Enhanced One-Day-Ahead AUD/USD Price Exchange Rate Prediction using CatBoost Model

Authors

  • Syed Ahmad Bin Syed Alwee Motorola Solutions (M) Sdn Bhd, Bayan Lepas, Penang, Malaysia
  • Farah Shahnaz Feroz Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM)
  • Minli Chai Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM)
  • Farhannah Shahfinaz Binti Feroz Bahagian Pengurusan Sumber Manusia, Jabatan Ketua Menteri Melaka.
  • Mohd Fauzi Ab Rahman Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM)

Abstract

The foreign exchange (Forex) market is highly liquid and volatile, making accurate short-term forecasting both critical and challenging. This study investigates one-day-ahead AUD/USD exchange rate prediction using CatBoost, Random Forest (RF), and Support Vector Machine (SVM) machine learning (ML) models with continuous and discretized technical indicators. Ten technical indicators were derived from 5,027 historical data points. This is the first study to apply discrete technical indicators with CatBoost for recent AUD/USD price forecasting. Results showed that CatBoost achieved the highest accuracy (89.68%) and AUC (0.9609) on the discretised dataset. Statistical test confirmed the significance of CatBoost’s superior performance, highlighting its potential to enhance predictive performance and support real-time decision-making in Forex trading.

Keywords— AUD/USD exchange rate; price prediction; CatBoost; one-day-ahead forecasting; machine learning

Additional Files

Published

2026-05-16

Issue

Vol. 72 No. 2 (2026)

Section

Applied Informatics

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