Vuong Dang Xuan

About

Dr. Vuong Dang Xuan is a researcher at the forefront of applying machine learning to financial forecasting, with a specialized focus on foreign exchange (Forex) trading. His primary research area centers on the use of supervised learning algorithms—most notably the Support Vector Machine (SVM)—to model and predict currency market trends. Dr. Xuan’s major contribution lies in demonstrating that SVM classifiers can effectively transform complex, high-frequency Forex data into actionable binary predictions, distinguishing between uptrend and downtrend movements. His foundational work, "Using support vector machine in FoRex predicting" (2018), has garnered 21 citations, establishing a benchmark for subsequent studies in computational finance. A follow-up paper, "Supervised Support Vector Machine in Predicting Foreign Exchange Trading" (2018), with 7 citations, further refines this methodology by framing currency rate shifts as a binary classification problem. Together, these publications underscore his impact in bridging machine learning theory with practical trading systems. Dr. Xuan’s research offers a compelling entry point for students and researchers interested in how AI can decode the volatility of global financial markets.

Research Focus

Key Achievements

Top Papers

1. 1
   Using support vector machine in FoRex predicting

   21 citations · 2018
2. 2
   Supervised Support Vector Machine in Predicting Foreign Exchange Trading

   7 citations · 2018

Key Collaborators

Contact & Links