https://repositorio.ufpb.br/jspui/handle/123456789/36517 2026-07-16T15:55:04Z https://repositorio.ufpb.br/jspui/handle/123456789/38163 Título: Estudo sobre a evolução da eficiência das Odds no mercado de apostas de futebol Autor(es): Flôr, Matheus Santos de Oliveira Orientador: Viana, Jorge Henrique Norões Abstract: This study aims to analyze and compare the degree of informational efficiency in football betting markets for the English Premier League and the Brazilian Championship (Série A), with the purpose of identifying the presence, magnitude, and possible causes of pricing inefficiencies in the odds. The methodology was based on building a database through automated extraction (web scraping) and the integration of weather variables. The predictive modeling, focused on binary outcome classification, compared five machine learning algorithms, optimized via stochastic search and validated by the intertemporal walk-forward method to prevent data leakage. The prediction testing occurred in a simulated financial backtest environment. Statistical results demonstrated the superiority of Logistic Regression with L2 regularization (60.17% accuracy and 0.6202 AUC in the test set), which outperformed complex architectures based on decision trees. In the financial application guided by Expected Value (+EV) thresholds, a market asymmetry was observed: the algorithm achieved a positive Return on Investment (ROI) of 0.78% in the Brazilian Championship, but recorded a loss of -38.35% in the Premier League. It is concluded, further supported by Monte Carlo simulations, that the English market validates the Efficient Market Hypothesis in its semi-strong form due to its high liquidity, nullifying basic statistical advantages, whereas the Brazilian scenario, in its consolidation phase, still presents marginal inefficiencies that can be quantitatively exploited. Editor: Universidade Federal da Paraíba Tipo: TCC 2026-04-09T00:00:00Z https://repositorio.ufpb.br/jspui/handle/123456789/38109 Título: Modelo preditivo para despesas discricionárias sob restrição fiscal no ensino Superior: evidências a partir da UFPB Autor(es): Diniz, Tainá Alcantara Alves Orientador: Almeida, Aléssio Tony Cavalcanti de Abstract: Public budgeting is a central instrument of government planning; however, in Brazilian federal universities, its effectiveness is constrained by recurring fiscal restrictions, the authorizing nature of budget execution, and the persistence of incremental forecasting practices. In this context, the use of historical averages adjusted for inflation proves insufficient to capture the dynamics of discretionary expenditures. In light of this, this study analyzes the dynamics between institutional demand and budgetary constraints at the Federal University of Paraíba (UFPB) and develops a predictive model to estimate the monthly execution of these expenditures. The research is characterized as applied, with a quantitative approach and documentary research procedures, developed within the scope of a case study on UFPB. An integrated monthly database was built for the period from January 2015 to December 2025, bringing together budgetary, macroeconomic, institutional, and academic information, with committed expenditure as the dependent variable. Time series and machine learning models were compared, with emphasis on ARIMAX, SARIMAX, Random Forest, and XGBoost. The comparison among the algorithms revealed that the SARIMAX model presented the best predictive performance, recording a Mean Absolute Error (MAE) of R$ 7.91 million, a Root Mean Squared Error (RMSE) of R$ 9.97 million, and a Mean Absolute Scaled Error (MASE) of 0.55, outperforming the ARIMAX, Random Forest, and XGBoost models. Additionally, the feature importance analysis revealed that expenditure dynamics are strongly influenced by macroeconomic and academic variables, especially the exchange rate (14.4%), the National Construction Cost Index (INCC) (10.3%), and the number of student assistance beneficiaries (9.7%). It is concluded that budget planning should not rely solely on incremental parameters, but rather requires predictive tools capable of modeling the structural tension between growing institutional needs and severe external fiscal constraints. Editor: Universidade Federal da Paraíba Tipo: TCC 2026-04-07T00:00:00Z https://repositorio.ufpb.br/jspui/handle/123456789/37945 Título: Unificação de avaliações de produtos em E-commerce: uma solução computacional para apoiar decisões de compra online Autor(es): Rocha, Lívia Fernandes da Orientador: Viana, Jorge Henrique Norões Abstract: This study aimed to develop a web platform designed for the collection, integration, and unified presentation of product reviews from e-commerce platforms, focusing on Amazon and Mercado Livre. The research was motivated by the problem of fragmented online reviews, which makes product comparison more difficult and turns the consumer decision-making process into a more time-consuming and dispersed task. To address this issue, the study adopted an applied and experimental approach, structured in stages of automated data extraction, processing and standardization of the collected information, cross-platform integration, and the development of a web application for comparative visualization of results. Data collection was carried out through web scraping techniques using the Selenium, BeautifulSoup, and Pandas libraries, while the matching of equivalent products across the two platforms was performed based on Natural Language Processing techniques, using TF-IDF and cosine similarity. As a result, a functional platform was developed, capable of consolidating reviews and comparative information for 124 best-selling products from Amazon and Mercado Livre, gathering 16.568 collected reviews. The application allows users to visualize, within a single environment, data such as price, overall rating, number of reviews, and comments, as well as automatically identify the platform offering the best price and present summaries generated by artificial intelligence based on the collected comments, thereby contributing to a simpler and more efficient search and comparison process for consumers. Editor: Universidade Federal da Paraíba Tipo: TCC 2026-04-07T00:00:00Z