Reseña

These methods involve summarizing and describing the key features of financial data. Common descriptive statistics include measures of central tendency (mean, median), dispersion (variance, standard deviation), skewness, kurtosis, and correlation coefficients. Descriptive analysis helps understand the distribution, trends, and relationships in financial time-series data, asset returns, risk measures, and other financial metrics. These methods form the foundation for more advanced modeling by providing initial insights into data behavior and characteristics.
Machine learning (ML) methods go beyond traditional statistics by enabling automated pattern recognition, prediction, and classification from complex, high-dimensional financial datasets. ML approaches used in finance include supervised learning (such as regression, classification, support vector machines, random forests, neural networks), unsupervised learning (such as clustering, dimensionality reduction), and reinforcement learning. These methods are used for tasks like asset price prediction, portfolio optimization, fraud detection, credit scoring, and algorithmic trading. ML models can identify nonlinear relationships and interactions in financial data that traditional descriptive statistics might miss.