John Garvey

We explore how earnings surprises propagate through firm networks constructed from shared sell-side analyst coverage. Using a Net Beat/Miss (NBM) ratio that aggregates the weighted directional earnings outcomes of analyst-connected firms, we show that this ratio predicts focal firm beat probability with a Q1–Q5 spread of 16.17 percentage points - outperforming all four levels of GICS classification. This predictive power survives in a multivariate logit framework controlling for past beat rate, size, book-to-market, momentum, analyst coverage, and GICS peer outcomes, yielding an incremental marginal effect of 3.72 percentage points. The NBM ratio’s predictive power scales monotonically with size-adjusted network connectivity, i.e., firms with above-median analyst connections (net of size) produce a Q1–Q5 beat spread of 19.86% versus 14.84% for below-median firms, with a dose-response pattern that is monotonic across connectivity quintiles. This reflects an aggregation effect that shows denser networks average out idiosyncratic noise and amplify the common economic signal. Together, our findings are consistent with bounded rationality among the information intermediaries responsible for constructing the forecasts.

1. This study measures executive responsiveness during earnings announcement conference calls and analyzes how this communication behavior affects stock prices. Our analysis focuses on executives’ ...

Adequate ventilation is essential for maintaining indoor environmental quality in schools, where ventilation standards are often based on an indoor concentration of human-generated carbon dioxide (CO₂) above ambient levels. Low-cost non-dispersive infrared (NDIR) CO₂ sensors offer a practical solution for ventilation monitoring, yet variability between sensors can compromise accuracy, particularly when applications depend on the determination of precise concentration differences. This study evaluates the performance of twenty-three low-cost CO₂ sensors, developing normalisation functions to improve comparability across sensors, introducing an accessible methodology for on-site sensor calibration without the need for laboratory-grade reference equipment. The sensors were co-located for three independent test periods in 2025 representing typical school internal conditions in Ireland. Pre-normalisation analysis showed strong linearity (coefficient of determination (R²) = 0.999) but notable variability, with a mean root mean square error (RMSE) of 18.3 ppm and 0.45% of measurements outside manufacturers stated accuracy. Normalisation models were trained and validated using a leave-one-period-out approach. Regression-based correction yielded the greatest improvement, reducing RMSE by 16%. When applied to the full dataset, final correction factors reduced RMSE by 27%, out-of-range measurements by 43%, and proportional bias by 31%. Corrected sensors demonstrated highly consistent performance, particularly within the CO₂ ranges most relevant for classroom ventilation assessment, with an RMSE = 7.4 parts per million (ppm) at ambient concentrations and 11.9 ppm at concentrations below 1500 ppm. Field-based co-location in the deployment environment across full CO₂ cycles, combined with a network-derived global reference, produced effective correction factors. Performance declined marginally above 1500 ppm and during dynamic occupancy, while overall accuracy remained strong. The study presents a practical and accessible methodology for evaluating and normalising low-cost CO₂ sensors without specialised laboratory equipment, supporting reliable ventilation assessments in schools.

Indoor air quality (IAQ) and thermal comfort influence the health and cognitive performance of school occupants. This study investigated carbon dioxide (CO₂), nitrogen dioxide (NO₂), thermal comfort, and ventilation rates (VRs) in eight naturally ventilated (NV) primary school classrooms in Ireland during October 2024, combining environmental monitoring with teacher surveys. Mean CO₂ concentrations ranged from 796 ppm to 2469 ppm, exceeding national guidelines in seven of the eight classrooms. NO₂ levels ranged from 3.4 µg/m³ to 7.2 µg/m³, with indoor/outdoor ratios increasing with VRs and influenced by window orientation and road proximity. Indoor temperatures remained within recommended limits, while relative humidity ranged from 53% to 78% mirroring CO₂ trends and exceeding guideline levels in classrooms with lower VRs and temperatures. Occupied VRs ranged from 1.2 L/p/s to 4.1 L/p/s with window opening behaviours, reliant on teachers’ perceptions of thermal comfort, accounting for 84% to 96% of VRs. Ventilation in NV classrooms is often insufficient, yet increasing VRs can compromise thermal comfort and increase ingress of outdoor pollutants and noise. The findings highlight the ineffectiveness of current school ventilation standards, which rely heavily on user operation. Integrating occupant-led strategies, including scheduled purging, awareness campaigns, and pre-emptive air quality alerting, into policy offers practical, immediate pathways to improving IAQ, fostering healthy, sustainable learning environments.

Indoor air quality (IAQ) in schools significantly impacts occupant health and academic performance, especially in naturally ventilated (NV) classrooms, where CO₂ levels are often elevated. This systematic review synthesises findings from 125 studies, examining CO₂ as an indicator of ventilation rates (VRs) and its impact on IAQ, health, and academic performance in NV primary school classrooms. This analysis highlights seasonal and temporal variations in CO₂ concentrations, revealing a median CO₂ concentration of 1487 ppm across 2444 classrooms, with 81% exceeding the recommended 1000 ppm threshold. Influencing factors include VR, occupant density, generation rates, and occupant behaviours. Increased VRs consistently lowered CO₂ levels and enhanced IAQ. CO₂ concentrations correlated with particulate matter, volatile organic compounds, bioeffluents, microbial concentrations, and bacteria and fungi levels, but not with traffic-related pollutants like NO₂, which is associated with asthma prevalence. Elevated CO₂ levels consistently correlated with fatigue, headaches, respiratory symptoms, reduced academic performance and absenteeism, suggesting potential socio-economic benefits of increased VRs. However, effective IAQ management requires balancing ventilation with considerations of thermal comfort, noise, and outdoor pollutants. The findings highlight the need for standardised IAQ indices and CO₂ monitoring protocols, offering insights for future research, intervention design, and investment aimed at enhancing classroom environments.

Livestock marketplaces consist of infrastructure and rules through which information is exchanged and transactions are completed. Livestock trading incurs significant transaction costs, beyond just commission fees paid to marketplace owners (e.g., se-lecting, loading and transporting animals). In addition, animals being transported, held and transacted in a market setting increases the likelihood of injury or disease. The Covid-19 pandemic contributed to large-scale changes across typically physical marketplaces as stay-at-home orders and travel restrictions were invoked. Livestock marketplaces pivoted online rapidly, facilitated by the deployment of video feeds, al-lowing buyers to view/purchase animals via mobile phone. This shift offered the promise of mitigating many of the transaction costs associated with a fully physical marketplace. This study evaluated how computer vision (CV) approaches can con-tribute to the evolution of livestock marketplaces. It used livestock video data for 240 cattle (bulls) weighing 320-740 Kg from 16 different breeds to elicit additional trade-relevant data (livestock weight) and can be considered as an intermediary step to better understand how video data can be used to further digitalize livestock trade, price formation and transaction completion via technology solutions. The dataset comes from three sales held on different dates in a single mart location, which allows a consistent camera setup throughout. CV technologies were employed to solve the problem of cattle weight estimation and a distinctive achievement lies in curating a dataset cap-turing cattle in motion within the context of a cattle auction thereby injecting realism and complexity into the CV weight estimation task. Employing advanced object detection algorithms, the study created an intricate data pipeline to generate an appropriate modelling dataset and devised new multi-channel architectures for deeper analysis of diverse image inputs. The significance of sizable datasets and the challenge of man-aging inherent input variability for accurate CV regression is highlighted. Top-down view predictions demonstrate comparatively better performance, although failing to achieve benchmark precision. Challenges arise from camera angle variations, roaming behaviors, and breed diversity. Likewise, side-view predictions face challenges due to increased variability and lack of standardization. Despite this, these predictions offer insights into the value of incorporating Region of Interest (ROI) coordinates and data augmentation for enhanced performance. The study concludes with a multi-input ar-chitecture combining top-down and side-view predictions, demonstrating moderate success highlighting potential synergy between both perspectives. The central con-clusion resonates strongly – using standardized and multi-view images encompassing controlled movement and consistent poses could bolster accurate CV weight estima-tion.