Equities

  • SEED favors certain characteristics, namely value, when forming a diverse basket of stocks or investing in ETFs.

    First and foremost, we favor a value bias in the stock portfolios we manage. Paying less for a variety of valuation measures has shown over time and across markets the ability to generate additional returns to investors. Many other broad characteristics, commonly called “factors”, also seem to add to incremental returns including market capitalization, momentum, liquidity and volatility to name a few. As we cover below, these added returns may be associated with added risk. Behavioral economists argue instead that these factors add to performance by avoiding common human decision traps. Either way, these factors have historically and across markets provided excess (i.e., above market) returns. The biases we favor also pass two other key hurdles: they are theoretically sound and sensible, and they are simple to apply and test for.

    From an academic standpoint and ours, Occam’s razor applies: the simpler the better. For models designed to capture market mispricing, the more complex the required empirical test, the higher the risk of data mining. In other words, by running the data and adjusting the model enough times, a researcher will find good results regardless of how spurious. But just because the price-to-book ratio (to pick on one measurement used in academia) adequately captures “value” does not imply that it’s the best in practice. Even if the extra returns associated with value stems from added risk, that risk remains largely undefined. Whatever measurement of value employed is only a proxy for this nebulous and undiversifiable risk factor or factors. In a mispriced asset world favored by behavioral economists, price-to- book is an even more suspect measure of cheapness given how poorly book value captures the worth of intangible assets like brand value. So yes, any particular value index fund may provide better historical risk adjusted returns than the next, but SEED won’t attempt to track either one at the expense of higher taxes or costs. We apply this same common sense approach to gaining exposure to other factors.

    For instance, for those clients where a large basket of stocks will better provide greater diversification away from either legacy assets or their own profession, we initially weight stocks more equally in a portfolio rather than weight them by their market capitalization. We understand the resulting performance will not accurately track any of the multiple “market” portfolios available. In a tax-free world, we thus won’t be “mean-variance efficient” (i.e., mathematically maximizing average expected returns while minimizing volatility). Because the distribution of returns across many stocks will be widely disperse, equal weightings also expand our ability to harvest tax losses (see section on Tax Loss Harvesting). The willingness to equal weight our stock holdings hopefully does more than just provide our clients with greater true diversification and tax efficiency. It may add to returns by exposing us to the value and small cap factors.

    The superior historical performance of the equal versus capitalization weighted S&P 500 should not be surprising to either behaviorists who believe in simple mean reversion of stock prices or efficient market disciples who believe in the random walk of stock prices. For the mean reversion crowd, investors simply are benefitting from vacillations in popularity. For those that believe that markets are efficient, equal weighted portfolios loosely track the benefits of having a value and small cap bias. Either way, the gains we achieve in optimizing tax efficiency and tailoring the portfolio to offset the full array of an investor’s risks (namely to their own income) more then off-set the costs of failing to track one of the myriad of available indices.

    We don’t want to downplay the fact that the costs of diverging from a market index can be real. The expected return of equities is already uncertain and will be only more so with an ad hoc basket of stocks. We thus prefer exchange traded funds (ETFs) or index funds for investors who have the majority of their wealth in tax-advantaged accounts. We pay attention to industry weights and prefer to utilize the strategy for only those that can buy a large (at least 50, and preferably more) basket of stocks efficiently. Otherwise, over time, any one stock can dominate the portfolio. We implement out strategy using larger companies and augment the basket with ETFs designed to more readily capture the advantages of investing in smaller companies (especially for those designed to also incorporate a value bias) and stocks exhibiting momentum. Explore the links below for more details on these and other model factors.  

Exposure to various stock attributes and equal weightings do not assure or guarantee better performance and cannot eliminate the risk of investment loss. This is not constitute a blanket recommendation for any particular fund as all investors are unique. Before investing, investors should carefully read the applicable volatility disclosure for any underlying fund, which can be found in their current prospectus.








For a concise summary of these ideas, see the S&P Dow Jones article “The Limits of History.” Author: Craig J. Lazzara. February, 2013 and this Chicago Booth Review artilce “300 Secrets* to High Stock Returns: Has the Hunt for Investible Factors Gone Too Far.”







I use quotes for “market” as this term is defined very differently in practice than in theory as explored in my article “Passive Investing: Theory and Practice in a Global Market“.




















See S&P 500 Cap Weighted vs Equally Weighted return as calculated by Bloomberg here. Past performance is not, however, indicative of future results.






See Gene Fama and Ken French’s excellent overview on the uncertainty of active management and premiums associated with equities in general and small cap and value biases specifically at Dimensional Advisor’s website found here.