A comparative analysis on the predictive performance of LSTM and SVR on Bitcoin closing prices.

Abstract: Bitcoin has since its inception in 2009 seen its market capitalisation rise to a staggering 846 billion US Dollars making it the world’s leading cryptocurrency. This has attracted financial analysts as well as researchers to experiment with different models with the aim of developing one capable of predicting Bitcoin closing prices. The aim of this thesis was to examine how well the LSTM and the SVR models performed in predicting Bitcoin closing prices. As a measure of performance, the RMSE, NRMSE and MAPE were used as well as the Random walk without drift as a benchmark to further contextualise the performance of both models. The empirical results show that the Random walk without drift yielded the best results for both the RMSE and NRMSE scoring 1624.638 and 0.02525, respectively while the LSTM outperformed both the Random Walk without drift and the SVR model in terms of the MAPE scoring 0.0272 against 0.0274 for both the Random walk without drift and SVR, respectively. Given the performance of the Random Walk against both models, it cannot be inferred that the LSTM and SVR models yielded statistically significant predictions.