This paper aimed to develop a useful Machine Learning (ML) model for detecting companies with lasting competitive advantages (companies’ moats) according to their financial ratios in order to improve the performance of investment portfolios. First, we computed the financial ratios of companies belonging to the S&P 500. Subsequently, we assessed the stocks’ moats according to an evaluation defined between 0 and 5 for each financial ratio. The sum of all the ratios provided a score between 0 and 100 to classify the companies as wide, narrow or null moats. Finally, several ML models were applied for classification to obtain an efficient, faster and less expensive method to select companies with lasting competitive advantages. The main findings are: (1) the model with the highest precision is the Random Forest; and (2) the most important financial ratios for detecting competitive advantages are a long-term debt-to-net income, Depreciation and Amortization (D&A)-to-gross profit, interest expense-to-Earnings Before Interest and Taxes (EBIT), and Earnings Per Share (EPS) trend. This research provides a new combination of ML tools and information that can improve the performance of investment portfolios; to the authors’ knowledge, this has not been done before. The algorithm developed in this paper has a limitation in the calculation of the stocks’ moats since it does not consider its cost, price-to-earnings ratio (PE), or valuation. Due to this limitation, this algorithm does not represent a strategy for short-term or intraday trading.
Objective: To estimate the size and the dynamics of the coro-navirus (covid-19) pandemic in Advanced, Emerging, and Developing Economies, and to determine its implications for economic growth.Methodology: A susceptible Infected Recovered (sir) mod-el is implemented, we calculate the size of the pandemic through numerical integration and phase diagrams for covid-19 trajectory; finally, we use ensemble models (ran-dom forest) to forecast economic growth.Results: We confirm that there are differences in pandemic spread and size among countries; likewise, the trajectories show a long-term spiral cycle. Economic recovery is expect-ed to be slow and gradual for most of the economies.Limitations: All countries differ in covid-19 test applica-tions, which could lead to inaccurate total confirmed cases and an imprecise estimate of the pandemic’s spread and size. In addition, there is a lack of leading indicators in some countries, generating a higher mse of some machine learning models. Originality: To implement economic-epidemiological mod-els to analyze the evolution and virus’ spreading through-out time.Conclusions: It is found the pandemic’s final size to be be-tween 74-77%. Likewise, it is demonstrated that covid-19 is endemic, with a constant prevalence of 9 years on av-erage. The spread of the pandemic has caused countries to self-induce in an unprecedented recession with a slow recovery.
We aim to characterise the incidence and distribution of crime in 32 states of Mexico. A hierarchical grouping with average linkage is implemented with crime information from DataMéxico, segmented by state and type of crime from 2015 to 2020. Based on the proportional number of crimes over the population of each state, through the elbow method and the average linkage with a Cophenetic correlation coefficient, we validate the number of clusters. Subsequently, a principal component analysis (PCA) is performed to identify each state’s contribution to the clusters proposed. The main results reveal criminal activity can be characterised by three groups. Drug trafficking is the crime that leads the first group, which in turn generates subgroups of interrelated crimes, such as crimes against the family, sexual abuse and harassment, and falsehood, to name a few. These crimes are committed homogeneously in most of the states of the country. Correspondingly, domestic violence and theft lead clusters two and three, and present significant concentration levels since four states accumulate 62% and 55% of crime incidence respectively. The results also provide an overview of how a particular crime can trigger the presence of others.
Objetivo: analizar los componentes cíclicos entre variables económicas cuyos acoplamientos instauraron los mecanismos de propagación y causalidad en la formación del ciclo económico de la economía estadounidense. Método: se utiliza la técnica de sincronización de fase en series de tiempo para los componentes cíclicos de las variables sumada a un análisis de causalidad con un modelo VAR (Vectores Autorregresivos). Resultados: se encuentran los mecanismos de propagación entre variables mediante la secuenciación de los ciclos, reflejando los impactos de las decisiones de política monetaria en la economía agregada, transmitiendo inconscientemente ineficiencias en el desempeño y comportamiento de variables financieras y macroeconómicas. Recomendaciones: implementar análisis de componentes principales para capturar mejor los mecanismos de transmisión de política monetaria. Limitaciones e implicaciones: la dinámica de ajuste cíclico no es del todo pertinente para ser modelada en sistemas lineales. Principal contribución: se expone evidencia de una fuerte relación en subsistemas cíclicos de variables y la dinámica de transmisión y causalidad cíclica, revelando la generación de periodos de crisis económicas y financieras debidos a las decisiones de política monetaria.
Recientemente dos eventos exógenos han provocado desequilibrios económicos y sociales en la mayoría de los países: la crisis sanitaria del COVID-19 en 2020 y el conflicto bélico entre Rusia y Ucrania en 2022. La principal manifestación de estos eventos ha ocurrido a través de la inflación. En México, el crecimiento constante en los precios no se observaba desde la década de los noventa. Sin duda, esta situación provocará un endurecimiento de la política monetaria, lo que impactará negativamente en el crecimiento económico. Sin embargo, no basta solamente con alcanzar altas tasas de crecimiento económico; adicionalmente, es necesario fortalecer el sistema educativo y de salud, así como distribuir de manera equitativa la riqueza generada, sin omitir el fomento a la inversión pública y privada, permitiendo así el aumento en los niveles de empleo. Todo ello está encaminado a disminuir la desigualdad social e impulsar el desarrollo económico. Bajo este contexto, la presente obra contiene un enfoque multifactorial, expone un trabajo con una visión plural, con el fin de enriquecer las aportaciones hacia un mismo objetivo: presentar los factores que han impulsado o inhibido el desarrollo económico en México. La obra se divide en diez capítulos, los cuales han sido agrupados en cinco secciones, en función de la similitud de factores. De esta manera se tienen factores de crecimiento, entorno macroeconómico, mercado laboral, normatividad y COVID-19.
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