Enhancing Stock Trend Prediction Using BERT-Based Sentiment Analysis and Machine Learning Techniques

Aug 3, 2026

Predicting stock trends with accuracy in fast-changing financial markets remains challenging. This research proposes a hybrid approach that combines BERT for sentiment classification with supervised machine learning to improve stock trend prediction. Sentiment features are extracted from financial news using BERT, then aggregated daily (including daily net sentiment) and merged with traditional financial indicators. The resulting predictive model establishes a quantitative relationship and demonstrates statistically significant forecasting power, improving state-of-the-art performance by 15 percentage points.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

Introduction

Motivation and challenge in stock prediction

Role of sentiment analysis and BERT

Proposed fusion of BERT sentiment with supervised learning

Benchmark with bag-of-words baseline

Related work on stock price forecasting models

Available online at [https://journal.rescollacomm.com/index.php/ijqrm/index](https://journal.rescollacomm.com/index.php/ijqrm/index) International Journal of Quantitative Research and Modeling Vol. 5, No. 1, pp. 1-11, 2024 e-ISSN 2721-477Xp-ISSN 2722-5046 Enhancing Stock Trend Prediction Using BERT-Based Sentiment Analysis and Machine Learning Techniques Nikesh Yadav 1* 1AVP at Sovrenn Research Lab (SRL), R&D division of Sovrenn, India *Corresponding author email: [nikesh.yadav@sovrenn.com](nikesh.yadav@sovrenn.com) Abstract Predicting stock trends with precision in the ever-evolving financial markets continues to be a formidable challenge. This research investigates an innovative approach that amalgamates the capabilities of BERT (Bidirectional Encoder Representations from Transformers) for sentiment classification (Pang et al., 2002; ?) with supervised machine learning techniques to elevate the accuracy of stock trend prediction. By harnessing the natural language processing process of BERT and its capacity to understand context and sentiment in textual data, coupled with established machine learning methodologies, we aim to provide a robust solution to the intricacies of stock market prediction. By leveraging BERT's natural language processing capabilities, we extract sentiment features from financial news articles. These sentiment scores, combined with traditional financial indicators, form a comprehensive set of features for our predictive model. We aggregate daily net sentiment, among other metrics, and demonstrate its statistically significant predictive efficacy concerning subsequent movements in the stock market. We employed a machine learning model to establish a quantitative relationship between the aggregation of daily net sentiment and trends in stock market movements. Which improved the state-of-the-art performance by 15 percentage points. This research contributes to the ongoing effort to improve stock trend prediction methods, ultimately aiding market participants in making informed investment choices. Keywords: Stock Price Prediction, Sentiment Analysis, BERT Model. 1. Introduction In the ever-evolving landscape of financial markets, the ability to make informed decisions about stock investments is a paramount challenge. Investors and traders continuously seek methods to gain a competitive edge in predicting stock price movements. In this pursuit, advances in natural language processing (NLP) and machine learning have provided new avenues for extracting valuable insights from textual data sources, such as financial news articles, earnings reports, and social media posts. Sentiment analysis, a branch of NLP, has emerged as a powerful tool for gauging market sentiment and investor emotions by analyzing and classifying the sentiment or emotional tone expressed in textual data. Within this context, the Bidirectional Encoder Representations from Transformers (BERT) model has garnered significant attention. BERT, known for its ability to capture context and semantics in text, has demonstrated remarkable performance in various NLP tasks, including sentiment analysis. The primary objective of this article is to delve into an innovative fusion of BERT (Bidirectional Encoder Representations from Transformers) with supervised machine learning algorithms in the context of stock price prediction. Our exploration begins by contrasting this novel approach with a more conventional and simplistic method, which entails using a bag-of-words (Joachims, 1998) representation in conjunction with a supervised machine learning algorithm to forecast stock prices. This initial approach serves as our benchmark, providing a foundation against which we can measure the improvements brought about by integrating BERT-based sentiment analysis. Several methods for stock price prediction have been proposed recently. (Shah et al., 2022) introduced a model for predicting the closing price of the Indian Nifty 50 st

FAQs

How does the method use BERT in stock trend prediction?

BERT is applied to financial news to perform sentiment classification and generate context-aware sentiment features used by the predictive model.

What features are used to train the stock prediction model?

The model uses BERT-derived sentiment scores aggregated daily (e.g., daily net sentiment) along with traditional financial indicators.

How does the proposed approach compare with a simpler baseline?

The study benchmarks against a bag-of-words representation with supervised machine learning, and the BERT-based sentiment integration improves performance by 15 percentage points.

Aug 3, 2026

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