Quantitative AI Models for Company Valuations

Accurate company valuation is a cornerstone of financial decision-making, driving investment strategies, mergers and acquisitions.Traditional valuation techniques, such as Discounted Cash Flow (DCF) and comparable company analysis, suffer from limitations including reliance on static assumptions, sensitivity to forecasting errors and inability to integrate large, dynamic datasets effectively.In this study, we explore the potential of Artificial Intelligence (AI) models in addressing these limitations by leveraging supervised learning algorithms (e.g., XGBoost, Random Forests) and deep learning architectures (e.g., LSTMs, Transformers).Our methodology combines structured financial metrics with unstructured textual data, such as market news and earnings reports, processed through Natural Language Processing (NLP) techniques.Using hybrid multi-modal architectures, the models analyze financial patterns, sentiment and other market dynamics.Results demonstrate that AI-driven models outperform traditional approaches across key evaluation metrics, such as Mean Absolute Error (MAE) and R-squared, achieving a 25% improvement in accuracy compared to DCF models.The findings highlight AI's ability to capture non-linear and complex relationships in data, offering significant advancements in valuation accuracy and reliability.This research provides a foundation for real-time valuation systems, enabling dynamic updates for portfolio management and investment strategies.Future work will focus on enhancing model explain ability and integrating macroeconomic variables for improved predictive performance.