Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis (2006.14473v1)

Abstract: Bitcoin is the first digital decentralized cryptocurrency that has shown a significant increase in market capitalization in recent years. The objective of this paper is to determine the predictable price direction of Bitcoin in USD by machine learning techniques and sentiment analysis. Twitter and Reddit have attracted a great deal of attention from researchers to study public sentiment. We have applied sentiment analysis and supervised machine learning principles to the extracted tweets from Twitter and Reddit posts, and we analyze the correlation between bitcoin price movements and sentiments in tweets. We explored several algorithms of machine learning using supervised learning to develop a prediction model and provide informative analysis of future market prices. Due to the difficulty of evaluating the exact nature of a Time Series(ARIMA) model, it is often very difficult to produce appropriate forecasts. Then we continue to implement Recurrent Neural Networks (RNN) with long short-term memory cells (LSTM). Thus, we analyzed the time series model prediction of bitcoin prices with greater efficiency using long short-term memory (LSTM) techniques and compared the predictability of bitcoin price and sentiment analysis of bitcoin tweets to the standard method (ARIMA). The RMSE (Root-mean-square error) of LSTM are 198.448 (single feature) and 197.515 (multi-feature) whereas the ARIMA model RMSE is 209.263 which shows that LSTM with multi feature shows the more accurate result.

Real-Time Prediction of Bitcoin Price using Machine Learning Techniques and Public Sentiment Analysis

The paper explores the feasibility of applying ML techniques and sentiment analysis for predicting Bitcoin, a highly traded and volatile digital cryptocurrency. The focus lies in predicting Bitcoin's price movements using Long Short-Term Memory (LSTM) networks in comparison with traditional Autoregressive Integrated Moving Average (ARIMA) models, while integrating sentiment analysis derived from social media platforms like Twitter and Reddit.

Methodology Overview

The research employs a data-driven approach encompassing two primary data sources: historical Bitcoin price data from prominent platforms such as Coinmarketcap, Bitstamp, and Coinbase, and sentiment data extracted from tweets and Reddit posts. Sentiment analysis classifies tweets as positive, negative, or neutral based on their polarity, leveraging tools such as Textblob and Haven OnDemand for automated sentiment scoring.

A significant component of the research focuses on the application of LSTM networks, a form of Recurrent Neural Network (RNN), renowned for handling sequential data and managing long-term dependencies in time series data. This approach is juxtaposed with the ARIMA model, which is widely deployed for traditional time series forecasting involving trends and seasonality.

Key Findings

The results reveal that multi-feature LSTM models outperform ARIMA in Bitcoin price prediction, as evidenced by lower RMSE scores. LSTM achieved RMSE values of 198.448 (single feature) and 197.515 (multi-feature), outperforming ARIMA's RMSE of 209.263. This marked improvement suggests the efficacy of LSTM in capturing the complex dynamics and volatile nature of Bitcoin market prices.

Moreover, the integration of sentiment analysis provides additional explanatory power, correlatively enhancing the predictive accuracy of Bitcoin price movements. While the paper highlights the potential time lag effect of social media sentiments on market trends, it affirms the importance of public sentiment as a non-quantitative predictor of Bitcoin’s valuation.

Practical and Theoretical Implications

From a practical standpoint, this research underscores the potential for financial market entities, individual investors, and automated trading platforms to utilize advanced ML techniques, specifically LSTM networks, for real-time cryptocurrency evaluation. The incorporation of sentiment data offers a media-informed perspective that complements quantitative analyses, simultaneously broadening strategic trading approaches.

Theoretically, this paper reinforces the advantages of LSTM networks over traditional statistical methods in scenarios characterized by high volatility and non-linearity, such as cryptocurrency markets. It invites further exploration into hybrid models combining multilayer perceptron, NARX, and other neural networks to offer robust price prediction strategies.

Future Directions

While this paper delivers valuable insights, it points towards the necessity of incorporating diversified sentiment sources like Facebook and LinkedIn, offering a more comprehensive measure of public sentiment. Future research could also explore refining sentiment analysis methodologies to capture nuanced investor behaviors more accurately. Comparing LSTM with other emerging ML algorithms could unveil alternative strategies and augment predictive proficiency across different time scales.

The implications of this research extend to broader applications in financial forecasting and risk management within the rapidly evolving digital asset markets. Integrating machine learning with behavioral finance presents lucrative avenues not only for enhancing predictive models but also for cultivating adaptive and responsive trading infrastructures.