Naive diversification: why investors default to equal weights and what it costs

When presented with several investment options and asked how to allocate among them, investors gravitate toward equal weights—1/N across N choices, regardless of what those choices are or how they relate. The heuristic is sensible in some settings and badly suboptimal in others, and the boundary between the two is the practical question.

What naive diversification is

Naive diversification, also called the 1/N heuristic, is the tendency to allocate equally across whatever options are presented in a choice set. If presented with three investment funds, investors tend to put 33% into each. If presented with five, they tend to put 20% into each. The heuristic is applied even when the funds within the menu are economically very different—an equity fund and a bond fund and a commodity fund—and even when the same effective allocation could be reached more efficiently by combining a smaller number of more diversified funds.

Benartzi and Thaler (2001), in Naive Diversification Strategies in Defined Contribution Saving Plans, documented the effect using participant data from US 401(k) plans. They showed that participants' equity-bond mix was systematically influenced by the composition of the menu they were offered: a menu with more equity funds produced a higher average equity allocation, regardless of whether the additional funds added meaningful diversification. The 1/N rule was being applied to whatever menu was placed in front of the participant.

How it manifests in investing

The most direct manifestation is in defined-contribution plans, where participants choose among a fixed menu of funds. A plan that offers six equity funds and four bond funds will produce, on average, a 60/40 equity-bond allocation among participants who use 1/N—even if the four bond funds were genuinely sufficient to capture all the bond exposure available, and even if some of the equity funds are highly correlated with each other and add little diversification.

The same heuristic appears in self-directed brokerage portfolios. Investors who pick five equity ETFs because they 'wanted diversification' often hold five funds with overlapping holdings—five US-large-cap funds that hold 70% of the same names, for instance—and end up with a portfolio that is no more diversified than a single fund would have provided, plus the additional management fees of the four redundant ones.

A related expression is over-diversification across asset classes when the underlying assets are similar. Holding five different US-equity ETFs and one international-equity ETF is not the same as holding 1/6 in each of six asset classes; the resulting portfolio is dominated by US equity beta with a small international tilt, regardless of how the choices were labelled.

The cost

The cost of naive diversification depends on the menu. In some settings, 1/N is approximately optimal: DeMiguel, Garlappi, and Uppal (2009) compared 14 portfolio construction methods (including various optimisation-based approaches) against a naive 1/N allocation across multiple datasets and concluded that none of the sophisticated methods consistently outperformed the simple equal-weight portfolio out of sample. The estimation error in the inputs to the optimisation methods was large enough to wipe out their theoretical advantage. For an investor choosing among genuinely diversified asset classes, 1/N is a reasonable starting point.

The cost is higher when the menu is poorly constructed. If the choice set contains five overlapping equity funds and one bond fund, the resulting 5/6 equity allocation may be far more equity-concentrated than the investor's risk tolerance warrants. The naive heuristic gives the menu designer disproportionate influence over the resulting portfolio, regardless of whether the menu was designed thoughtfully.

The cost is also higher in contexts where the investor has genuine information that should produce a non-equal weighting—different expected returns, different volatilities, different correlations with the rest of the portfolio. Equal weighting in those settings discards the information and produces an allocation that is theoretically suboptimal. The cost depends on how good the information is; in practice, the information is often poor enough that 1/N still wins out.

What helps

The structural remedy is to define the asset universe deliberately rather than letting the menu dictate the allocation. Constructing the portfolio from a small set of broad, diversified, low-cost funds—one global equity fund, one global bond fund, one diversified alternatives fund—and assigning weights based on the investor's risk tolerance produces a more controlled outcome than picking equal weights from a long menu of overlapping options.

The other remedy is to use construction methods that explicitly account for the correlations and volatilities of the chosen assets, rather than treating them as interchangeable. Mean-variance optimisation, Hierarchical Risk Parity, and risk-budgeting approaches all produce allocations that are sensitive to the structure of the universe rather than depending only on its cardinality. Each has its own trade-offs—1/N often wins out-of-sample against optimisation methods with noisy inputs—but the deliberate engagement with the universe's structure is more defensible than ignoring it.

Naive diversification in pfolio

Equal weight is one of pfolio's three portfolio optimisation methods, alongside mean-variance optimisation and Hierarchical Risk Parity. Investors who want the simplicity of an equal-weight allocation can select it directly; the platform's analytics then track the same risk and return metrics as for any optimisation method, allowing direct comparison of 1/N against more sophisticated alternatives.

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