Introducción El sector retail en México ha experimentado una transformación significativa en los últimos años, con un punto de inflexión claro en 2020 debido a la pandemia de COVID-19. Este evento…
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…
La Autenticación biométrica: una solución ante el creciente fraude financiero. Autores: José Guadalupe Mendoza Macías | José Antonio Quesada Palacios - Date: Nov 11, 2024 En el contexto actual de…
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.
Note en la gráfica la estabilidad de las hectáreas cosechadas, la línea naranja es la media de la producción desde 2015. En este caso se puede observar como 2020 fue el año en que la producción bajo de manera significativa. Entonces salvo ese año que ha representado problemas a nivel mundial, el mercado parece muy estable, y esta muy por debajo del maíz blanco que es el que realmente domina el mercado , como veremos en otra gráfica.
El mercado accionario es el lugar donde las empresas pueden acudir para conseguir capital a través de la emisión de acciones a la venta. La otra opción que tienen las empresas para conseguir recursos es a través de deuda y pagar intereses sobre esa deuda adquirida...
El día de hoy es conocido el impacto que la información de diferentes medios tienen sobre diversas variables financieras. Destacadamente la información de twitter ha sido estudiada para estimar su impacto en diversos mercados como el accionario, tipo de cambio, criptomonedas entre otros.