Financial Valuation of Artificial Intelligence Firms: A Panel Data Approach Based on the Modified Ohlson Model

Omar   METIDJI

Omar METIDJI University of Laghouat, Digitalization and Quantitative Applications in Economic Sciences Laboratory, Algeria

Keywords: Artificial Intelligence, Financial Valuation, Modified Ohlson Model, Panel Data, R&D Expenditures

Abstract

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.

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