Shortfall minimization and the Naive (1/N) portfolio: an out-of-sample comparison

ABSTRACT

Naive portfolio selection, wherein an investor allocates an equal portion of their wealth to the field of candidate assets, is a simple ad-hoc way to create a portfolio. Naive portfolio selection contrasts to the many sophisticated portfolio selection rules that are optimal with respect to a specific portfolio allocation objective and which often perform well in sample. However, some recent research finds that many of these ‘optimal’ portfolio allocation mechanisms perform no better than naive diversification in out-of-sample data. This paper extends this line of inquiry by comparing the out-of-sample performance of naive portfolio selection to several recently developed shortfall-minimizing portfolio selection methods. The results corroborate the prior findings that optimal portfolio methods struggle to beat the naive portfolio in out-of-sample environments.

KEYWORDS:

• Entropy
• Safety First
• skewness
• optimal portfolio selection

JEL CLASSIFICATION:

• G11

Disclosure statement

No potential conflict of interest was reported by the author.