Black Swan: Stock Crash Predictor

Abstract

Global financial markets have grown so interconnected and fast-moving that spotting a high-impact event before it hurts investors\' portfolios has become a serious challenge. This project introduces \"Black Swan: Stock Crash Predictor,\" an AI-driven system built to catch unpredictable global events early and gauge how they are likely to ripple through financial markets, using a combination of Generative AI and Agentic AI. Rather than waiting for a crisis to unfold, the system keeps a constant watch over news outlets, RSS feeds, financial reports, and live market feeds, flagging emerging risks such as geopolitical conflict, pandemics, economic downturns, and supply-chain breakdowns as they surface. Generative AI is used to turn this raw information into readable summaries, forward-looking narratives, and impact assessments, so investors can make sense of complicated situations quickly. Agentic AI adds a further layer of autonomy, allowing the system to reason independently, make decisions on its own, and keep adjusting to shifting market conditions. It also runs automated stress-testing simulations and risk assessments to gauge how markets and investments might behave under uncertainty. Where conventional monitoring tools tend to fall behind fast-changing events, this approach is built to keep pace, offering proactive alerts that give investors an early warning. Overall, the framework is aimed at sharpening key decision-making factors: response time, prediction accuracy, market awareness, and investment strategy planning.

Introduction

The text presents the Autonomous Black Swan Risk Monitor, an AI-based system designed to identify unexpected, high-impact global events and assess their potential effects on stock markets and individual companies. As financial markets become more complex and retail investor participation increases, traditional investment strategies that rely mainly on historical data and manual news analysis are often too slow to respond to sudden events such as geopolitical conflicts, pandemics, economic shocks, and supply-chain disruptions.

The literature review examines existing research on anomaly detection, machine learning, natural language processing (NLP), sentiment analysis, and stock-price prediction. Techniques such as Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), BERT, and multi-agent systems have improved financial forecasting and automated monitoring. However, many existing approaches focus on predicting stock-price movements rather than identifying the real-world events that trigger major market disruptions. They also face challenges related to market volatility, computational complexity, data quality, scalability, and the reliability of short-term predictions.

To address these limitations, the proposed system integrates real-time global news collection, NLP, Agentic AI, event detection, market-impact analysis, stress-testing simulations, and real-time alerts. It aims to continuously monitor global information sources, identify potentially significant events, estimate their effects on particular companies and markets, and provide timely insights to support proactive risk management.

The main objective is to help investors recognize emerging risks earlier, reduce dependence on manual monitoring, and make better-informed decisions in uncertain market conditions.

Conclusion

Black Swan: Stock Crash Predictor offers a proactive, intelligent way to catch unexpected events before they take a serious toll on financial markets. By keeping a constant eye on global news, RSS feeds, and live data sources, the system is designed to pick up on potential Black Swan events early and estimate how they might play out across the markets. Bringing Artificial Intelligence, Generative AI, and Agentic AI together allows raw, unstructured information to be turned into insight that investors can actually act on, cutting down the need for manual monitoring and sharpening overall risk awareness. Beyond detection, the system helps identify which companies are likely to be affected, produces predictive impact analysis, sends out real-time alerts, and runs stress-testing simulations to support better decision-making in uncertain conditions. By pairing automated event detection with intelligent financial analysis, the framework moves past the limitations of conventional tools that lean mainly on historical data and basic indicators. The project is deliberately scoped around risk awareness and decision support rather than direct trading or portfolio management, positioning it as a scalable foundation for proactive financial risk monitoring and early-warning systems.

References

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Copyright

Copyright © 2026 Mrs. Sreedevi S, Sanmithra B S, Shreyas G, Srujan M V, Syed Muzain Ahmed. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.