Neural network foreign exchange trading system using CCS-IRS basis: Empirical evidence from Korea

•Developed a Neural Network foreign exchange trading system using CCS-IRS basis.•The NN foreign exchange trading model outperforms XGBoost and LR model in USDKRW.•The NN trading model outperforms a buy-n-hold and sell-n-hold strategies in USDKRW.•The CCS-IRS basis replaces the CDS premium as a new leading indicator in USDKRW. This study aims to develop a foreign exchange trading model with a new leading indicator based on cross-currency swap (CCS) and interest rate swap (IRS) rates. We derive the correlation dimension from the chaos analysis, use Neural Networks (NNs) to optimize foreign exchange trading, and then compare the performance of the NNs model with XGBoost and Logistic Regression model (LR). The CCS-IRS basis is the spread between the cross-currency swap (CCS) rate and interest rate swap (IRS) rate and is often used as an indicator for foreign exchange soundness along with the CDS premium in Korea. Based on the phenomenon in which the USDKRW exchange rate rises (falls) when the CCS-IRS basis widens (tightens), this study proposes to use the CCS-IRS basis to build a foreign exchange trading model of the USDKRW exchange rate. The experimental results show that our NNs trading model of the USDKRW exchange rate outperforms XGBoost and LR model, generating an annual return of 4.888%∼8.464%, whereas the buy-and-hold and sell-and-hold strategies in the USDKRW exchange rate market produces an annual return of 2.2% and −1.15%, respectively. The foreign exchange trading model established in this paper not only enables foreign exchange traders to obtain sustainable economic profits, but also contributes to the efficiency of the financial market, thereby contributing to sustainable economic growth.