CUBE - Contribuições em Revistas Científicas / Contribution to Journals
URI permanente para esta coleção:
Navegar
Entradas recentes
- Bank risk-taking and impaired monetary policy transmissionPublication . Koenig, Philipp J.; Schliephake, EvaHow does risk-taking affect the transmission of interest rate changes into loan issuance? We study this question in a banking model with agency frictions. The risk-free rate affects bank lending via a portfolio adjustment and a loan risk channel. The former implies that the bank issues more loans when the risk-free rate falls. The latter implies that the bank may issue fewer loans because lower risk-free rates lead to higher risk-taking. Thus, the loan risk channel can counteract the portfolio adjustment channel. There exists a reversal rate, so that loan supply even contracts due to higher risk-taking. The model’s implications square with recent evidence on monetary transmission.
- Private financing, R&D, and export activity: evidence from PortugalPublication . Bação, Pedro; Martins, António; Portela, MiguelUsing firm-level data for Portugal, 2006–2021, we investigate linkages between private financing — private equity (including venture capital) and private debt — and firms’ exporting and innovation. Combining matching and regression procedures, we find that private financing is associated with exporting and R&D activity. Firms financed by private equity are more likely to export and to export a larger share of their sales. They also exhibit higher propensity to allocate employees and funds to R&D, and to channel a larger share of investment into it. Private debt is likewise positively related to innovation inputs and exports, but both effects are limited to the extensive margin.
- Digital twins for circular cities: planning for positive energy districtsPublication . Marante, Claudia Antunes; Rezazadeh, Arash; Bohnsack, RenéPositive energy districts (PEDs) address the energy issues of unsustainable urban development by producing more renewable energy than they consume. However, the transformation of PEDs face challenges that require the application of new technologies. This article focuses on the role of digital twins and generative AI to explore how these technologies can support the development of PEDs in line with circular economic principles. Based on a case study of an EU Horizon R&D project, this article develops a framework for implementing generative AI-assisted digital twins for PEDs and provides decision support for their integration into 9R circular economic strategies.
- Corrigendum to ‘Modelling time-varying volatility interactions’ (International Review of Financial Analysis, (2026), 111, C, (105098), (S1057521926000256), 10.1016/j.irfa.2026.105098)Publication . Campos-Martins, Susana; Amado, CristinaThe authors regret that in Eq. (7) the nonstationary component gt was incorrectly written asgt=??+?r=1qAr??t?r2+?s=1pBs?ht?sGt/T. The correct expression isgt=Gt/T??+?r=1qAr??t?r2+?s=1pBs?ht?s. This was a typographical error in the order of matrix-vector multiplication. This correction does not affect the results or conclusions of the paper. The authors would like to apologise for any inconvenience caused.
- Stratified consumer activism: how socioeconomic status shapes boycott participationPublication . Vieites, Yan; Fernandes, Daniel; Thompson, Debora V.Consumer activism is becoming increasingly common worldwide, but are all consumer groups as likely to engage in these practices? The current research investigates the presence of socioeconomic status (SES) differences in boycotting participation and explores the psychological processes underlying potential discrepancies. Results from four studies, including cross-national surveys, experiments, and lab-in-the-field evidence, show that low-SES consumers display a lower likelihood of boycotting companies than their high-SES peers. The phenomenon emerges across different measures, including self-reported intentions, past actions, and actual behaviors. This reduced inclination to boycott among the socioeconomically disadvantaged is driven by their reduced sense of control, which induces lower beliefs that consumption can be used as an effective instrument to enact change. These findings offer important contributions to the study of boycotting practices and shed new light on the complex relationship between socioeconomic conditions and consumer behavior.
- Evaluation of smartphone camera positioning on artificial intelligence pose estimation accuracy for exercise detection: observational studyPublication . Oliosi, Eduarda; Ferreira, Soraia; Giordano, Ana Paula; Viveiros, Guilherme; Parraca, José; Pereira, Paulo; Guede-Fernández, Federico; Azevedo, SaloméBackground: Artificial intelligence (AI)–driven pose estimation (PE) offers a scalable and cost-effective solution to track exercises in mobile health apps. However, occlusion, influenced by camera angle and distance, can reduce detection accuracy and repetition counting precision. The influence of smartphone positioning on these performance metrics remains underexplored in controlled studies. Objective: The study aimed to examine how smartphone camera angle (front, side, and diagonal) and distance (90 cm, 180 cm, 200 cm, and 360 cm) affect detection performance and repetition counting accuracy during push-ups and squats using AI-based PE. Methods: In this cross-sectional, within-subject study, 44 healthy university students (9 [20.5%] female participants; mean age 20.3 y, SD 0.4 y; mean BMI 23.2, SD 0.6 kg/m2) were assigned to perform either squats or push-ups. Each participant completed their assigned exercise across 12 predefined smartphone camera configurations, yielding approximately 264 squat trials (n=22) and 264 push-up trials (n=22). Each trial consisted of an average of 5 repetitions, totaling approximately 1320 repetitions per exercise. PE performance was assessed using binary classification accuracy, detection rate, and mean absolute error (MAE) for repetition counting. Generalized linear mixed-effects models evaluated classification odds, linear mixed-effects models analyzed MAE, and Tukey-adjusted post hoc tests followed significant effects. Results: The mean detection rate was 61.1% (SD 48.8%) for push-ups and 61.5% (SD 48.7%) for squats, with MAEs of 1.08 (SD 1.78) and 1.11 (SD 1.82) repetitions, respectively. Push-ups were most accurately detected from diagonal views at 90 to 180 cm (up to 85.7% detection; MAE=0.28) and least accurately from the front at 360 cm (20%; MAE=2.70). Squats performed best from a diagonal view at 200 cm (95.5%; MAE=0.05) and worst from the side at 90 cm (0%; MAE=5). Generalized linear mixed models showed that for push-ups, the front 90 cm and diagonal 360 cm views significantly reduced classification odds compared to the side 90 cm view (P=.03 and P=.04, respectively), whereas for squats, diagonal and front views significantly outperformed side views across all distances (P<.001). Post hoc tests confirmed that for push-ups, diagonal close or mid-range views had significantly lower MAEs than far front views, and for squats, diagonal and front views at 180 to 200 cm achieved the highest accuracy and lowest MAEs (P<.05). Conclusions: AI-based PE effectiveness for exercise tracking is significantly affected by smartphone positioning. Diagonal and frontal views at mid-range distances (180?200 cm) provided the highest detection accuracy and counting precision. These findings offer actionable guidance for developers, clinicians, coaches, and users optimizing mobile health exercise monitoring.
- Designing and evaluating technology-enabled sustainable tourism offerings: a case study of virtual reality conferencesPublication . Meath, Cristyn; Smith, Alex; Karlovsek, Jurij; Bubb, Luke; Bohnsack, Rene; Glencross, Mashhuda; Viller, Stephen; Weigel, Jason; Schlosser, Paul; Wilson, Syvannah; Bidmon, ChristinaGlobal sustainability transitions and the rise of emerging technologies present both threats and opportunities for tourism organisations to navigate. Yet no framework currently exists that provides guidance on how to identify the value of emerging technologies for specific tourism offerings, and how to design, implement, and evaluate the effectiveness of technology integration. This topic is explored through a case study of the integration of social virtual reality (VR) and digital twin technologies to develop an alternative to face-to-face conference offering in response to demands to reduce conference air travel and the associated CO2 emissions. The study, which takes a participatory action approach, comprises three ground-breaking social VR conferences, evolving from a small research pilot (GRONEN2020) to a non-academic conference (Circular Fashion Summit, Paris Fashion Week 2020) to a hybrid social VR, Zoom, and in-person session (SIGGRAPH Frontiers 2020). These events evidence the first known application of the integration of a building digital twin within a social VR platform for conferences. Integrating project findings and relevant literature, we propose the Technology-Enabled Sustainable Tourism Offering Framework, a dynamic framework that builds on Kolb’s Experiential Learning Theory to guide the implementation of emerging technologies for tourism offerings, and contribute timely theoretical insights.
- Modelling time-varying volatility interactionsPublication . Campos-Martins, Susana; Amado, CristinaWe propose an additive time-varying (or partially time-varying) multivariate model of volatility, where a time-dependent component is added to the extended vector GARCH process for modelling the dynamics of volatility interactions. Volatility co-dependence is allowed to change smoothly between two extreme states, and second-moment interdependence is identified through these structural changes. The estimation of the new time-varying vector GARCH process is simplified using an equation-by-equation estimator for the volatility equations in the first step and estimating the correlation matrix in the second step. A new Lagrange multiplier test is derived for testing the null hypothesis of constant volatility co-dependence against a smoothly time-varying interdependence between financial markets. Monte Carlo experiments show that the test statistic has satisfactory finite-sample properties. An empirical application to sovereign bond yields illustrates the modelling strategy and the usefulness of the new specification.
- Does the US individual income tax display systemic racism? Negative evidence from audited US federal tax return data for 1967–73Publication . Strauss, Robert P.; Gouveia, MiguelOfficial statistics from the US Bureau of Labor Statistics and the US Bureau of the Census have long documented differences by ethnicity in employment rates and incomes. Recently, it has been suggested that the structure of the US federal individual tax system is 'systemically racist' which we interpret to mean that the application of the Internal Revenue Code through collection of individual income taxes adversely affects African American compared to White individuals and households. This paper contributes to the public discussion of possible systemic racism in the US tax system issue by studying an unusual set of US individual income tax data. These data differ from those used in other studies in two important ways. First, race is not imputed but obtained from the administrative records of the Social Security Administration. Second, the income tax data are audited tax return data. Using these audited and administratively matched data, we estimate effective income tax functions with an explicit role for race. After accounting for the basic structure of the US tax system, we find no statistical evidence of systemic racism in the operation of the US federal individual income tax during the period under study. Our results show that once income and filing status are taken into account, the effective tax rate tax did not vary by race—a finding that remains robust across multiple checks.
- Artificial intelligence for algorithmic trading digital assets: evidence from the Counter-Strike 2 skin marketPublication . Guede-Fernández, Federico; Wagle, Yash; Dias, Pedro; Giordano, Ana Paula; Henriques, Lúcio; Costa, Gonçalo; Azevedo, SaloméIntroduction: The Counter-Strike 2 skin market has developed into a multi-billion-dollar digital asset ecosystem, characterized by high volatility, low liquidity, and pricing inefficiencies that differ substantially from traditional financial markets. Despite the growing economic relevance of virtual items, no previous study has systematically examined the use of artificial intelligence for skin trading. Methods: This work designs and evaluates an automated trading system that applies deep learning models, specifically Long Short-Term Memory networks and Neural Hierarchical Interpolation for Time Series, to forecast skin prices and guide trading decisions. A dataset of 12,000 unique skins from the Steam Market, covering the period from May 2024 to April 2025, was collected using the CSGOskins.gg application programming interface. To reflect real market conditions, the trading strategy incorporated the Steam Market restrictions of a seven-day minimum holding period and a ten percent transaction cost, and was benchmarked against a traditional buy-and-hold strategy. Backtesting was performed multiple time horizons of two, three, and 6 months. Portfolio selection was based on risk and return criteria, including a Sharpe ratio greater than one, a Sortino ratio greater than two, and a return on investment above five percent. Results: Artificial intelligence consistently outperforms buy-and-hold, particularly in smaller, more concentrated portfolios and over longer time horizons. For example, in 6-month simulations, artificial intelligence portfolios achieved returns approaching 20%, compared to 5% to 10% for buy-and-hold, with excess returns as high as 75% in small portfolios. Larger portfolios reduced absolute returns but improved risk-adjusted performance, confirming that diversification enhances stability while diluting raw profitability. Analysis of portfolio composition by rarity further revealed that artificial intelligence favors moderately rare and liquid skins such as Mil-Spec, resembling mid-cap equity investment strategies, while buy-and-hold accumulates rarer skins, analogous to small-cap holdings that rely on scarcity premiums. Discussion: These findings highlight that even in virtual goods markets, the trade-offs between return, risk, and diversification reflect established principles of modern portfolio theory. The study demonstrates both the feasibility and the potential of artificial intelligence-based trading systems in the Counter-Strike 2 skin economy, contributing methodological advances and practical insights for participants in this emerging digital asset market.