DSpace Collection:https://repositorio.ufu.br/handle/123456789/177522026-09-06T06:50:17Z2026-09-06T06:50:17ZEfeito de diferentes dosagens do adubo orgânico à base de casca de maracujá na cultura da mandioca no Cerradohttps://repositorio.ufu.br/handle/123456789/501092026-09-04T06:31:37Z2026-03-04T00:00:00ZTitle: Efeito de diferentes dosagens do adubo orgânico à base de casca de maracujá na cultura da mandioca no Cerrado2026-03-04T00:00:00ZModelo preditivo do ciclo de colheita de sementes de milhohttps://repositorio.ufu.br/handle/123456789/498812026-08-28T06:19:43Z2026-08-05T00:00:00ZTitle: Modelo preditivo do ciclo de colheita de sementes de milho2026-08-05T00:00:00ZImpacto da COVID-19 na saúde cardiovascularhttps://repositorio.ufu.br/handle/123456789/497792026-08-25T06:22:31Z2026-08-07T00:00:00ZTitle: Impacto da COVID-19 na saúde cardiovascular2026-08-07T00:00:00ZEntre o acaso e a inteligência: uma comparação entre investimentos na B3 baseados em aprendizado de máquina e o jogo da roletahttps://repositorio.ufu.br/handle/123456789/490342026-07-24T06:19:18Z2025-11-10T00:00:00ZTitle: Entre o acaso e a inteligência: uma comparação entre investimentos na B3 baseados em aprendizado de máquina e o jogo da roleta
Abstract: This study aims to investigate the use of machine learning algorithms as support for medium-term investment strategies on the Brazilian Stock Exchange (B3), comparing their percentage
performance with that of casino roulette betting systems. In this context, three supervised classification models are tested, namely: k-nearest neighbors (k-NN), decision tree, and random
forest. Thus, the signals generated by these models are employed in buy-only transactions of
the target assets — without any sell orders — allowing evaluation of their wealth multiplication
potential from the perspective of the risk-return trade-off inherent in the equity market. Regarding the assets analyzed, over 30 stocks comprising the Ibovespa index are initially examined;
at the end of this selection process, four of them — assessed on weekly and monthly price scales — are defined as the objects of study: VALE3, ITUB4, PETR4, and SBSP3. In parallel with
the computational modeling related to the stock market, a computational system based on the
probabilistic logic of casino roulette betting is implemented to establish a benchmark of random
nature for comparative purposes. Within this system, the Streets of Gold betting strategy is applied in pursuit of profit maximization and mitigation of the randomness effects inherent to the
game. The results indicate that, in absolute terms, no model demonstrates superiority across
all assets and time horizons; however, certain combinations achieve significantly higher returns
than the betting system under identical conditions of time (rounds) and available capital, delivering not only substantial financial gains but also lower exposure to wealth risk. Thus, this
study contributes to the existing academic literature by demonstrating the potential of machine
learning in formulating financial strategies that provide practical support to investors seeking
quantitative alternatives in highly volatile market scenarios. Additionally, it underscores the
purely recreational nature of betting systems.2025-11-10T00:00:00Z