Yun Seon

Papers

Total Citations

H-Index

About

Yun Seon is a researcher specializing in the application of machine learning to financial forecasting, with a particular focus on volatility index prediction and options trading. In their most cited work, "VKOSPI Forecasting and Option Trading Application Using SVM" (2016), Seon leverages Support Vector Machines (SVM)—a supervised learning model used for classification and regression—to analyze and predict the Korea Volatility Index (VKOSPI). This study demonstrates how machine learning can enhance financial decision-making by enabling models to autonomously learn from data and classify new data points. Although the paper has garnered 2 citations, it represents a foundational contribution to the intersection of artificial intelligence and quantitative finance. Seon’s work underscores the growing role of data-driven techniques in financial markets, where vast datasets and advanced algorithms are increasingly used to improve forecasting accuracy and trading strategies. Their research is particularly relevant for students and researchers interested in computational finance, offering a clear example of how SVM can be applied to real-world financial instruments.