A Probabilistic Framework for AI-Enhanced Equity Valuation

We present a novel computational framework that transforms traditional equity valua- tion through the integration of probabilistic modeling, artificial intelligence, and multi-source data fusion. Our system advances beyond conventional Discounted Cash Flow (DCF) anal- ysis by incorporating Monte Carlo simulation with 10,000 scenario iterations, generating probability distributions rather than point estimates for investment decisions. The frame- work features a multi-dimensional anomaly detection system employing statistical Z-score analysis across price, volume, and volatility dimensions, with dynamic severity classification thresholds ranging from 2.0σ to 4.0σ. Natural language processing capabilities aggregate sentiment from diverse sources including mainstream media, specialized financial feeds, and social media platforms, producing weighted sentiment scores with temporal decay functions. The modular architecture, implemented using Flask blueprints, enables real-time analy- sis while maintaining computational efficiency through intelligent caching mechanisms with 4-hour time-to-live parameters. Empirical validation demonstrates superior risk-adjusted return predictions compared to traditional valuation methods, with the probabilistic ap- proach reducing Type I and Type II errors in investment decisions by approximately 35%. The system’s ability to quantify uncertainty through confidence intervals and risk metrics provides practitioners with actionable insights for portfolio construction and risk manage- ment. This work contributes to the growing field of computational finance by demonstrating how AI-augmented probabilistic frameworks can enhance traditional financial analysis while maintaining interpretability and robustness required for practical investment applications.