Financial Valuation of Artificial Intelligence Firms: A Panel Data Approach Based on the Modified Ohlson Model - METIDJI, Omar (Journal of Excellence for Economics and Management Research, 2026)
This study investigates the determinants of market valuations for Artificial Intelligence (AI) firms to determine whether the recent surge in technology stock prices reflects a behavioral speculative bubble or rational pricing of financial fundamentals. Using a Modified Ohlson Model, the research analyzes a balanced panel data sample of 30 leading U.S. AI-centric technology firms from Q1 2020 to Q4 2025. Econometric estimation via a robust Fixed Effects model reveals a paradigm shift in value relevance. Traditional accounting metrics, such as tangible Book Value Per Share (BVPS), lost statistical significance, while current Earnings Per Share (EPS) exhibited a negative valuation impact. Conversely, Research and Development (R&D) expenditures demonstrated a significant positive premium, indicating that financial markets actively reward innovation intensity and penalize short-term profit maximization. The findings conclude that the ’AI premium’ is not merely speculative noise but a rational market response to aggressive technological reinvestment. Consequently, the study recommends recalibrating traditional valuation multiples and reforming accounting standards to better capture intangible assets.