Prototype Feasibility
Demonstrated that sentiment and narrative analysis of news can statistically predict stock market trends, with an average accuracy of 65%.
Technologies used
Python
NLTK
Keras
Gensim
World2Vec
Motivation
The client needed a way to understand how news and media coverage impact stock prices, as traditional analysis methods were too slow and imprecise. The goal was to develop a machine learning-based solution that could automatically analyze news articles, capture sentiment and narrative patterns, and provide actionable insights on stock market trends, helping the client make informed investment decisions.
Main challenges
Challenge 01
Lack of Data
The team lacked sufficient high-quality datasets for model training. Historical stock prices existed, but relevant news articles were sparse and unstructured. Engineers manually collected, cleaned, and normalized data from multiple sources using custom web scrapers, filtering irrelevant content and standardizing formats for effective model training.
#1
Challenge 02
Data Mapping and Labeling
Text data was unstructured and contained ambiguous terms and industry-specific jargon, complicating automatic processing. Two data entry specialists manually mapped and labeled key phrases and entities, ensuring machine learning models could link news content to stock movements.
#2
Challenge 03
Sentiment Analysis Complexity
Capturing sentiment across industries was complex, as words could imply different outcomes depending on context. LSTM neural networks were trained to understand narrative sequences and sentiment nuances. Generalizing models while maintaining predictive accuracy required careful tuning and significant computational resources.
#3
Our approach
Sentiment and Narrative Analysis
Processed historical stock price changes alongside news articles using LSTM neural networks to evaluate sentiment and narrative context, linking news impact to market trends.
Data Collection and Preprocessing
Built custom web scrapers, manually cleaned and labeled text data, and created structured datasets to enable model training despite unstructured sources.
MVP Development
Developed a prototype with interconnected Python scripts for text preparation, model training, and trend probability generation, demonstrating the feasibility of predicting trends from news.
Scalability Considerations
Designed the architecture to scale for larger datasets, allowing the system to process thousands of articles for improved accuracy, even though real-time API integration was not feasible due to processing times.
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01
Custom Data Preprocessing
Scripts automatically clean, normalize, and structure raw news articles, transforming unstructured text into a format suitable for sentiment and trend analysis, ensuring consistent input quality for machine learning models.
Key capabilities:
- Web scraping and extraction of news articles
- Manual labeling and key phrase identification
- Text normalization and filtering
- Preparation of structured datasets
02
Sentiment and Narrative Analysis with LSTM
The prototype uses LSTM neural networks to analyze textual sentiment and narrative flow, correlating news content with stock market movements, which helps identify potential trends.
Key capabilities:
- Detection of positive and negative sentiment
- Trend impact prediction based on narrative analysis
- Adaptation to various industry news
- Probabilistic output for trend direction
03
Trend Prediction and Probability Scoring
Generates probabilistic predictions of stock market trends, allowing financial analysts to evaluate the likelihood of price increases or decreases based on current and historical news context.
Key capabilities:
- Calculation of trend probability scores
- Integration of multiple data sources
- Visualization of trend predictions
- Support for batch processing of news articles
04
Scalable Processing Architecture
The system is designed to handle growing volumes of news articles, with modular scripts and structured pipelines enabling efficient large-scale processing and improved prediction accuracy over time.
Key capabilities:
- Modular Python scripts for flexibility
- Efficient batch processing of large datasets
- Capability to retrain models with new data
- Framework for iterative accuracy improvements
Results & business impact
Data Handling Improvements
Established robust data collection, cleaning, and preprocessing techniques, creating a foundation for scalable AI-based financial analysis.
Machine Learning Framework
LSTM-based model provides a blueprint for extending predictive analytics to additional industries or larger datasets.
Strategic Insights
Offered early-stage insights into linking news narratives to market fluctuations, supporting data-driven investment decisions.
