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Bridgeway Capital Management: A Deeper Look at The Performance

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Multi-factor investing is slowly becoming a more common investment strategy among professional investment management firms. These firms, more informally known as “quants,” use statistical models to understand the common factors that drive performance in markets around the world. By structuring strategies that focus on these factors, managers can increase the expected return of their portfolios.

We at IFA have been investing our clients’ money this way since the founding of our firm, now 18 years ago. The earliest pioneer of multi-factor investing was Dimensional Fund Advisors, our preferred fund partner. By following a passive and disciplined approach to structuring their strategies around the known dimensions of expected return, which include the market, size, relative-price, and direct profitability, they have been able to deliver higher risk-adjusted returns for their clients.

Investment management firm Bridgeway Capital Management also follows a quantitative approach to their own investment strategies. Their homepage even includes the tagline of, “statistically driven, evidence-based investing.” Sound familiar?

Founded in 1993 and based in Houston, TX, the firm currently manages $7.7 Billion in assets across 13 separate strategies that focus on the size and relative-price (value) premiums as well as attempt to capture the benefits of low volatility and momentum in separate strategies.

While they may be put into a similar bucket as Dimensional in terms of being “quants,” their foundational philosophy is quite different. Believing that “alpha” can be reliably delivered, Bridgeway deviates from Dimensional in the simple notion that markets are not effectively functioning in terms of pricing risk. In a market where risk is priced efficiently and effectively, than “alpha” is expected to be a random act of luck versus a displayed act of skill.

Today, we are going to take a deep dive into Bridgeway’s performance to see how it stacks up against not only their Morningstar assigned benchmarks, but also their “alpha” once we adjust for their exposure to the market, size, and relative-price factors that have been shown to be priced risk factors (i.e. Betas) in the market. We will conclude our analysis with a comparison to Dimensional and explain why not all multi-factor investing is created equal. 

We have taken a deeper look at the performance of several other mutual fund companies and have come to one universal conclusion: they have failed to deliver on the value proposition they profess, which is to reliably outperform a risk comparable benchmark. You can review by clicking any of the links below:

Fees & Expenses

Our analysis begins with an examination of the costs associated with the strategies. It should go without saying that if investors are paying a premium for investment “skill,” then they should be receiving above average results consistently over time. The alternative would be to simply accept a market's return, less a significantly lower fee, via an index fund.

The costs we examine include expense ratios, front end (A), level (B) and deferred (C) loads, and 12b-1 fees. 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. We can estimate the amount of cost associated with an investment strategy by looking at its annual turnover ratio. For example, a turnover ratio of 100% means that the portfolio manager turns over the entire portfolio in 1 year. This is considered an active approach and investors holding these funds in taxable accounts will likely incur a higher exposure to tax liabilities to short term and long term capital gains distributions relative to those incurred by passively managed funds.

The table below details the hard costs as well as the turnover ratio for all 9 strategies offered by Bridgeway that have at least 3 years of complete performance history. 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).

Fund Name Ticker Turnover Ratio % Prospectus
Net Expense
Ratio
Global Category
Bridgeway Managed Volatility BRBPX 54.00 0.95 Long/Short Equity
Bridgeway Small-Cap Growth BRSGX 137.00 0.94 US Equity Small Cap
Bridgeway Small-Cap Momentum N BRSMX 184.00 0.91 US Equity Small Cap
Bridgeway Ultra-Small Company Market BRSIX 41.00 0.84 US Equity Small Cap
Bridgeway Omni Tax-Managed Sm-Cp Val N BOTSX 29.00 0.60 US Equity Small Cap
Bridgeway Small-Cap Value BRSVX 62.00 0.94 US Equity Small Cap
Bridgeway Omni Small-Cap Value N BOSVX 24.00 0.61 US Equity Small Cap
Bridgeway Ultra-Small Company BRUSX 101.00 1.27 US Equity Small Cap
Bridgeway Aggressive Investors 1 BRAGX 124.00 0.63 US Equity Mid Cap

On average, an investor who utilized a strategy from Bridgeway experienced a 0.85% expense ratio. This can have a substantial impact on an investor’s overall accumulated wealth if it is not backed by superior performance. The average turnover ratio for equity strategies offered by Bridgeway was 84%. This implies an average holding period of about 14 months.  It is safe to say that Bridgeway makes investment decisions based on short-term outlooks, which means they trade quite often. Again, this is a cost that is not itemized to the investor, but is definitely embedded in the overall performance. In contrast, most index funds have very long holding periods--decades, in fact, thus deafening themselves to the random noise that accompanies short-term market movements, and focusing instead on the long term.

Performance Analysis

The next question we address is whether investors can expect superior performance in exchange for the higher costs associated with Bridgeway’s “skill.” We compare each of the 3 strategies that have at least 3 years of performance history and against its current Morningstar assigned benchmark to see just how well each has delivered on their perceived value proposition. We have included alpha charts for each strategy at the bottom of this article. Here is what we found:

  • 33% (3 funds) have underperformed their respective benchmarks since inception, having delivered a NEGATIVE alpha
  • 67% (6 funds) have outperformed their respective benchmarks since inception, having delivered a POSTIVE alpha
  • 0% (0 funds) have outperformed their respective benchmarks consistently enough since inception to provide 95% confidence that such outperformance will persist as opposed to being based on random outcomes

Based on the historical performance of their strategies, it seems that Bridgeway has done quite well in terms of delivering outperformance for their investors. 2 out of every 3 funds produced an average return above their benchmark. But there has been much variability around their “alpha,” leading all of their funds to have a statistically insignificant outperformance. In other words, we have a low degree of confidence that this outperformance will persist into the future. The inclusion of statistical significance is key to this exercise as it indicates which outcome is the most likely vs. random-chance.

Regression Analysis

How we define or choose or benchmark is extremely important, especially for funds that are targeting the known dimensions of expected return. If we relied solely on commercial indices assigned by Morningstar, then we may lead to the false conclusion that Bridgeway has the “secret sauce” as active managers. Because Morningstar is limited in terms of trying to fit the best commercial benchmark with each fund in existence, there is of course going to be some error in terms of matching up proper characteristics such as average market capitalization or average price-to-earnings ratio.

A better way of controlling for these possible discrepancies is to run multiple regressions where we account for the known dimensions (Betas) of expected return in the US (market, size, relative price, etc.). For example, if we were to look at all of the US based strategies from Bridgeway that have been around for at least the last 10 years, we could run multiple regressions to see what their alpha looks like once we control for the multiple Betas that we know are being systematically priced into the overall market. The chart below displays the average alpha and standard deviation of that alpha for the last 10 years ending 12/31/2016.

As you can see, for all Bridgeway strategies with at least 10 years of performance history, the entire alpha diminished once we controlled for risk exposure (Beta). Not a single fund produced an alpha that was statistically significant at the 95% confidence level (green shaded area). Why is this important? It means that if we wanted to simply replicate their risk exposure, we could do so more cost effectively through the use of index funds. Given the lower costs associated with index funds, we could have more confidence that we will experience a more desirable result compared to more expensive actively managed funds.

Comparison with Dimensional

We have shown that Bridgeway’s performance can be attributed to their overall exposure to the known dimensions of expected return. The last part of our analysis is to dissect their risk exposure in attempt to highlight the difference in approaches to targeting risk premiums.

First, if we were to compare the 3-factor regressions of Bridgeway’s Ultra Small Company Fund (BRUSX) and Dimensional’s Micro Cap (DFSCX) strategy over the 10-year period ending 12/31/2016, you will notice a trend. Dimensional’s strategies have more relative-price (HML) exposure compared to that of Bridgeway. They have similar size exposure (SmB), but Bridgeway has significantly more sensitivity to the overall market (MKT-B).

Data Series Symbol Annualized Return α t(α) MKT-B SmB HmL Adj R²
Bridgeway Ultra-Small Company BRUSX 4.24 -0.36% -1.51 1.14 0.89 0.05 0.88
DFA US Micro Cap I DFSCX 7.63 0.00% 0.05 0.99 0.91 0.30 0.98

Over the last 10-years, the value premium within small cap stocks has actually been negative, which should harm Dimensional's performance more since they have more exposure to the relative price factor. If we compared the performance of the Russell 2000 Growth Index to the Russell 2000 Value Index for the 10-year period ending 12/31/2016, you will see that small cap growth stocks have outperformed small cap value stocks by 1.50% per year.

Index 10-Year Return as of 12/31/2016
Russell 2000 Growth Index 7.76%
Russell 2000 Value Index 6.26%

The size premium (SmB) has actually been a wash over the last 10 years. The performance of the Russell 1000 Index and the Russell 2000 Index has almost been identical.

Index 10-Year Return as of 12/31/2016
Russell 1000 Index 7.08%
Russell 2000 Index 7.07%

Given these data points, we would expect Bridgeway’s Ultra Small Company Fund would have outperformed DFA’s Micro Cap Fund. Surprisingly, DFA outperformed Bridgeway by 3.39% per year. How could this be?

When Bridgeway targets risk factors, they do so in a very concentrated way, placing bets on companies they believe will outperform. Their current holdings indicate just over 100 different securities. This concentration comes with additional risk, most of which is idiosyncratic (firm specific risk), which is not expected to compensate investors with additional return. This explains why Bridgeway is much more sensitive to the overall market (MKT-B). 

On the other hand, Dimensional takes a much more diversified approach, which increases the reliability of capturing the premiums they are seeking in the market place. We have written on this topic before in our article Targeting Premiums & Diversification. The DFA U.S. Micro Cap Fund currently holds 1,546 different companies. This diversification has worked well for investors in terms of capturing the risk premiums both Bridgeway and Dimensional are seeking in their strategies.

We can do a similar analysis between the Bridgeway Small Cap Value Fund and the DFA Small Cap Value Fund over the 10-year period ending 12/31/2016. The regression results are below. We see that DFA has outperformed by 1.35% per year; although, DFA should have been penalized for their increased exposure to the relative-price factor, which didn’t provide a positive premium over this time. 

Data Series Symbol Annualized Return α t(α) MKT-B SmB HmL Adj R²
Bridgeway Small-Cap Value BRSVX 5.47 -0.20% -1.03 1.10 0.67 0.14 0.89
DFA US Small Cap Value I DFSVX 6.82 -0.05% -0.76 1.06 0.85 0.48 0.99

Conclusion

Like many of the other largest financial institutions, a deep analysis into the performance of Bridgeway has yielded a not so surprising result: active management is likely to fail many investors. We believe this is due to market efficiency, costs, and increased competition in the financial services sector. Although the vast majority of strategies offered by Bridgeway have outperformed their Morningstar assigned benchmark, this entire alpha can be explained away once we control for the known dimensions of expected return. Further, it is important to note that not all multi-factor investing is created equal. Bridgeway takes a more concentrated approach while Dimensional takes a more diversified approach. As we have shown, the diversified approach increases the reliability of capturing the premiums that both are seeking as concentration is likely to increase the overall risk of a strategy without additional expected return. As we always like to remind investors, a more reliable investment strategy for capturing the returns of global markets is to buy, hold, and rebalance a globally diversified portfolio of index funds.

 

Here are the individual alpha charts for the Bridgway funds we analyzed.

 

 

 

 

 

 

 

 

 


 

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.