Asset Allocation Strategies, Data Snooping, and the 1/N Rule

Abstract

Using a series of advanced tests from White’s (2000) “Reality Check” to correct for data-snooping bias, we assess the out-of-sample performance of various portfolio strategies relative to the naive 1/N rule. When we analyze 16 basic portfolio strategies, 126 learning strategies, and nearly 2,000 extended strategies, we find that some strategies outperform the 1/N rule in conventional tests that do not account for data-snooping bias. However, after we use the new tests that control for such bias, we find that none or very few of these strategies outperform the 1/N rule. Thus, our finding underscores the necessity to control for data-snooping bias when making asset allocation decisions.

Keywords: Portfolio strategies, Data snooping, Reality Check, Predictability

JEL Classification: G11, G14