Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data

Paper: arXiv 2311.14759

Abstract

We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is made available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper is highly empirical with a public codebase, extensive backtesting, and multiple datasets, while using advanced but established deep learning models rather than novel mathematical derivations.

Research Flowchart

  flowchart TD
  A["Research Goal: Forecast Crypto Prices"] --> B["Data Acquisition & Preparation"]
  B --> C["Sentiment Analysis with NLP"]
  B --> D["Price Target Definition"]
  C --> E["Model Training & Integration"]
  D --> E
  E --> F["Model Evaluation & Trading Simulation"]
  F --> G{"Key Findings"}
  G --> H["NLP Integration Improves Accuracy"]
  G --> I["DL Models Outperform Baselines"]
  G --> J["Targets Reduce Volatility & Boost Profitability"]

  subgraph "Data Sources"
      B1["Financial Data"]
      B2["Blockchain Data"]
      B3["Social Media Text"]
  end

  subgraph "Key Methodologies"
      C1["BART MNLI Zero-Shot"]
      C2["Fine-tuned DL Models"]
      C3["Dictionary Methods"]
      D1["Daily Price Moves"]
      D2["Local Extrema"]
  end

  B --> B1
  B --> B2
  B --> B3
  C --> C1
  C --> C2
  C --> C3
  D --> D1
  D --> D2

Cited References (78)

1. Attention Is All You Need (2017) — link
2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018) — link
3. Distributed Representations of Words and Phrases and their Compositionality (2013) — link
4. Language Models are Few-Shot Learners (2020) — link
5. Deep Learning (2015) — link
6. Neural Machine Translation by Jointly Learning to Align and Translate (2014) — link
7. RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019) — link
8. Language Models are Unsupervised Multitask Learners (2018) — link

0 of 78 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.