. Using a regression discontinuity design (RDD) and leveraging merged administrative data, we do not find an overall effect of the HDG on the target population. Nevertheless, we present evidence that individuals who were eligible for the programme and lived in rural areas (where previous works have found the largest effects in terms of on short-term impact) during their childhood, approximately 12 years before the pandemic, exhibited greater economic resilience to the pandemic. In particular, eligibility increased the likelihood of remaining employed in the formal sector during some of the most challenging phases of the COVID-19 crisis. The likely drivers of these results are the weak conditionality of the HDG and demand factors given the limited ability of the formal economy to absorb labour, even if more educated.
(In)stability in the Dynamics of the Cross-Country Distribution of Income Per Capita
Authors: Davide Fiaschi, Paul Johnson
Date: 2025-06-07 00:00:00

Using a panel of 102 countries from PWT 10.0 covering 1970-2019, we examine the veracity of the assumption that a time-homogeneous, first-order process describes the evolution of the cross-country distribution of per capita output, an assumption often made in studies of the convergence hypothesis employing the distribution dynamics approach pioneered by Quah (1993). To test homogeneity, we compare transition kernels estimated for different time periods and, for those periods exhibiting evidence of homogeneity, we test the first-order assumption using an implication of such a process's Chapman-Kolmogorov equations. Both tests require measurement of the distance between probability distributions which we do with several different metrics, employing bootstrap methods to assess the statistical significance of the observed distances. We find that the process was time-homogeneous and first-order in the 1970-1995 period during which the distribution dynamics imply a bimodal long-run distribution, consistent with convergence clubs. Following the apparent break in the process in the late 1990s, the 2000-2010 distribution dynamics imply a unimodal long-run distribution suggestive of a single convergence club, consistent with recent claims of short-term beta-convergence from the late 1990s and beyond made by Patel et al. (2021) and Kremer et al (2022). After 2010, there is some evidence of a return to non-convergent dynamics similar to those of the 1970-1995 period.
Solving Nash Equilibria in Nonlinear Differential Games for Common-Pool Resources
Authors: Yongyang Cai, Anastasios Xepapadeas, Aart de Zeeuw
Date: 2025-06-07 00:00:00

Many resources are provided by an ecological system that is vulnerable to tipping when exceeding a certain level of pollution, with a sudden big loss of ecosystem services. An ecological system is usually also a common-pool resource and therefore vulnerable to suboptimal use resulting from non-cooperative behavior. An analysis requires methods to derive cooperative and non-cooperative solutions for managing a dynamical system with tipping points. Such a game is a differential game which has two well-defined non-cooperative solutions, the open-loop and feedback Nash equilibria. This paper provides new numerical methods for deriving open-loop and feedback Nash equilibria, for one-dimensional and two-dimensional dynamical systems. The methods are applied to the lake game, which is the classical example for these types of problems. Especially, two-dimensional feedback Nash equilibria are a novelty of this paper. This Nash equilibrium is close to the cooperative solution which has important policy implications.