E. Mejuto-Villa

Papers

Total Citations

H-Index

About

E. Mejuto-Villa is a researcher at the intersection of artificial intelligence and quantitative finance, whose work focuses on the application of deep learning to financial market prediction and trading strategy automation. Their most cited paper, "Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications" (2018, 92 citations), is a landmark study demonstrating how Long Short-Term Memory (LSTM) neural networks can learn complex trading rules by modeling the relationship between market indicators and human decision-making. This work has been foundational for researchers exploring the use of recurrent neural networks in algorithmic trading, bridging the gap between machine learning theory and practical financial applications. Mejuto-Villa's contributions are particularly notable for their emphasis on replicating human expertise through artificial intelligence, rather than simply optimizing for historical returns. Their research has significant implications for the financial technology sector, offering a pathway toward more adaptive and intelligent trading systems. With a growing citation footprint, Mejuto-Villa continues to influence the development of AI-driven financial tools, making their work essential reading for students and researchers interested in the practical deployment of deep learning in high-stakes financial environments.