Predicting Currency Crises in Emerging Markets: A Case Study on South Africa Using Artificial Neural Networks

Purpose: In this paper, we study the potential of using Artificial Neural Network (ANN) models to predict currency crises in emerging markets, with a specific focus on the South African economy. South Africa’s rand is one of the most volatile currencies in the world and is prone to crises. Methodology: We built two ANN models, where Model 1 uses ten economic indicators and Model 2 uses four. These models were assessed for statistical significance (using probit analysis), and their performance in predicting major South African currency crises (e.g., 1998, 2001, and 2008) was tested with both in-sample and out-of-sample data. Results: The first model was much more accurate than Model 2 in predicting early warning signs nearly two years before the currency crises occurred. Model 1’s higher accuracy is attributed to its inclusion of a greater number of economic variables. Both models occasionally produced false positives, though overall, they were very accurate in predicting crises. Originality: Our paper highlights the importance of ANNs in capturing nonlinear patterns in economic data, demonstrating their strength as early warning tools for financial crises. We recommend that ANN methods continue to be researched and advanced to further reduce false positives and improve predictive performance.