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Descriptive and machine learning statistical methods for finance
Authors:
Jorge Luis Ilquimiche Melly, Enio Elias Tena Jacinto, Robert William Castillo Alva, Noeding Edith Cardenas Lara, Mónica Beatriz La Chira Loli, Manuel Abelardo Alcántara Ramírez, Carlos Alberto Asián Quiñones
ISBN: In progress
ARK: In progress
DOI: In progress
«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 (e.g., regression, classification, support vector machines, random forests, neural networks), unsupervised learning (e.g., 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.»
Jorge Luis Ilquimiche Melly
Published: 31/10/2025
Location: Colonia, Colonia, Uruguay
Catalogue ISBN – Uruguay: In progress
