Modelling Cryptocurrency in the Philippines

Abstract

This study sought to generate predictive models for the leading cryptocurrencies in the Philippines. Autoregressive Distributed Lag (ARDL) and Artificial Neural Networks (ANN) underpinned these forecasting models for XRP, Bitcoin (BTC), and Ethereum (ETH). The study also examined how market capitalization, trading volume, exchange rate, and volatility affect pricing. This quantitative study used historical data from 2018 to 2023 to forecast these three cryptos. Findings revealed that BTC, ETH, and XRP prices fluctuated over the past five years. Market capitalization, exchange rate, and volatility also affected BTC, ETH, and XRP prices. The forecasting models, based on historical data, exhibited prediction accuracy rates of 95.49%, 92.92%, and 93.73% for the ARDL model, and 93.18%, 90.18%, and 91.25% for the ANN model, as measured by mean absolute percentage error. The study concluded that ARDL predicts Philippine cryptocurrency prices better than ANN. By using algorithmic trading strategies, such as those grounded into mathematical models, is recommended to interfere in the economic aspect of sustainable development.