# FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics

arXiv: https://arxiv.org/abs/2411.12748 · HTML: https://mlsanity.com/paper/2411.12748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 2 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis](https://mlsanity.com/paper/2407.16150/) (2024)
- [Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series](https://mlsanity.com/paper/2009.12325/) (2020)
- [LARNN: Linear Attention Recurrent Neural Network](https://mlsanity.com/paper/1808.05578/) (2018)
- [FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis](https://mlsanity.com/paper/2211.07392/) (2022)
- [Financial sentiment analysis using FinBERT with application in predicting stock movement](https://mlsanity.com/paper/2306.02136/) (2023)
- [Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond](https://mlsanity.com/paper/2403.12161/) (2024)

## Built on by (most cited first, 1 of 1)

- [TopicProphet: Prophesies on Temporal Topic Trends and Stocks](https://mlsanity.com/paper/2512.11857/) (2025)

## Abstract (arXiv)

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.