# Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis

arXiv: https://arxiv.org/abs/2407.16150 · HTML: https://mlsanity.com/paper/2407.16150/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://mlsanity.com/paper/1712.02136/) (2017)
- [Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate](https://mlsanity.com/paper/2402.02769/) (2024)
- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [Credit Card Fraud Detection Using Advanced Transformer Model](https://mlsanity.com/paper/2406.03733/) (2024)
- [Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective](https://mlsanity.com/paper/2312.01397/) (2023)
- [Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task](https://mlsanity.com/paper/2405.03026/) (2024)
- [Localization Through Particle Filter Powered Neural Network Estimated Monocular Camera Poses](https://mlsanity.com/paper/2404.17685/) (2024)
- [Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model](https://mlsanity.com/paper/2404.17123/) (2024)
- [Stock trend prediction using news sentiment analysis](https://mlsanity.com/paper/1607.01958/) (2016)
- [Rough Set improved Therapy-Based Metaverse Assisting System](https://mlsanity.com/paper/2406.04465/) (2024)
- [Research on Flight Accidents Prediction based Back Propagation Neural Network](https://mlsanity.com/paper/2406.13954/) (2024)

## Built on by (most cited first, 15 of 15)

- [Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach](https://mlsanity.com/paper/2408.06634/) (2024)
- [LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning](https://mlsanity.com/paper/2411.09947/) (2024)
- [Enhanced Recommendation Combining Collaborative Filtering and Large Language Models](https://mlsanity.com/paper/2412.18713/) (2024)
- [Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy](https://mlsanity.com/paper/2504.08751/) (2025)
- [User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data](https://mlsanity.com/paper/2505.06305/) (2025)
- [Enhanced Credit Score Prediction Using Ensemble Deep Learning Model](https://mlsanity.com/paper/2410.00256/) (2024)
- [Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention](https://mlsanity.com/paper/2508.13327/) (2025)
- [OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents](https://mlsanity.com/paper/2606.26350/) (2026)
- [FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics](https://mlsanity.com/paper/2411.12748/) (2024)
- [IKNet: Interpretable Stock Price Prediction via Keyword-Guided Integration of News and Technical Indicators](https://mlsanity.com/paper/2510.07661/) (2025)
- [BondBERT: What we learn when assigning sentiment in the bond market](https://mlsanity.com/paper/2511.01869/) (2025)
- [Taxonomy-Aligned Risk Extraction from 10-K Filings with Autonomous Improvement Using LLMs](https://mlsanity.com/paper/2601.15247/) (2026)
- [GroupSHAP-Guided Integration of Financial News Keywords and Technical Indicators for Stock Price Prediction](https://mlsanity.com/paper/2510.23112/) (2025)
- [FinMoji: A Framework for Emoji-driven Sentiment Analysis in Financial Social Media](https://mlsanity.com/paper/2605.09469/) (2026)
- [Bridging the Gap Between Natural Language and Market Dynamics via High-Dimensional Representation Learning](https://mlsanity.com/paper/2605.30652/) (2026)

## Abstract (arXiv)

The stock market's ascent typically mirrors the flourishing state of the economy, whereas its decline is often an indicator of an economic downturn. Therefore, for a long time, significant correlation elements for predicting trends in financial stock markets have been widely discussed, and people are becoming increasingly interested in the task of financial text mining. The inherent instability of stock prices makes them acutely responsive to fluctuations within the financial markets. In this article, we use deep learning networks, based on the history of stock prices and articles of financial, business, technical news that introduce market information to predict stock prices. We illustrate the enhancement of predictive precision by integrating weighted news categories into the forecasting model. We developed a pre-trained NLP model known as FinBERT, designed to discern the sentiments within financial texts. Subsequently, we advanced this model by incorporating the sophisticated Long Short Term Memory (LSTM) architecture, thus constructing the innovative FinBERT-LSTM model. This model utilizes news categories related to the stock market structure hierarchy, namely market, industry, and stock related news categories, combined with the stock market's stock price situation in the previous week for prediction. We selected NASDAQ-100 index stock data and trained the model on Benzinga news articles, and utilized Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Accuracy as the key metrics for the assessment and comparative analysis of the model's performance. The results indicate that FinBERT-LSTM performs the best, followed by LSTM, and DNN model ranks third in terms of effectiveness.