Sun Woong Kim

Papers

Total Citations

H-Index

About

Sun Woong Kim is a researcher at the intersection of machine learning and financial forecasting, with a primary focus on applying advanced computational models to options pricing and market prediction. His most cited work, "VKOSPI Forecasting and Option Trading Application Using SVM" (2016), demonstrates his expertise in using Support Vector Machines—a powerful supervised learning model for classification and regression—to forecast the VKOSPI, Korea’s volatility index. This research bridges theoretical machine learning with practical financial applications, offering a data-driven approach to option trading strategies. By leveraging large datasets and SVM’s ability to classify new data points based on existing patterns, Kim’s work provides a framework for improving prediction accuracy in volatile markets. While his citation count is modest, his contribution lies in pioneering the integration of machine learning into Korean financial derivatives, a field that has since gained significant traction. Kim’s research is particularly valuable for students and practitioners interested in quantitative finance, algorithmic trading, and the real-world application of AI to economic decision-making.