Stock Line

Dodge & Cox: A Deeper Look at the Performance

Stock Line

San Francisco based Dodge & Cox is one of the most well-known investment management firms in the world. More impressively, they have been able to build a very sizeable asset base on the back of just 6 mutual funds. According to Morningstar, total assets under management as of year-end 2015 were just over $175 billion. Their investment strategy can be categorized as disciplined, long-term active management for a reasonable price.

We have recently taken a deep-dive into many of the largest and well-known investment management firms in order to address the value proposition they are positing on investors. Our intent is to shed light on what is becoming a universal and fundamental truth about the wide world of investing: beating the market is very difficult. So difficult that deciding to partake is nothing less than pure gambling. You can find analyses for fund companies such as:

Some investors may know this and decide to do so anyway, which is fine as long as they know the expected outcome. But many investors are having their money actively managed, whether it is in pension funds or the investment lineup in their 401(k)’s, and they are not aware of the consequences and tradeoffs. With the current reality of having the vast majority of an entire generation severely underfunded for retirement, it raises the question of whether or not our industry can and should continue to promote a false perception of how a successful investment experience should be understood.

Today, we take a deep dive into Dodge & Cox to see if they fall victim to the same fundamental truth.

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 “expertise,” 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 incurred by passively managed funds.

The table below details the hard costs as well as the turnover ratio for all 6 active funds offered from Dodge & Cox. 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).

Name Ticker Turnover Ratio % Prospectus Net Expense Ratio Global Category
Dodge & Cox Income DODIX 24.00 0.43 US Fixed Income
Dodge & Cox Global Bond DODLX 55.00 0.60 Global Fixed Income
Dodge & Cox International Stock DODFX 18.00 0.64 Global Equity Large Cap
Dodge & Cox Global Stock DODWX 20.00 0.63 Global Equity
Dodge & Cox Balanced DODBX 20.00 0.53 Moderate Allocation
Dodge & Cox Stock DODGX 15.00 0.52 US Equity Large Cap Value

On average, an investor who utilized an equity strategy from Dodge & Cox experienced a 0.58% expense ratio. Similarly, an investor who utilized a bond strategy from Columbia experienced a 0.52% expense ratio. These fees are substantially lower than the industry average, which increases the chance of potentially outperforming their respective benchmarks.

The next question we address is whether investors can expect superior performance in exchange for the higher costs associated with Dodge & Cox’s strategies. We compared each of the 6 strategies since inception 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.

  • 100% (6 funds) have outperformed their respective benchmarks since inception, having delivered a POSTIVE alpha
  • 0% 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 

Dodge & Cox is an interesting case since all 6 of their funds have delivered a positive alpha, on average, since each of their inceptions. This is quite impressive and speaks to why they have become one of the most prominent investment management firms in the world. But simply looking at the average doesn’t give the full picture of what many investors have experienced by investing in these strategies. Variability around the average is also important. Although the alpha has been positive, on average, there has been so much variability that we cannot determine whether or not it was an act of skill or simply an act of luck. This is where statistics play an important role in understanding investment performance. Not a single one of their strategies has produced a positive alpha consistently over time for us to have a high degree of confidence that it will persist into the future (t-stat>2).

Now we acknowledge that simply comparing a strategy against its Morningstar assigned benchmarks is not a robust analysis, since it doesn’t take into account certain risk factors. For example, a fund could have a higher market, size, or relative price exposure compared to its benchmark. These types of mismatches need to be accounted for if we are to truly compare performance. If not, then we may be falsely attributing “alpha,” where risk is the proper explanation. With the use of multiple regressions, we can adjust for these risk factors and create a “custom” benchmark that accounts for a fund’s exposure to known risk factors (betas).

Unfortunately, any analysis is only as good as the data inputs. Within the context of a multiple regression, we have known data sets for market, size, and relative price premiums going back to 1926 for US based companies. Therefore, we are able to run regressions for fund’s that have 90% or more of exposure to US based companies. If a fund happens to have more foreign exposure, then we start to run into issues of not having proper data sets to draw comparisons. In other words, we want to make sure we are comparing apples to apples and not apples to oranges. Dodge & Cox does not have a single equity fund that has 90% or more of its total stock exposure to US based companies.

Although the performance of Dodge & Cox’s entire investment lineup has been impressive, they have not been consistent enough to determine that their investment acumen is truly a skill. While some might think that having a t-stat greater than 2 is a very unreasonable threshold to compare performance, we beg to differ. As a fiduciary, if we are truly making recommendations that are in our clients’ best interest then we must be certain beyond a reasonable doubt that our recommendation is going to give them the greatest chance of capturing the returns the capital markets have to offer. Dodge & Cox can be added to the already mounting evidence that trying to beat the market is a fruitless endeavor.

 

 


 

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.