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Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility

Zeyd Boukhers, Azeddine Bouabdallah, Cong Yang, Jan Jürjens

TL;DR

This work proposes CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data, thereby validating the importance of multimodal combination over exclusive reliance on trading data.

Abstract

Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. This study examines the various independent factors that affect the volatility of the Bitcoin-Dollar exchange rate. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.

Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility

TL;DR

This work proposes CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data, thereby validating the importance of multimodal combination over exclusive reliance on trading data.

Abstract

Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. This study examines the various independent factors that affect the volatility of the Bitcoin-Dollar exchange rate. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.
Paper Structure (29 sections, 2 equations, 11 figures, 4 tables)

This paper contains 29 sections, 2 equations, 11 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Traditional Market Price Forecasting
  4. Machine Learning for Cryptocurrency Price Forecasting
  5. Sentiment Analysis and Multimodality for Cryptocurrency Price Forecasting
  6. Approach: CoMForE
  7. Input modalities
  8. Trading data:
  9. Public awarness:
  10. Blockchain details:
  11. Search volumes
  12. Fluctuation Analysis
  13. Input varieties:
  14. Model dropouts:
  15. Predicted Price Distribution:
  16. ...and 14 more sections

Figures (11)

  • Figure 1: Overview of CoMForE's Architecture for Cryptocurrency Price Prediction
  • Figure 2: Data split for training, validation, and testing (70% training, 15% validation, 15% testing)
  • Figure 3: Visualization of "Bitcoin" search volume from 2013 to 2021.
  • Figure 4: LSTM and CoMForE forecasting results with blockchain data over 100-day interval.
  • Figure 5: CoMForE and LSTM outcomes across various modality combinations over 100-day interval.
  • ...and 6 more figures