Baltimore based T. Rowe Price is amongst some of the most well known fund companies in the entire world. Like Fidelity or American Funds, we are never surprised to see at least one of their strategies in a prospect’s portfolio or retirement plan. Boasting over 100 different stock, bond, asset allocation, and money market funds, the company has offices around the world filled with close to 6,000 employees and is publicly traded on the New York Stock Exchange (TICKER: TROW).
Racking in over $3 billion in revenue from their investment management services, T. Rowe Price has clearly stated their value proposition to their investors. But what exactly is that value and, more importantly, who is receiving the bulk of that value? We have done similar analyses for companies such as Wells Fargo, MFS Investment Management, JP Morgan, Lord Abbott, USAA, Midas Funds, Thrivent, American Funds, Fidelity Part 1, Fidelity Part 2, and Oppenheimer Funds.
This is not an attack on the intelligence of the many skilled individuals who work at T. Rowe Price, but an understanding that there are many skilled individuals who collectively participate in the market. Given that no one can have all information at any given time, and because many investment professionals have different outlooks about particular economies and companies, it is virtually impossible for any one person or fund company to have insight beyond that which is reflected in the price of any given security. The price is the ultimate aggregator of current information and collective forward-looking sentiment.
As always, we begin our analysis by examining the costs associated with investing in strategies offered by T. Rowe Price. The costs we examine include expense ratios, front end and deferred 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 relatively to incurred by passively managed funds. The table below details the hard costs, as well as the turnover ratio, for all 111 T. Rowe Price funds that have at least 3 years of 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 insample test, the next level of analysis is to do an out-of-sample test. You can read about this test here.
On average, an investor who invested in a T. Rowe Price fund has experienced a 0.79% expense ratio and a 0.06% 12b-1 fee. These fees can have a substantial impact on an investor’s overall accumulated wealth if it is not buoyed by outperformance. The average turnover ratio is 52.80%, which implies an average holding period for each individual stock or bond of 2 years. This also implies that T. Rowe Price, on average, makes investment decisions based on short-term outlooks. In contrast, most index funds have very long holding periods, thus deafening themselves to the random noise that accompanies short-term market movements and focusing instead on the long term.
The next question we address is whether investors can expect superior performance in exchange for the higher costs associated with T. Rowe’s “expertise.” We compare each of the 111 strategies against its Morningstar assigned benchmark since inception 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:
- 46% (51 funds) have underperformed their respective benchmarks since inception, having delivered a NEGATIVE alpha
- 54% (60 funds) have outperformed their respective benchmarks since inception, having delivered a POSTIVE alpha
- 8.1% (9 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 (see next test)
In general, we conclude that the T. Rowe Price family of funds has no expectation of producing above-average returns for their investors beyond that of a random coin toss. The inclusion of analysis of statistical significance is key to this exercise, as it indicates which outcome is the most likely vs. random-chance outcomes.
Now, let's take a deeper look at the funds that produced a statistically significant alpha at a 95% confidence level.
The 9 funds that had a positive alpha at the 95% confidence level were:
- Global Multi-Sector Bond Fund (RPGMX)
- New Asia Fund (PRASX)
- International Concentrated Equity Fund (PRNCX)
- International Core Equity Fund (TRCEX)
- Global Value Equity Fund (PRIGX)
- Global Technology Fund (PRGTX)
- New Horizons Fund (PRNHX)
- Health Sciences Fund (PRHSX)
- Media & Telecommunications Fund (PRMTX)
In general, we can group these funds into 2 distinct groups. First, 4 of the 9 strategies have 7 years of performance history or less. This includes the Global Multi Sector Bond Fund, International Concentrated Equity Fund, International Core Fund, and the Global Value Equity Fund. Second, 5 out of the 9 strategies have multiple decades of performance history we can examine. We believe that we should dismiss the first group from the analysis given that limited number of data points for these particular strategies. Although some may see this at cherry picking, there are very real reasons why it isn’t a good idea to invest in a strategy that has only been around since the last financial crisis.
Most of these strategies have had a couple of years of extreme outperformance, which heavily skews both the average alpha and its subsequent t-statistic. For example, lthe International Concentrated Equity Fund (PRNCX) has 5 years of performance history, an average alpha of 5.46% and a t-statistic of 3.30. Given the limited amount of data points, it would take only one year in which the fund underperforms its benchmark by 4.50% to have the t-statistic go under the level 2 threshold. It doesn’t seem so ridiculous for a fund manager to have at least one bad year every few years and losing by -4.50% in the volatile equity markets is not that unrealistic. This is why it is important to have a very large sample size when doing statistical analyses. Investors could easily go down a wrong road by basing decisions off of very limited sample sizes. As the sample size grows, the threat of a single bad year threatening the overall statistical significance diminishes.
This brings us to the second group, which has a legitimate sample size and is therefore eligible for additional scrutiny. 3 out of the 5 funds are sector funds, which means that they are focusing on a particular industry within the entire market. Comparing a particular strategy to its Morningstar assigned benchmark is not a robust comparison. Morningstar attempts to find a commercial benchmark that “best fits” a particular strategy. However, we know that differences in stock returns are driven by factors outside of pure labels. Mainly, differences in stock returns have been explained almost completely by factors such as market, size, relative-price, and profitability. So, a fund that has more exposure to smaller, more value oriented companies with robust profitability[i] has the expectation of outperforming another fund or a benchmark with different exposures to these driving factors. This is where we need to go into our “statistical toolkit” to see if we can find a better way to compare performance results between a fund and a benchmark with similar stock exposure.
Utilizing multiple regressions allows us to develop a custom benchmark in which we can compare performance. Regressing the monthly returns of a particular fund against the market, size, and relative-price factors will allow us to control the variables that are systematic (i.e. priced factors) and then examine what the fund’s alpha and t-statistic for that alpha look like. For example, if we were to look at all of the US based strategies from T Rowe Price who have been around for at least the last 10 years, we could run multiple regressions to see what their alpha looks like. The chart below displays the average alpha and standard deviation of that alpha for the last 10 years ending 12/31/2015.
As you can see, not a single fund produced an alpha that was statistically significant at the 95% confidence level (green shaded area). This is what we would expect in a well functioning capital market.
Unfortunately, this type of statistical analysis is also limited. Our model is only as good as the data that we use as the independent variables in the regression. For example, if we were to run a regression for the T Rowe Price New Asia Fund, we would need a market, size, and relative-price factors for International stocks mainly focused in Asia. No such database currently exists at this point. The alternative would be to try to explain its performance using stock market information from the US (more robust data set), which doesn’t make a whole lot of sense since it is two completely different samples.
In a similar vein, trying to use multiple regressions for sector-based funds becomes problematic as well. Using a market-based factor to try and explain the variation in only a very small section of the market may create statistical problems. When we ran regressions for the sector-based funds from T. Rowe Price, we found that the model only explained about 50-60% of the variation (as measured by R-squared) in the sector-funds returns, which means the results from the model cannot be relied upon with a high degree of confidence (not a good fit). We are currently looking into alternate models to better analyze sector-based funds.
The reason we bring this up is because we would like to examine the alpha of the remaining 5 funds in the analysis, but we currently do not have the datasets to complete the analysis.
What does this mean for investors? We have shown that almost half of the T Rowe Price lineup has failed to deliver alpha, and only 9 of the funds have passed the first layer of examination in terms of determining whether or not its performance is truly a result of skill. 4 out of the 9 funds have a very limited performance history, which leads to highly suspect results (could be random), and the remaining 5 funds have an international or sector-based exposure, which currently doesn’t allow us to examine more in-depth.
It would be wishful thinking to believe that out of 111 different strategies, 5 have been a true display of skill. Why would this be? Are those 5 portfolio managers that much smarter or have more insight? Do they have better processes in place in terms of implementation versus the other managers at T. Rowe Price? In answer to all of theses questions, probably not!
There is not a sufficient amount of evidence that indicates a robust expertise, process, or track record that T. Rowe Price is a superior fund management company. In a global context, because we see a very similar trend across many major fund management complexes, general intuition would indicate that T. Rowe Price falls victim to a similar fate based on market efficiency, cost, and competition.
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 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.
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:
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About the Authors
Tom Allen
Tom Allen is an Accredited Investment Fiduciary (AIF®), Certified Cash Balance Consultant (CBC) and a Chartered Financial Analyst (CFA®) Level III Candidate. Tom received his Bachelor of Science in Management Science as well as his Bachelor of Art in Philosophy from the University of California, San Diego.
Mark Hebner - Founder, Index Fund Advisors, Inc. Â
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.