As financial markets grow larger and attract more investors, stock prices actually become less accurate rather than more efficient.
April 1, 2026
Original Paper
Complexity Breaks Arbitrage Pricing Theory 
SSRN · 6502339
The Takeaway
Standard economic theory suggests that more data and more players lead to 'perfect' pricing. This paper proves that because of the sheer complexity and 'estimation noise' of a massive market, pricing errors actually scale up as the number of assets increases, allowing completely different investment strategies to all be 'right' at the same time.
From the abstract
Arbitrage Pricing Theory predicts pricing errors vanish as markets grow. Three findings reverse this prediction. First, under estimation uncertainty, equilibrium pricing errors scale as N/t (number of assets over sample size), with a persistent component from time-varying fundamentals that no estimator can eliminate. Second, the stochastic discount factor loads primarily on estimation errors rather than fundamental risk factors, explaining why risk-based factor models fail to price assets. Third