As many of our readers know, we don’t think highly of any form of active investing and in particular the actively managed mutual fund industry. These fund companies specialize in trying to identify mispriced securities in concentrated portfolios, with the hope of trying to beat a benchmark. Their pursuit of attempting to outperform a benchmark using other investors’ money has only enriched themselves by charging high fees without delivering superior performance. More often than not, those fund managers have left their investors worse off than if they would have just invested in a lower cost index fund that tracks the risk comparable benchmark.
We have recently published numerous studies analyzing the historical performance of many major actively managed mutual fund companies and have come to a resounding conclusion: they have failed to deliver on the value proposition they profess, which is to reliably outperform a risk comparable benchmark.
The fund families we have analyzed include:
Although we have shed light on many of the largest actively mutual fund managers in the industry, it does beg the question of, “well how about the mutual fund family that IFA most frequently advises their clients to invest in: Dimensional Fund Advisors?”
It’s a good question because it allows us to educate our investors about the basic philosophical differences between the approaches taken by the actively managed firms and Dimensional Fund Advisors. Simply put those differences can be summarized by stating that markets work, diversification reduces risk and certain company characteristics explain returns. Here are further explanations of these ideas:
Financial markets are efficient. Current market prices fully incorporate available information and forecasts. Future prices reflect random and unexpected new information. As the result of millions of traders engaged in price discovery, the current price is the best estimate of the fair price and from that fair price investors expect a fair return on average and over risk appropriate periods of time. The range of short term returns relative to the average is commensurate with the risk of the investment.
Risk and return are inseparable. Although there is no such thing as return without risk, not all risks are equally rewarded. Long-term historical risk and return data informs the investment selection process, and index portfolios seek to capture the historical risk factors that have appropriately compensated investors for risks taken, including market, size, value, and profitability for equity and term and default for fixed income.
Diversification is essential. Diversification within and among asset classes, and over extended periods of time, reduces risk and security selection and market timing errors, allowing investors to improve the probability of capturing the returns offered by global financial markets.
Structure explains performance. The returns of diversified portfolios can be explained by the exposure to the various risk factors. Therefore, investors can design diversified portfolios that target those risk factors and avoid the high costs, errors and risks of active management that have been harmful to investor's financial success.
Advisor Advantage. There are distinct measurable benefits to enlisting the services of a passively-oriented advisor, including investor behavior modification, appropriate asset allocation, disciplined rebalancing, tax loss harvesting, asset location, and glide path risk reduction over time.
Fair Prices Equal Fair Returns. In competitive capital markets, investors should expect to pay a fair price for securities based on their risks, expected returns and the uncertainties of those returns. The more uncertainty of acheiving that expected return, the lower the price buyers should be willing to pay. The future monthly returns on a diversified portfolio approximate a normal distribution or bell curve and our best estimation of that expected return is the very long term (something like 50 years) historical average based on those fair prices that were obtained in publicly traded markets. In essence, when you are selecting an index portfolio of a certain risk level, you are accepting a future distribution of outcomes, that on a monthly basis approximate a bell curve.
Dimensional Fund Advisors accepts this basic premise that markets are fairly pricing securities based on the risks associated with them (i.e. markets work) and therefore accept market prices and market returns. Their active counterparts believe that free markets are not performing their basic function of setting fair prices and that they can identify those securities to either buy the undervalued and avoid, sell, or “short” overvalued. As Rex Sinquefeld cleverly put it, “so who still believes market don’t work? Apparently it is only the North Koreans, the Cubans and the active managers.”
Dimensions of Expected Stock and Bond Returns
Is there only one type of risk investors are considering? Well, no! In fact, there are multiple dimensions of risk associated with stock and bond markets. Academics have identified multiple factors including size, value, profitability, capital investment, default, and term as the driving forces behind stock and bond returns. Think of these factors as the components of the engine in your car. Like your car’s ability to perform, the driving force behind stock and bond returns are these factors that are being considered by investors and are therefore (consciously or subconsciously) being embedded into stock and bond prices.
This is extremely important to understand because it is going to explain the discrepancies that we see when examining performance of both actively managed mutual funds and for even their passive counterparts. It also allows us to explain the outperformance of Dimensional’s strategies against well known index fund competitors such as Vanguard.
When comparing index funds to indexes, the analysis is more about the differences in indexes (factor exposures) rather than the existence of alpha (selecting stocks or bonds from within a benchmark). This is what makes Dimensional different. They create their own Dimensional Indexes that include different tilts to the dimensions of returns, such as size, value and profitability. Then they implement their indexes in their funds with rules that minimize market impact costs, minimize taxes in tax-managed funds, screen for social and sustainability factors, and screen for float considerations and momentum.
So when we see positive or negative differences in live Dimensional fund returns, the next step is to look at the longer period index data on the factors that distinguish the index fund from the Morningstar assigned benchmark. When you do that, you see that long-term data supports Dimensional's factor tilts, even though the shorter term live data may not show the premiums (excess returns of one factor over the other). This is where it becomes difficult for investors because they prefer to see the premiums on a consistent basis, where you may see 10 to even 20 years where the premium was not available in the data. So when comparing indexes, unfortunately live fund data is rarely enough data to draw conclusions.
The chart below illustrates the difference among Russell Indexes and Dimensional Indexes over the last 37 years, which starts from the inception dates of some Russell indexes. The scales on this chart are important to understand. On the y-axis you have a scale of company size, with 0 representing the average company size of a total market index fund, 1.0 representing a micro cap company and -0.4 representing a mega cap company. The x-axis scale represents a book to market ratio, which is a value quantification. On the left side are growth companies and on the right side are value companies. For investors in stock index funds, this chart maybe one of the most important charts to understand because it explains that when an index is tilted towards small and value the 37 year annualized return was 15.94%, while the opposite end of the size/value spectrum, a large growth index, had an annualized return of 11.05%. That is a substantial 4.89% per year difference in annualized return. So now investors can see that an index with a smaller and more value tilted design is a preferred index. Look at the Russell 2000 Value Index (small cap value index) with a 12.83% annualized return versus the Dimensional Small Cap Value Index (which had a stronger small and value tilt) with a annualized return of 15.94%. This chart demonstrates why Dimensional had the best designed indexes in this 37 year period. Data over 88 years also supports that small and value tilts had higher returns in US markets.
Based on the long-term data, there has been an excess return for exposure to these risk factors, referred to as the US Equity Premium (Risk of the Total Market Minus Risk Free 30 d T-Bill), the US Value Premium (High Book to Market Minus Low Book to Market), and the US Size Premium (Small Companies Minus Big Companies). An important consideration for investors is the likelihood that these risk "premiums" are actually zero (i.e., there is no premium) despite a historical mean that is positive. The starting point is calculating a t-stat for each premium return series as outlined in the bar charts and data below. The blue bars indicate a positive excess return for the factor premium and the red bars indicate a negative factor premium. The t-stats, as shown in the bottom section of the chart, are all considered statistically significant (i.e., greater than 2), and we can almost be 99% sure that all three risk premiums are positive, with only the size premium (Small Cap minus Large Cap) t-stat being marginally lower than the required 2.6 for a 99% level of significance.
Fees & Expenses
Let’s get into the actual data. We examined funds offered by Dimensional Fund Advisors that Index Fund Advisors recommends to its clients. There are many other funds from Dimensional that are either duplications of existing asset classes or funds that IFA does not think will add incremental value to a globally diversified portfolio.
The costs we examine include expense ratios, front end (A), level (B) and deferred (C) loads, and 12b-1 fees (there are no loads or 12b-1 fees in Dimensional Funds). These are considered the “hard” costs that investors incur. Prospectuses, however, do not reflect the trading costs associated with mutual funds. Commissions and market impact costs are real costs associated with implementing a particular investment strategy and can vary depending on the frequency and size of the trades taken by portfolio managers.
The table below details the hard costs, as well as the turnover ratios, for 35 mutual funds offered by Dimensional Fund Advisors that IFA utilizes in their IFA Index Portfolios. You can search this page for a symbol or name by using Control F in Windows or Command F on a Mac. Then click the link to see the Alpha Chart. Also remember that this is what is considered an in-sample test, the next level of analysis is to do an out-of-sample test (for more information see here).
On average, an investor who utilized an equity strategy from Dimensional experienced a 0.40% expense ratio. Similarly, an investor who utilized a bond strategy from Dimensional experienced a 0.20% expense ratio. The average turnover ratios for equity and bond strategies from Dimensional were 8.89% and 84.75%, respectively. This implies an average holding period of about 14 months for bond funds and 11 years for equity funds. The high turnover associated with Dimensional’s bond funds is due to the majority of them being short term in nature and variable term and credit strategies employed.
Remember those driving factors of stock and bond returns that we mentioned above? Most of the discrepancies between passively managed fund returns and a benchmark is just discrepancies in “factor exposures.” As a result, we have identified these differences in the charts below as Factor Gaps.
We have included Factor Gap charts below for 36 Dimensional funds that are utilized in various implementations of IFA Index Portfolios. Dimensional's equity funds are smaller and more value oriented, so underperformance relative to their benchmark is due to these factor premiums not being positive for the time period examined. The size, value, or profitability premiums are not positive for all time periods. This is because there is risk associated with these premiums. If they were always positive, the price would reflect that and then there wouldn't be any risk and the premiums would become zero.
Dimensional's fixed income funds tend to hold bonds that are very high quality and are shorter term in nature. They were created to complement DFA's equity funds. Looking through the lens of an entire portfolio, equity funds that are tilted towards the dimensions of expected return are better diversified if they are complemented with fixed income that is short term and of extremely high quality. When looking at the Factor Gap between DFA's fixed income funds and their benchmark, underperformance can be attributable to differences in the average maturity of the bonds as well as the average quality of those bonds.
Not All Index Funds Are Created Equal
When investors finally embrace a passive approach to investing they often falsely believe that all passive investing is the same and that the lowest fee will result in the highest return.
Dimensional’s ability to better target the driving forces behind stock returns allows them to have outperformed other index funds. This can also be displayed in a comparison of Dimensional’s performance against a similar fund offered by Vanguard. The chart below shows you historical performance of Dimensional versus Vanguard for the 17.5-year period from 1/1/1999 to 06/30/2016. The time period is limted by the inception dates of the funds.
As you can see, in almost every asset class Dimensional has outperformed Vanguard, even with higher expense ratios. Why?
In the table below the bar chart, there are 5 different categories that include Book-to-Market (a value metric), Weighted Average Market Capitalization and Standard Deviation. The higher the Book-to-Market ratio, the more the fund is exposed to the value factor. Weighted Average Market Capitalization is a measure of exposure to the size factor. The smaller this number, the greater the exposure to the size factor (small and micro cap stocks). You can see that Dimensional consistently had a higher Book-to-Market Ratio and a smaller Weighted Average Market Capitalization. In other words, they have been better at targeting the known dimensions of expected stock returns. This has led to their outperformance against Vanguard while still maintaining robust diversification (# of holdings).
When investors ask us about Dimensional’s “alpha,” it presents a great opportunity to educate investors on the fact that Dimensional doesn’t try to generate an “alpha” in the traditional sense of security selection. This is in contrast to the entire active investment community, which is seeking alpha by selecting the "best stocks or bonds". In our analyses of actively managed mutual fund firms, we find that virtually all active funds, when considering statistical significance of the alphas, have not been able to reliably deliver alpha and have falsely attributed their positive alphas as a repeatable skill, as opposed to chance outcomes. In our opinion, Dimensional understands the absence of alpha and the academic research on risk factors better than any other mutual fund company, allowing them to have returns that are different than we find with other index fund offerings from Vanguard, Fidelity, Schwab, or Barclays.
This, among many other reasons, is why Index Fund Advisors advises clients to invest in Dimensional funds.
Factor Gap Charts
Below we show Dimensional fund returns versus the Morningstar assigned benchmark. Rather than identify the differences in returns as Alpha, we have changed the label to Average Factor Gap. This is because when comparing a passively managed fund to an index, the differences are primarily a difference in factor exposure. Dimensional does have a few other modifications to their index implementation such as patient trading, tax-management, social and sustainability screens, etc.
An example of factor exposure differences can be seen in the third chart below showing DFA 2 Year Global Fixed Income (DFGFX) compared to Morningstar's choice of a benchmark. While DFGFX has an average maturity that is less than 2 years, the Citi WGBI non USD has an average maturity of just under 9 years. This can lead to a lot of tracking error between the fund and the benchmark, like we saw in 2002-2004. This underperformance isn't due to inferiority of the fund, but due to a difference in the term or maturity risk factor.
Here is a calculator to determine the t-stat. Don't trust an alpha or average return without one.
The Figure below shows the formula to calculate the number of years needed for a t-stat of 2. We first determine the excess return over a benchmark (the alpha) then determine the regularity of the excess returns by calculating the standard deviation of those returns. Based on these two numbers, we can then calculate how many years we need (sample size) to support the manager's claim of skill.
About the Author
Mark Hebner - Founder, Index Fund Advisors, Inc.
Founder and President of Index Fund Advisors, Inc., and author of Index Funds: The 12-Step Recovery Program for Active Investors. He is a Wealth Advisor, with an MBA from the University of California at Irvine and a BS in Pharmacy from the University of New Mexico with a specialization in Nuclear Pharmacy.