In this post, I explore how a fundamental challenge in empirical asset pricing — the joint hypothesis problem — appears in many aspects of our daily lives. While we often take for granted that evaluation requires comparison, this simple principle leads to complications in how we assess everything from investment strategies to societal wellbeing. I thank Lucy Msall for comments.
An important rule in life is that evaluation necessarily requires comparison. Whether we're assessing investment performance, technological capabilities, or life experiences, we implicitly rely on some benchmark of what "good" looks like.
This fact is particularly relevant when individual evaluations must be aggregated into collective decisions. The solutions developed in finance, particularly the focus on "near-arbitrage" tests, might offer insights for this broader challenge of social decision-making.
The challenge that begets evaluation was formally recognized in financial economics by Eugene Fama while developing the Efficient Market Hypothesis (EMH). Fama observed that when testing market efficiency, we're actually testing two joint hypotheses:
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In other words, if we find statistically significant excess returns, we can't definitively say whether:
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As a result, when we find that value stocks outperformed growth stocks, or small-cap stocks generated excess returns, we couldn't definitively attribute these patterns to market inefficiency. These "anomalies" might instead reflect risk factors missing from our pricing models.