Abstract
In recent years, cryptocurrencies have gained significant traction as potential investment assets, sparking debates among investors and scholars regarding their financial viability. While academia remains skeptical about the intrinsic value of cryptocurrencies—citing concerns over inflated prices and potential bubbles—the gradual adoption of Bitcoin as a payment method suggests its possible future role as an alternative to gold or fiat currencies.
Given the growing importance of cryptocurrencies, this study employs deep learning techniques to predict Bitcoin price trends. We compare the accuracy of these advanced methods against traditional econometric models, identifying superior forecasting tools to aid investors in decision-making.
Research Motivation and Objectives
Bitcoin's creator, Satoshi Nakamoto, capped its supply at 21 million coins to prevent inflationary risks—a feature that appeals to nations facing hyperinflation (e.g., Venezuela, where citizens turned to Bitcoin during a 1.7 million% inflation crisis in 2018). However, Bitcoin's extreme volatility complicates price prediction. This study leverages time-series models and deep neural networks to develop reliable forecasting techniques.
Methodology and Framework
We model Bitcoin price (𝑦𝑡) as a function of:
- Historical values (𝑦𝑡−1,...,𝑦𝑡−𝑝)
Financial variables (𝑥1𝑡,...,𝑥𝑘𝑡), including:
- Dow Jones Industrial Average
- VIX Fear Index
- Gold prices
- WTI crude oil prices
Key Steps:
- Function Definition: Determine input variables, neurons per layer, and hidden layers.
- Loss Function: Use Mean Squared Error (MSE).
- Model Optimization: Split data into training/testing sets; apply backpropagation.
- Performance Evaluation: Compare predicted vs. actual values using RMSE and MAPE.
Results and Analysis
Data Scope: July 21, 2010 – August 5, 2022
Tools: Eviews 9.5, TensorFlow
Variables: WTI crude oil, VIX, Dow Jones, gold prices (selected based on prior research).
Model Comparison:
| Model | RMSE | MAPE |
|-------------|-------|---------|
| ARIMA | 0.05 | - |
| LSTM | - | 0.07% |
| AR | 0.05 | - |
Findings:
- ARIMA/AR models showed the lowest RMSE (0.05).
- LSTM achieved the best MAPE (0.07%).
Conclusion
Bitcoin exhibits negative correlations with gold, crude oil, and the VIX—highlighting its diversification potential. For investors seeking hedging options, these insights provide critical guidance.
👉 Explore advanced crypto trading strategies
FAQs
Q1: Why use deep learning for Bitcoin prediction?
A1: Deep learning captures complex, non-linear patterns in volatile assets like Bitcoin, outperforming traditional models in certain scenarios.
Q2: Which financial variables most impact Bitcoin?
A2: Gold, crude oil, and fear indices show significant correlations, reflecting macroeconomic influences.
Q3: How reliable are these predictions?
A3: While LSTM/ARIMA models show promise, investors should combine them with fundamental analysis for robust decision-making.