CASE STUDY
Generative AI for Stock Market Prediction
TechnoLynx supported a portfolio management company in exploring how NLP and Generative AI can help interpret market sentiment from unstructured text, news, financial reports, and social media, to surface early signals of volatility and potential trading opportunities.
The Challenge
The client wanted to test whether NLP could improve how they understand and anticipate market volatility. They were especially interested in extracting signal from unstructured text, financial news, earnings reports, and social media, and combining those insights with traditional numerical models.
Predict volatility using non-numerical signals.
The client wanted to see whether NLP could detect subtle shifts in sentiment that might signal future movements in stock prices.
Merge news, reports, and social discussions.
They were interested in analysing news articles, financial reports, and social media discussions to help identify trading opportunities.
Handle social media noise and misinformation.
Social media can provide real-time insight, but it is also filled with misinformation and irrelevant content, so the system needed to filter less relevant posts and focus on more credible sources.
Explore advanced NLP techniques.
The client was interested not only in standard machine learning and NLP methods but also in more advanced techniques like using LLMs and transformers.
Project Timeline
From NLP task analysis to a hybrid approach combining numerical and textual-driven insights
NLP Task Analysis
Conducted an analysis of NLP tasks that could be applied to the stock market, including sentiment analysis, the confidence level of sentiment, and cross-referencing financial reports with analysts’ expectations.
Integrated data from multiple sources, including news outlets, social media platforms, and financial reports. Sentiment from Twitter and Reddit was given extra weight due to rapid dissemination of real-time information, and financial reports were analysed using Natural Language Understanding (NLU).
Multi-Source Integration
Short- vs Long-Term Considerations
Considered the client’s needs for both short-term and long-term predictions, where short-term relies heavily on real-time data and immediate reactions, while long-term predictions require more historical data and detailed NLP tasks like NLU and sentiment analysis.
Proposed a hybrid approach of merging numerical and textual-driven insights by combining sentiment analysis of financial reports with numerical predictions based on market data, using feature engineering to get an optimal set of features.
Hybrid Modelling
Divergence Detection
Contrasted the final sentiment with analysts’ expectations to detect when sentiment diverged from the common view, indicating a possible opportunity for trading.
The Solution
We designed a hybrid approach that combines textual sentiment signals with traditional numerical market data. The system extracts sentiment from news, reports, and social content, assigns confidence, and compares results against analysts’ expectations to highlight meaningful divergences.
Hybrid Approach
Merged numerical and textual-driven insights by combining sentiment analysis with numerical predictions, using feature engineering for optimal features.
Sentiment + Confidence
Focused on sentiment analysis and confidence levels, including cross-referencing financial reports with analysts’ expectations.
Source Integration
Integrated data from multiple sources: news outlets, social media platforms (with extra weight for Twitter and Reddit), and financial reports, to capture real-time market sentiment.
Technical Specifications
Inputs
News outlets, financial reports, social media (including Twitter and Reddit)
Core tasks
Sentiment analysis, confidence scoring, natural language understanding (NLU)
Structuring
Textual data was processed and structured to extract optimal features during analysis
Hybrid modelling
Combined textual sentiment with numerical market data to produce a final market trend signal
Outliers
Compared sentiment against analysts’ expectations to detect meaningful divergence
The Outcome
While the Generative AI approach we developed did not entirely replace traditional models, it provided valuable insights into market sentiment. It offered a different perspective on market movements that could be used in tandem with existing models to improve the accuracy of predictions. The sentiment analysis could identify early warning signs of stock volatility, allowing the client to act more quickly than they could using only numerical data. The client decided to continue developing the model with their in-house team, building on the foundation we had established.
Analysed NLP tasks for the stock market, including sentiment analysis, confidence scoring, and cross-referencing with analysts’ expectations.
Integrated data from multiple sources, including news outlets, social media platforms, and financial reports.
Gave extra weight to Twitter and Reddit sentiment for real-time information; used Natural Language Understanding (NLU) for financial reports.
Proposed a hybrid approach combining sentiment analysis with numerical predictions, using feature engineering for optimal results.
The client’s in-house team continued development based on the project’s foundation.
Exploring NLP for Financial Insights?
Let’s discuss how sentiment analysis, structured text pipelines, and scalable real-time processing can turn unstructured data into decision-support signals.
