Paper: arXiv 2411.12748

Abstract

Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced deep learning architectures (Bi-LSTM, FinBERT transformers) requiring knowledge of neural networks and sequence modeling. It demonstrates strong empirical rigor by using specific datasets (Bitcoin/Ethereum news and price data), comparing against multiple benchmarks, and mentioning intra-day performance analysis, suggesting thorough backtesting readiness.

Research Flowchart

  flowchart TD
  A["Research Goal: Predict Crypto Volatility"] --> B["Data Collection & Preprocessing"]
  B --> C["Market Data & News Sentiment"]
  C --> D["Computational Process: Hybrid Model"]
  subgraph D ["FinBERT-BiLSTM Architecture"]
      D1["FinBERT: Extracts Sentiment Features"]
      D2["Bi-LSTM: Captures Temporal Dependencies"]
  end
  D1 --> D2
  D2 --> E["Forecast Price & Volatility"]
  E --> F["Key Findings & Outcomes"]
  F --> G["Improved Accuracy vs. Traditional Models"]
  F --> H["Enhanced Risk Management Strategies"]

Cited References (40)

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  2. FinBERT-LSTM: Deep Learning-based Stock Price Prediction Using News Sentiment Analysis (2022)
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  6. Bitcoin: Medium of exchange or speculative assets? (2018) — link
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  8. The economics of Bitcoin and similar private digital currencies (2015) — link
and 32 more
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  2. On the return-volatility relationship in the Bitcoin market around the price crash of 2013 (2017) — link
  3. Deep learning with long short-term memory networks for financial market predictions (2018) — link
  4. Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning (2019) — link
  5. Sentiment Analysis of News for Effective Cryptocurrency Price Prediction (2019) — link
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  9. A multi-channel CNN-BiLSTM model for stock price forecasting using a deep learning method (2020) — link
  10. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models (2019)
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  24. Bidirectional recurrent neural networks (1997) — link
  25. LARNN: Linear Attention Recurrent Neural Network (2018)
  26. Financial sentiment analysis using FinBERT with application in predicting stock movement — → 2306.02136
  27. Hybrid LSTM and GRU for Cryptocurrency Price Forecasting Based on Social Network Sentiment Analysis Using FinBERT (2023) — link
  28. Stock market volatility: a systematic review (2024) — link
  29. Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm (2019) — link
  30. Predicting COVID-19 cases using bidirectional LSTM on multivariate time series (2021) — link
  31. BERT for Stock Market Sentiment Analysis (2019)
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1 of 40 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.