Heung Sik Choi
Papers
1
Total Citations
2
H-Index
1
About
Heung Sik Choi is a researcher whose work sits at the intersection of machine learning and quantitative finance, with a particular focus on volatility forecasting and options trading. His most cited paper, "VKOSPI Forecasting and Option Trading Application Using SVM" (2016), demonstrates his pioneering application of Support Vector Machines (SVM)—a core machine learning technique for classification and regression—to predict the VKOSPI, Korea's volatility index. By leveraging data-driven models to enable self-learning predictions, Choi showed how SVM can be used to make informed decisions in options markets, bridging the gap between artificial intelligence and financial analytics. While his citation count is modest, his contribution is notable for its early integration of machine learning into practical trading strategies, offering a framework that anticipates the growing role of AI in finance. Choi’s work serves as a valuable reference for students and researchers interested in computational finance, volatility modeling, and the application of supervised learning to real-world market data.
Research Focus
Key Achievements
Top Papers
- 1VKOSPI Forecasting and Option Trading Application Using SVM2 citations · 2016