Synthesis of Active and Index Investing

There are trade-offs in using active funds or index funds.  You have to take the bad with the good.  I believe the best solution is to use funds practicing evidence-based investing, which combines the best attributes of both approaches.


First, as set out in detail in the last section, I believe you should use funds that charge a low annual fee.  While not as low as index funds, the systematic, rules-based funds I’d suggest have fees well below those of traditional active funds.

With the Constancy Model Portfolios, you will not leak returns to other market participants by trend-following, as is the case with index funds.  Because, in selecting individual securities (stocks or bonds), the funds I suggest adhere to fundamental investing disciplines.  Most critically, they all exercise an explicit valuation discipline to achieve a “margin of safety,” the cardinal principle of Benjamin Graham.  As we’ve seen, study after study shows that a valuation discipline is the most robust factor in achieving higher returns than market benchmarks (indices).  And studies show this factor becomes even more robust when coupled with measures of company profitability.  In short, the funds I’d recommend—both stock funds and bond funds—adhere to sensible criteria that have been statistically robust factors in return premiums.

With active investing, there is the problem of skill uncertainty.  Traditional performance measures used in selecting investment managers—near-term returns, in particular—reflect luck (chance or randomness) as well as relevant information to evaluate the strategy.  And the relevant information includes manager skill as well as how the fund’s investment approach jibes with near-term market dynamics.  It is very difficult to distinguish signal from noise, leaving you prone to untimely fund switching through market cycles.  Like index funds, however, all the funds I suggest for the Constancy Model Portfolios are rules-based.  They apply fundamental criteria, including valuation criteria, in a systematic and automated process.  The criteria apply the common-sense principles discussed in the section A Sensible Approach and have been thoroughly tested by academics and practitioners.  Concerns about selection skill fade, as they do with index funds.

I believe this rules-based, systematic approach provides the highest probability of capturing the full returns that financial markets reliably make available over time, including the premiums from sensible criteria/statistically-proven factors.

When we'd expect the funds to beat and lag indices

Supported by a vast body of academic literature using statistical analysis, I am confident the funds in the Model Portfolios—because they adhere to a valuation discipline—will over time provide higher returns than their corresponding indices and the index funds that mimic them.  I am also confident that there will be periods of months and even years when these funds’ returns lag those of market indices.  This is again because they adhere to a valuation discipline.  As I discussed in the section Chasing Performance, markets move in cycles.  And there are periods in cycles when securities, stocks in particular, with low valuations can under-perform those with high valuations and market benchmarks generally.  This is often true in the later years of a bull market advance (think 1998-1999, 2006-2007), for example, as trend-following picks up and price momentum strategies do well.  Value strategies often do well, on the other hand during stock market downturns and in the early years of recoveries (think 2000-2004).  In studying how stock strategies have performed over the years, we see such patterns.  But, as I’ve discussed, all cycles vary, making them impossible to predict or time.  And there is also randomness as to when the strategies generate their premiums.  So, what I most want to emphasize is that such periods do not reflect skill waxing and waning, but simply how the approach jibes or not with prevailing market dynamics.  Sticking with the approach—and not trying to guess when it will or will not work—is critical in generating the higher returns over the long-term.