Examinando por Materia "Mercado bursátil"
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Ítem ¿Los activos de inversión con criterios ASG generan valor para el inversionista? evidencia a partir de ETF(Universidad EAFIT, 2022) Hoyos, Jonathan Camilo; Restrepo Ochoa, Diana ConstanzaÍtem Análisis de la noticia del Acuerdo de Paz en los rendimientos del índice accionario(Universidad EAFIT, 2018) Córdoba García, Amelia Elizabeth; Burbano Agredo, Jhon Edinson; Trespalacios Carrasquilla, AlfredoÍtem An analysis of herd behavior in Latin American stock markets(Universidad EAFIT, 2016-06-22) Duarte Duarte, Juan Benjamín; Garcés Carreño, Laura Daniela; Sierra Suárez, Katherine JuliethÍtem Estrategia de inversión activa vs. pasiva: ¿es posible ganarle a un índice bursátil?(Universidad EAFIT, 2012) Aguirre Medina, Nicolás; Botero Botero, Jonatan; Almonacid, Paula MaríaÍtem Explorando la señalización corporativa a nivel sectorial en el mercado bursátil estadounidense : el caso de las recompras de acciones(Universidad EAFIT, 2024) Rugeles Aristizábal, Felipe; Chaparro Cardona, Juan Camilo; Montoya Gil, Juan MiguelThis research offers a detailed analysis of the informational content of stock buyback plan announcements and how the reactions of different market agents influence the Price-Earnings Ratio, relative to their sector, of the companies that execute them. The study covers a sample of 40 companies from the U.S. stock market between the years 2018 and 2023, using Fixed Effects models that control for entity and temporality. The results for the complete sample reveal that, on average, repurchase announcements are associated with excess returns of 1.05% in the Price-Earnings Ratio relative to their sector on the day the announcements are made. Furthermore, it is observed that these effects are durable within a ten-day post-announcement window, gradually diminishing to an equivalent effect of 0.20% daily on the tenth day. Additionally, sector-specific analyses show divergences in the results, varying in magnitude, durability, and the sign of the effects, reflecting the specific characteristics of the companies in each sector and how these condition the investors' reactions to the signals sent by the entrepreneurs.Ítem Predicting Stock prices in Latin America using Associative Deep Neural Networks(Universidad EAFIT, 2023) Gallego Rojas, Juan Fernando; Almonacid Hurtado, Paula MaríaThe stock market is a critical sector of the global economy, and predicting stock prices is of great interest to investors and companies. However, the movements of the market are volatile, non-linear, and complicated. This topic has attracted the attention of researchers, who have proposed formal models that demonstrate accurate predictions can be made with appropriate variables and techniques. Deep learning algorithms are often used for this purpose due to their superior accuracy in time series-based and complex pattern analysis. This paper proposes to predict the opening, closing, highest, and lowest stock prices of select Latin American market indexes using associative deep neural networks that can simultaneously predict related values based on the Long Short Term Memory (LSTM) technique, known for its high accuracy in this area. As well as using classic econometric methods for the analysis of time series such as ARIMA models. The proposed model achieved a good performance in terms of prediction, which in turn allows finding interesting trading opportunities for investors. The results of the models were measured using the average RMSE of the predicted prices metric and compared with those obtained using a naive model.