A Risk-Free Discount Rate Prediction Model for Mineral Project Evaluation Using a Hybrid Discrete Wavelet Transform and Artificial Neural Network

Abstract

The discount rate input parameter of Net Present Value (NPV) in mineral project evaluation is a function of a risk-free rate and risk premium component. To obtain a reliable NPV, it is important to estimate each of these components. This study employs a hybrid approach to predict risk-free rate using Discrete Wavelet Transform and Artificial Neural Network (DWT-ANN). The DWT-ANN model was tested using London Interbank Offered Rate (LIBOR) dataset from 1986 to 2020. The results showed that Discrete Wavelet Transform-Radial Basis Function Neural Network (DWT-RBFNN) of the three different hybrid algorithms developed and applied performed best in predicting the risk-free rate. This is because it achieved the lowest root mean square error of 0.0376 and the highest correlation coefficient of 0.9995. The DWT-RBFNN model can be a useful alternative tool for predicting risk-free rate, which is a key input parameter for the determination of discount rate.