Reconstructing Algorithmic Trading in the Age of Generative AI: Implications for Market Structure, Regulation, and Epistemology
Reconstructing Algorithmic Trading in the Age of Generative AI: Implications for Market Structure, Regulation, and Epistemology
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El propósito de este artículo es analizar cómo la Inteligencia Artificial Generativa (Gen AI), definida aquí como sistemas capaces de producir resultados novedosos, como texto, código o datos, mediante aprendizaje automático, está transformando el trading algorítmico. Gen AI mejora la eficiencia computacional, pero también reconfigura los fundamentos epistemológicos (teorías del conocimiento), institucionales (estructuras y actores de mercado) y regulatorios (normas y supervisión) de los mercados financieros. Sostenemos que Gen AI marca un cambio desde el precio entendido como representación de los fundamentos económicos hacia el precio como resultado recursivo de la simulación entre máquinas. Basándonos en aprendizajes del machine learning financiero, la teoría legal crítica, la economía política y la sociología de las finanzas, estudiamos cómo los modelos generativos, como las arquitecturas tipo transformer (modelos de deep learning desarrollados inicialmente para el procesamiento de lenguaje natural) y los motores de datos sintéticos (herramientas que generan conjuntos de datos artificiales) reconfiguran la lógica de la formación de precios, la provisión de liquidez y la gestión del riesgo. Nuestro análisis muestra que Gen AI intensifica la abstracción del mercado, debilita los supuestos regulatorios tradicionales y consolida el poder informativo en una élite reducida de actores tecnológicamente sofisticados. Además, arroja luz sobre la opacidad epistémica, los bucles de retroalimentación y la reflexividad algorítmica. Estas características comprometen tanto la inteligibilidad del mercado como la legitimidad regulatoria. También prestamos especial atención a cómo estas dinámicas agravan las desigualdades globales, lo cual ocurre a través de asimetrías de infraestructura y la importación de marcos regulatorios que marginan al sur global. Concluimos instando a la implementación de nuevas regulaciones que prioricen la claridad, la rendición de cuentas y el control digital. Sugerimos reformas, como registros públicos de modelos, auditorías participativas y una infraestructura algorítmica abierta para restaurar la supervisión democrática en mercados cada vez más gobernados por el código. Nuestro objetivo es reposicionar el trading algorítmico como un espacio central para el debate sobre el valor, la gobernanza y la realidad económica en las finanzas sintéticas.
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