DINÁMICA FINANCIERA Y SERIES DE TIEMPO: UN ESTUDIO BIBLIOMÉTRICO Y SISTEMÁTICO DE LA PRODUCCIÓN ACADÉMICA
DOI:
https://doi.org/10.56238/arev7n8-123Palabras clave:
Análisis Bibliométrico, Finanzas Corporativas, Series Temporales, Modelos Híbridos, Inteligencia ArtificialResumen
Este artículo presenta un estudio bibliométrico y sistemático sobre la aplicación de modelos de series temporales en el análisis de las finanzas corporativas durante el periodo 2010–2025. A través del examen de 84 artículos científicos indexados en bases de datos académicas, se identifican y sintetizan cinco ejes temáticos principales: evolución teórica de los modelos, enfoques metodológicos, áreas de aplicación, tendencias emergentes y la integración de inteligencia artificial. Los resultados evidencian un tránsito desde los modelos clásicos univariantes, como ARIMA y SARIMA, hacia arquitecturas híbridas más robustas y complejas, tales como LSTM-ARFIMA, que combinan el aprendizaje profundo con técnicas econométricas tradicionales. Estas metodologías se aplican en diversos contextos, incluyendo la predicción de quiebras, la gestión del riesgo financiero, el diseño de políticas públicas, y la evaluación del desempeño de empresas. Además, se observa una creciente relevancia de la inteligencia artificial y el machine learning como herramientas complementarias para abordar problemas financieros con altos niveles de volatilidad y no linealidad. Este panorama metodológico y temático permite identificar lagunas de investigación y posibles sinergias entre disciplinas afines. El estudio concluye destacando la necesidad de desarrollar modelos más integrados y adaptativos, que permitan mejorar la precisión en las proyecciones financieras y fortalecer la toma de decisiones estratégicas en entornos dinámicos e inciertos. Las implicaciones teóricas y prácticas del análisis resultan relevantes tanto para académicos como para profesionales del área financiera, interesados en metodologías avanzadas para la comprensión de las dinámicas corporativas actuales.
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