What was the main challenge in developing the stock trend prototype?

The primary challenges were lack of pre-existing datasets, unstructured news text, and the complexity of training sentiment models to generalize across industries.

How did Azati handle data limitations?

The team manually collected and cleaned news articles using custom web scrapers and involved data entry specialists to map and label key phrases influencing stock trends.

What machine learning techniques were used?

LSTM neural networks were applied for sentiment and narrative analysis, allowing the prototype to detect patterns in news coverage that affect stock price movements.

What was the outcome of the prototype?

The MVP achieved an average prediction accuracy of 65%, demonstrating the feasibility of using news articles to estimate stock market trends, although processing time prevented real-time API integration.

How can the prototype be improved?

Accuracy can be increased by processing larger datasets, improving data cleansing, and enhancing model stability. Additional industry-specific modules could expand generalizability.

Stock Market Trend Discovery with Machine Learning

Azati Labs developed an AI-powered prototype that identifies stock market trends. The prototype leverages machine learning and sentiment analysis to evaluate how news articles impact stock price movements.

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65%

average prediction accuracy

20-30min

processing time for 1,000 news articles

1.5-2x

improvement in trend prediction reliability

All 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

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.

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.

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.

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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Solution

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

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

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

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

Business Value

Prototype Feasibility: Demonstrated that sentiment and narrative analysis of news can statistically predict stock market trends, with an average accuracy of 65%.

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.