Machine-learning residual income valuations from accounting data alone yielded 6.3% gross annual alpha for a monthly, equal-weighted long-short valuation-to-price decile portfolio, concentrated in smaller stocks (U.S. firms; 1985–2024; gross of costs).
Can artificial intelligence usefully estimate intrinsic firm value without cues from market prices? In his September 2026 paper entitled "Valu(AI)tion: Machine Value and Market Prices", Marc Schmitt builds a machine analyst that each year forecasts profits strictly from published firm accounting data (no stock prices/returns or analyst estimates) and calculates firm value by inserting forecasted profits into a residual income model. He assumes accounting data are available four months after fiscal year ends. He then each month reforms an equal-weighted hedge portfolio that is long (short) the tenth, or decile, of stocks with the highest (lowest) estimated valuation-to-price ratios. Using publicly available accounting data for U.S. firms during 1975 through 2024, with valuation testing starting in 1985, he finds that:
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