EP. 3 – Más allá del algoritmo: Construir comunidades científicas desde la operación.
Detrás de cada comunidad científica vibrante hay una mente operativa que convierte visión en estructura. En este episodio, conversamos con Bridaylú Furiate, la fuerza detrás de Conociverso, una plataforma que transforma la educación científica en Latinoamérica.
Bridaylú no solo diseña estrategias: las implementa, las mide y las vive junto con su comunidad. Su enfoque de marketing científico se aleja de fórmulas vacías y se basa en utilidad, evidencia y claridad. Hablamos de cómo lidera como mujer en ciencia y tecnología, cómo conecta con investigadores entrenados para cuestionarlo todo, y por qué su comunidad se ha convertido en un verdadero laboratorio de colaboración.
¿Qué pasa cuando combinas ciencia, tecnología y emprendimiento en una sola plataforma?
Surge Conociverso:
la comunidad que ya ha formado a más de 6,000 investigadores en las áreas más dinámicas del conocimiento científico.
En los últimos años, el mercado inmobiliario de Sinaloa ha mostrado una dinámica peculiar, caracterizada por incrementos constantes en los precios de las propiedades, incluso en un contexto de disminución…
Análisis Integral del Mercado Inmobiliario en México: Tendencias, Costos y Factores Determinantes Autor: Roman Alejandro Mendoza Urdiales - Date: May 21, 2024 Introducción al Mercado Inmobiliario en MéxicoEl mercado inmobiliario…
In this study, a database of approximately 50 million tweets was used for the estimation of the positive and negative sentiment factors for 2557 companies operating in US stock market. For each company, the sentiment factors were calculated through the mean equations on GARCH models of different orders. Our findings show that, for 503 companies the negative factor effect has a greater impact than the positive factor effect. The period analyzed was from October 2022 to January 2023, using hourly observations. Results provide evidence to support that there is an asymmetric effect from the factors traveling to the stock market and it takes at least an hour the signal to travel. The investors and regulatory agents can find useful the results given that news has been demonstrated a source of influence in the market. Therefore, news impact can be modeled into portfolio theory using GARCH which is easy to implement and to interpret. Given the exposure of prices and volatility to news, it can be considered that these findings provide evidence to support efficient market hypothesis. Modeling returns and volatility for the assets through GARCH family is a widely known tool. Including the news sentiment on social media is dually a novelty: the empirical demonstration of the effects of social comments on the stock performance and volatility, in addition to the use of a large data set of social network comments in an hourly frequency.
Financial economic research has extensively documented the fact that the impact of the arrival of negative news on stock prices is more intense than that of the arrival of positive news. The authors of the present study followed an innovative approach based on the utilization of two artificial intelligence algorithms to test that asymmetric response effect. Methods: The first algorithm was used to web-scrape the social network Twitter to download the top tweets of the 24 largest market-capitalized publicly traded companies in the world during the last decade. A second algorithm was then used to analyze the contents of the tweets, converting that information into social sentiment indexes and building a time series for each considered company. After comparing the social sentiment indexes’ movements with the daily closing stock price of individual companies using transfer entropy, our estimations confirmed that the intensity of the impact of negative and positive news on the daily stock prices is statistically different, as well as that the intensity with which negative news affects stock prices is greater than that of positive news. The results support the idea of the asymmetric effect that negative sentiment has a greater effect than positive sentiment, and these results were confirmed with the EGARCH model.
Transfer Entropy was applied to analyze the correlations and flow of information between 200,500 tweets and 23 of the largest capitalized companies during 6 years along the period 2013-2018. The set of tweets were obtained applying a text mining algorithm and classified according to daily date and company mentioned. We proposed the construction of a Sentiment Index applying a Natural Processing Language algorithm and structuring the sentiment polarity for each data set. Bootstrapped Simulations of Transfer Entropy were performed between stock prices and Sentiment Indexes. The results of the Transfer Entropy simulations show a clear information flux between general public opinion and companies’ stock prices. There is a considerable amount of information flowing from general opinion to stock prices, even between different Sentiment Indexes. Our results suggest a deep relationship between general public opinion and stock prices. This is important for trading strategies and the information release policies for each company.
