Forecasting the movements of Bitcoin prices: an application of machine learning algorithms

Paper: arXiv 2303.04642

Authors: Hakan Pabuccu, Serdar Ongan, Ayse Ongan

Abstract

Cryptocurrencies, such as Bitcoin, are one of the most controversial and complex technological innovations in today’s financial system. This study aims to forecast the movements of Bitcoin prices at a high degree of accuracy. To this aim, four different Machine Learning (ML) algorithms are applied, namely, the Support Vector Machines (SVM), the Artificial Neural Network (ANN), the Naive Bayes (NB) and the Random Forest (RF) besides the logistic regression (LR) as a benchmark model. In order to test these algorithms, besides existing continuous dataset, discrete dataset was also created and used. For the evaluations of algorithm performances, the F statistic, accuracy statistic, the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the Root Absolute Error (RAE) metrics were used. The t test was used to compare the performances of the SVM, ANN, NB and RF with the performance of the LR. Empirical findings reveal that, while the RF has the highest forecasting performance in the continuous dataset, the NB has the lowest. On the other hand, while the ANN has the highest and the NB the lowest performance in the discrete dataset. Furthermore, the discrete dataset improves the overall forecasting performance in all algorithms (models) estimated.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 4.5/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper applies standard off-the-shelf machine learning classifiers (SVM, ANN, Random Forest, Naive Bayes) to Bitcoin price forecasting using basic error metrics and t-tests. It lacks significant mathematical innovation or rigorous financial backtesting (such as accounting for transaction costs or market impact), offering limited novelty to the existing literature.

Research Flowchart

  flowchart TD
    A[Goal: Forecast Bitcoin Price Movements] --> B[Data Inputs: Continuous & Discrete Datasets]
    B --> C[ML Model Training: SVM, ANN, NB, RF & LR Benchmark]
    C --> D[Performance Evaluation: F-stat, Accuracy, MAE, RMSE, RAE, t-test]
    D --> E[Outcome: RF Best in Continuous Data, ANN Best in Discrete Data]
    D --> F[Key Finding: Discrete Dataset Encoding Improves All Models]