Adaptive trading system integrating machine learning and back-testing: Korean bond market case

•We proposed a novel adaptive trading system for the bond market.•This system integrates machine learning and back-testing.•The 10–3-year Korean treasury bond spread was predicted.•We defined the trading strategy and verified the profit generated by this strategy.•Our results can serve as a data-driven decision-making support system in real world. Although the bond market provides a basis for determining the capital cost of a corporation by forming the fair value of issued bonds, studies regarding the financial market has mainly focused on the stock market. Because the bond market is affected by several variables, it is a good candidate for machine learning applications. Specifically, traders create trading strategies that involves the difference between long- and short-term bond yields to minimize market risks; hence, if this spread can be predicted, it can serve as the data-driven long-term direction of the bond market and generate additional profits. Therefore, a prediction model that predicts the spreads between 10- and 3-year treasury bonds is proposed herein; subsequently, back-testing is applied to verify the performance of the prediction model. Consequently, the AdaBoost outperformed other prediction models. Moreover, when back-testing was applied based on the results of predictive models, we achieved up to 54.2% in return on investment over 6-month. This study establishes a novel adaptive trading system that integrates machine learning and back-testing for the bond market. In the future, this study will be extended using complex data or the reflection of real constraints based on its use as initial research in the bond market.