# Stock price prediction using BERT and GAN

arXiv: https://arxiv.org/abs/2107.09055 · HTML: https://mlsanity.com/paper/2107.09055/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 18 Jul 2021.

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

- [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)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [Unitary Evolution Recurrent Neural Networks](https://mlsanity.com/paper/1511.06464/) (2015)
- [Deep learning for Stock Market Prediction](https://mlsanity.com/paper/2004.01497/) (2020)
- [TI-Capsule: Capsule Network for Stock Exchange Prediction](https://mlsanity.com/paper/2102.07718/) (2021)

## Built on by (most cited first, 11 of 11)

- [PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin](https://mlsanity.com/paper/2206.00648/) (2022)
- [Transformers versus LSTMs for electronic trading](https://mlsanity.com/paper/2309.11400/) (2023)
- [DeFine: Decision-Making with Analogical Reasoning over Factor Profiles](https://mlsanity.com/paper/2410.01772/) (2024)
- [Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network](https://mlsanity.com/paper/2302.14164/) (2023)
- [Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey](https://mlsanity.com/paper/2308.04947/) (2023)
- [StonkBERT: Can Language Models Predict Medium-Run Stock Price Movements?](https://mlsanity.com/paper/2202.02268/) (2022)
- [Stock and market index prediction using Informer network](https://mlsanity.com/paper/2305.14382/) (2023)
- [ESAN: Efficient Sentiment Analysis Network of A-Shares Research Reports for Stock Price Prediction](https://mlsanity.com/paper/2112.11444/) (2021)
- [Stock Price Responses to Firm-Level News in Supply Chain Networks](https://mlsanity.com/paper/2409.06255/) (2024)
- [Encoder Decoder Generative Adversarial Network Model for Stock Market Prediction](https://mlsanity.com/paper/2510.10617/) (2025)
- [Beyond Sequential Prediction: Learning Financial Market Dynamics in Volatile and Non-Stationary Environments through Sentiment-Conditioned Generative Modelling](https://mlsanity.com/paper/2604.22801/) (2026)

## Abstract (arXiv)

The stock market has been a popular topic of interest in the recent past. The growth in the inflation rate has compelled people to invest in the stock and commodity markets and other areas rather than saving. Further, the ability of Deep Learning models to make predictions on the time series data has been proven time and again. Technical analysis on the stock market with the help of technical indicators has been the most common practice among traders and investors. One more aspect is the sentiment analysis - the emotion of the investors that shows the willingness to invest. A variety of techniques have been used by people around the globe involving basic Machine Learning and Neural Networks. Ranging from the basic linear regression to the advanced neural networks people have experimented with all possible techniques to predict the stock market. It's evident from recent events how news and headlines affect the stock markets and cryptocurrencies. This paper proposes an ensemble of state-of-the-art methods for predicting stock prices. Firstly sentiment analysis of the news and the headlines for the company Apple Inc, listed on the NASDAQ is performed using a version of BERT, which is a pre-trained transformer model by Google for Natural Language Processing (NLP). Afterward, a Generative Adversarial Network (GAN) predicts the stock price for Apple Inc using the technical indicators, stock indexes of various countries, some commodities, and historical prices along with the sentiment scores. Comparison is done with baseline models like - Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), vanilla GAN, and Auto-Regressive Integrated Moving Average (ARIMA) model.