Forecasting foreign exchange and trading using random forest

• Main Author: Song, Jiaze
• Other Authors: Wang Lipo
• Format: Final Year Project
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/77583
• Institution: Nanyang Technological University

The main objective of the project is to predict the future foreign exchange rate using a machine learning algorithm called random forests. Nowadays, machine learning algorithms has been widely applied in many fields. In financial industry, forecasting the price of financial products is always a hot topic. Due to the crucial role foreign exchange rate plays nowadays, constructing a prediction method with high accuracy can be a significant problem for the economists, analysts and speculators to understand the foreign exchange market better and avoid financial instability. With machine learning advances by leaps and bounds, there are many acknowledged prediction models have been invented, which all can lead to a reliable prediction result. For this project, we mainly focus on the random forest model to predict the future foreign exchange rate.