Development of an AI-Powered Text Similarity and Stock Price Prediction Platform

The client faces challenges in analyzing lengthy, complex financial reports to predict stock performance. Manual review is time-consuming and prone to inconsistency, making it difficult to leverage textual data for actionable investment decisions. Existing models have not effectively captured meaningful patterns that indicate future stock movements based on report similarities.

A mid- to large-sized financial analytics firm specializing in market trend prediction and investment insights.

• Develop an automated system to scrape and clean financial reports from publicly available sources for multiple companies over extended periods.
• Implement NLP techniques to compute similarity measures (cosine and Jaccard) between historical reports to identify patterns indicative of stock performance.
• Create a robust analytical framework that correlates report similarity metrics with subsequent stock price movements over predefined holding periods.
• Design an interactive dashboard to visualize similarity groups and corresponding financial performance, enabling strategic investment decisions.
• Achieve a minimum of 3% higher return in the high-similarity report group after a three-month holding period on average, validating the predictive hypothesis.

• Automated data scrapping module to collect SEC filings from multiple companies over 20 years.
• Text preprocessing pipeline including stemming, stopword removal, punctuation removal, and case normalization.
• Conversion of reports into vector representations based on term frequency for similarity calculations.
• Implementation of cosine similarity and Jaccard coefficient algorithms to assess report similarity efficiently.
• Clustering reports by similarity percentile (low, medium, high) and correlating these groups with stock price movements over specified periods.
• Integration of historical stock price data for return analysis post-report publication.
• Interactive visualization dashboard displaying return metrics across different similarity groups over multiple time horizons.

• SEC filings repositories or APIs for automated report download.
• Historical stock market data feeds for correlating report similarity with stock performance.
• Secure data storage solutions for large-scale historical report and price data.

• System scalability to process and analyze over 20 years of multithousand reports for hundreds of companies.
• Real-time or near-real-time processing capabilities for new report ingestion and analysis.
• Data security measures to protect sensitive financial and market data.
• High availability and fault tolerance to support continuous operation.

The platform aims to enhance predictive accuracy of stock movements based on report similarity metrics, resulting in approximately a 3% higher return in high-similarity groups over a three-month period. It will enable the client to make more informed investment decisions, improve analysis efficiency, and extract actionable insights from financial report texts, thereby strengthening market positioning and investment success rates.