En esta guía encuentras paso a paso cómo usar opciones de divisas para la cobertura de riesgo cambiario. Inicia con una explicación básica de opciones europeas call y put, la diferencia entre comprarlas y venderlas, las letras griegas y su uso y cierra con combinaciones de opciones (estructuras) para coberturas.

En este documento encontrarás el paso a paso para vivir la experiencia del simulador de trading de futuros, en este caso de FX, en la plataforma del Chicago Mercantile Exchange.

En este ejercicio, aprenderán a utilizar Refinitiv para seleccionar empresas, analizar gráficos de velas y construir un portafolio de inversión. Aprenderán también conceptos básicos de diversificación y análisis técnico, y presentarán sus decisiones de inversión en Gamma App.

En este Workshop se encuentrala solución del assessment de la evidencia de la materia Algoritmos y Análisis de Datos FZ2022

En este Workshop se encuentran los lineamientos e instrucciones para desarrollar el assessment de la evidencia de la materia Algoritmos y Análisis de Datos FZ2022

Solution for Workshop 3. We will winsorize variables, merge many-to-one, create a logit model, from which we will predict the probability of future stock returns being above the market (beating the market), and testing the predictions of said model.

This is an individual workshop. In this session, we will continue practicing data management for large-scale financial data. We will also learn about the logistic regression model and get an introduction to machine learning. We will work with a dataset that contains historical data from Q1 2010 to Q4 2023. The key topics covered in this workshop include: Winsorization: What it is and why it’s important Estimating and interpreting a logistic regression Training vs. testing samples Confusion matrix creation and interpretation

In this workshop, you will learn the fundamentals of machine learning using R and the popular caret package. We will cover key concepts such as supervised learning, model training, and data preprocessing, with a hands-on focus on splitting datasets, training models, and evaluating their performance.

Solution for analyzing quarterly financial statements of all US public firms listed on the NYSE and NASDAQ, using the library "dplyr." The analysis introduces logistic regression and machine learning techniques to explore the financial data and predict key outcomes.

We will work with a panel dataset of real US public firms. You will analyze historical quarterly financial statements of all US public firms listed on the New York Stock Exchange (NYSE) and NASDAQ. We introduce concepts of logistic regression and Machine learning.

Throughout the workshop, you’ll begin working with a large panel dataset of historical financial statement variables for many firms. This dataset will serve as the foundation for many exercises in the course, giving you ample opportunities to apply the techniques and tools you learn.