Growth Investing with a Distinct Perspective
By Aziz Hamzaogullari
January 28, 2011
VARIATION ON A THEME
Take what you know about large cap growth investing and set it aside for a moment. In its place, consider a growth strategy with a long-term horizon whose primary focus is on investing in businesses versus trading stocks, and seeks to create a diversified 30-40 stock portfolio. What are the implications of such a strategy in the growth stock universe? In the case of the Loomis Sayles Large Cap Growth portfolio from July 2006 to September 2010,* it translated into a Sharpe Ratio of 0.22, an information ratio of 0.67, an annualized gross return of 6.52% (net return 5.92%) and an annualized turnover of roughly 25% (see Endnote1). Its Sharpe Ratio ranked the portfolio in the first percentile, the gross return figure in the second and the information ratio in the third.
PRIVATE EQUITY INVESTOR
As portfolio manager of the Loomis Sayles Large Cap Growth discipline, I make buy and sell decisions as a private equity investor might–investing as if I am a partner in the business. This approach colors all aspects of my thinking when I weigh a company’s prospects.
My team and I look for three major characteristics in each prospective investment: quality, sustainable and profitable growth and attractive valuation. The following discussion outlines how focusing on quality and intrinsic value in the large capitalization growth universe can help generate a portfolio with growth characteristics, attractive returns and moderate or lower volatility (see Endnote1).
Our investment process begins with the art of trying to identify companies that have difficult-to-replicate business models. We believe such companies have the potential to generate profits through their competitive advantages over a sustainable period. These entities can exhibit healthy balance sheets and strong returns on invested capital; and in many instances, we have found they have solid management teams focused on efficient allocation of capital. In our opinion, long-term significant exposure to companies with these characteristics can have positive implications for a portfolio’s risk-adjusted performance over time.
The following table and chart support this belief. The data is based on Standard & Poor’s Quality Rankings of common stocks, which “attempt to capture the long-term growth and stability of earnings and dividends.” To show how quality can influence performance and volatility over time, our Quantitative Research Risk Analysis group (QRRA) applied the S&P’s quality rankings to stocks in the Russell 3000 Index. They used this index as a proxy for the broad market and split its holdings into two groups, “high quality” (companies rated A-minus or better by S&P) and “low quality” (rated B to D & Liquidation). In keeping with the S&P’s methodology, companies rated B+ fell into the “average” category and therefore were not included in either group. Approximately 25% of the companies fell into the high-quality category and 54% into the lower tier, on average. The high-quality and the low-quality equal-weighted baskets generated the following returns and standard deviations.
While the annualized performance of the two baskets was comparable after 25 years, the risk-adjusted performance of the high-quality basket of stocks was 55% better than the low-quality basket.
While the S&P’s Quality Rankings can provide an interesting overview of how a “quality” universe hasperformed historically, we do not rely on a third-party methodology to define “quality” or our investment universe. We use many qualitative inputs in defining the quality of a business and our universe. Though we begin our process with quality, there are two other requisite characteristics: sustainability of profitable growth and attractive valuation.
A high-quality company, generating attractive returns on capital, typically attracts competition. Therefore, durability of competitive position, or a wide “economic moat,” can be crucial for protecting a business model that generates long-term cash-flow growth. Morningstar rates companies based on their “economic moats.”(Endnote 2) As of September 30, 2010 there were 465 unique large cap growth funds in the Morningstar database. Of those, only 18, or a mere 4%, were classified in the “wide moat” category. The holdings in the Loomis Sayles Large Cap Growth Composite would put it in that group, reflecting our focus on these types of companies.
Credit Suisse captured this notion of sustainable returns by applying its proprietary measures of “quality” to identify companies that continue to earn superior cash-flow return on investment (CFROI®) over a period that is longer than anticipated by the market. Such quality, sustainable CFROI companies significantly outperformed the market during downturns while keeping pace during up markets.(Endnote 3) (This experience provides no guarantee of future results.)
LIMITING DOWNSIDE MARKET PARTICIPATION
To follow up on the Credit Suisse research, we examined the annual performance for the high-quality and low-quality groups we had created and compared it to the Russell 3000’s returns. Our analysis showed that the high-quality group of companies’ limited participation in down markets was a significant differentiating factor for superior risk-adjusted returns. In down markets, characteristics of high-quality companies such as sustainable cash-flow growth, lower leverage and favorable returns on invested capital may have contributed to their lower fundamental risk and better stock performance.
This is an important factor, since during our 25-year study period, the Russell 3000 had the following negative-return profile:
In an analysis of 361 large cap equity portfolios by eVestment Alliance from July 2006 to September 2010, the Loomis Sayles Large Cap Growth composite exhibited a similar pattern as the high-quality companies cited above. When the portfolio’s Benchmark, the Russell 1000 Growth Index, had a quarterly return of less than zero during the composite’s tenure (4 ¼ years ending 9/30/10), the portfolio’s quality bias resulted in it capturing, or participating in, only 81% of these returns. (See Endnote 1) This translated into a “downside market capture” that was lower than 93% of the portfolio’s peers. The median manager had a 100% participation rate in the market’s negative return. To complete the picture, the portfolio had an “upside market capture” of 105%, which was higher than 71% of its peers. This is in contrast to the median manager’s 98% participation in the Benchmark’s positive returns. This highlights the importance of having quality as one of the main characteristics we look for in a company.
When we find a company that we believe has a foundation for a strong and sustainable cash-flow growth rate, we estimate its intrinsic value by modeling the present value of its future cash flows using fundamental drivers within a scenario analysis that establishes the best, base, bear and worst cases. We perform an expectations analysis to determine what is embedded in a company’s stock price and how our thesis differs from the market. We look for situations where we believe there is a disconnect between the fundamentals and embedded expectations.
Armed with this information, our team determines an entry point for investing in a company. This we believe offers an opportunity to buy a business at a significant discount to our estimate of intrinsic value and creates a position with the potential to generate alpha in the long term, with limited downside. By maintaining a sufficiently long investment horizon, we seek to capitalize on the market’s short-term mentality and capture the secular sustainable growth we anticipate.
We believe our principal value added is using rigorous bottom-up research to identify quality companies that we estimate can maintain or increase their competitive advantages in the future and buying them at a significant discount to our estimate of intrinsic value. We believe this crucial process requires the art of anticipating and projecting a company’s future capabilities, a step that cannot be achieved with the use of screening programs based on historical data alone.
A RARE FUSION
Growth stocks with the profile we are looking for can be few and far between. We spend substantial time on research in an attempt to gain an analytical edge in our investment decisions. Our analysis incorporates a long investment horizon. In a typical year, we may analyze 30 companies and invest in only a select few. For example, in 2009 we bought four companies and in 2010 we purchased three. As a result, our growth portfolio is composed of our high-conviction ideas–typically 30 to 40 of them.
Can such a portfolio be sufficiently diversified? A recent Citigroup analysis showed that a portfolio of 30 stocks was able to explain more than 85% of the market’s risk. The marginal benefit of diversification from adding more stocks to the portfolio declined significantly as the number of stocks increased. For example, by adding 70 more stocks to the 30 stock portfolio (total 100 stocks), the “percent of risk explained” improved by just 9%.
We believe our bottom-up fundamental approach can achieve prudent diversification while generating excess returns. We attempt to diversify the portfolio by company-specific business factors. Consistent with our philosophy, a vast majority of portfolio risk and excess return stems from security selection. This can be seen in the following pie chart, which shows contribution to cumulative excess return for the Large Cap Growth portfolio relative to its Benchmark, the Russell 1000 Growth Index. (Endnote4 contains related definitions.)
A Long Investment Horizon: An Element of Low Turnover
A long investment horizon, a central part of our philosophy, is reflected in the strategy’s low turnover of less than 25%.(Endnote 5) In our view, a long investment horizon affords us the opportunity to capture value from secular growth opportunities as well as capitalize on the stock market’s short sightedness. We also believe this long-term framework and approach allows us the time it takes to uncover rare companies and do in-depth research.
This characteristic of our growth strategy stands out in the escalation of portfolio turnover seen in the market and documented by John Bogle in his book Common Sense on Mutual Funds. “From the 1940s to the mid-1960s, annual portfolio turnover of the typical general equity fund averaged a modest 17 percent. In 1997, average portfolio turnover of US equity funds stood at 85 percent, an amazing fivefold increase.” He stated, “the industry has abandoned the wisdom of long-term investing in favor of the folly of short-term speculation.” I could not agree more.
Multiple empirical research studies(Endnote 6) have been conducted to quantify the impact of portfolio turnover on performance. Mark Carhart, in his paper On Persistence in Mutual Fund Performance, tested the performance of three mutual fund strategies (aggressive growth, long-term growth and growth-and-income) using a database, free of survivorship bias, from January 1962 through December 1993. He found a turnover slope coefficient of -0.95, suggesting that for every 100-point increase in turnover, the annual return drops by 95 basis points (which he interprets as the net cost of trading).
In addition to trading cost benefits, low turnover can also contribute to net outperformance as well as lower return standard deviations. In his 2007 article The Pre-Tax Costs of Portfolio Turnover,(Endnote 7) David Blanchett updated existing research and demonstrated the relationship between higher net outperformance and lower turnover as well as lower standard deviation and lower turnover. The table below shows his findings for the Large Cap Equity Group, which had an average of 990 funds 2001 through 2006.
In this paper we outlined the distinct elements of our process and philosophy to show how the Loomis Sayles Large Cap Growth discipline takes the traditional definition of a growth strategy and seeks to infuse it with a quality and valuation focus. These preferences play out in our focus on finding companies with sustainable cash-flow growth and profitability as well as intrinsic value. We believe this helps us exploit opportunities offered by growth companies while tempering the return volatility often associated with growth investing. Our consistent long-term approach has generated a high-growth portfolio, as can be seen in the Morningstar style map below and the portfolio characteristics table in Endnote8.
1. Past performance is no guarantee of future results.
Turnover data is for the Large Cap Growth Composite and was calculated by Loomis Sayles. Gross return is net of trading costs. Net return is gross returns less the effective management fees.
Rankings used in the first paragraph are from eASEAnalytics System; eVestmentAlliance as detailed below.
2. Morningstar defines “Economic Moat” as a company’s ability to keep competitors at bay for an extended period. They base the rating on “a company’s competitive advantages, such as large market share or above-average returns on capital over an extended period of time…not only must a company’s historical financials have to demonstrate a moat, but we also have to be confident that its competitive advantages are sustainable well into the future.”
3. According to Credit Suisse, an average firm loses 10% of its excess return (CFROI minus long-term required rate of return) per year. However, some firms are able to earn stable CFROI for an extended period of time. As such, eCAPs experienced only half the rate of deterioration of excess return when compared to a typical industrial firm.
4. Risk Factors refers to returns attributed to Betas arising from active exposure to Northfield Information Service’s version of Beta and risk factors such as price to earnings, prices to book, etc. Industry Factors are from returns attributed to active exposures to industries as measured by Northfield. Transaction Effect: Northfield takes holdings at the beginning of the month and calculates the portfolio’s gross return and then applies factors. Any inter-month changes in holdings are not captured as stock or factor specific returns but are attributed to Transaction Effect. The Stock Specific portion is a reflection of the manager’s stock selection ability.
5. Estimated annualized turnover for the Large Cap Growth Composite as of 12/31/10. Source: Loomis Sayles.
6. Research on Portfolio Turnover
7. The author, David Blanchett, sourced the data for his paper from Morningstar. The author noted that the “style used for comparison purposes was the investment’s asset category at the end of each calendar year as defined by Morningstar.”
The standard Benchmark for the Large Cap Growth Composite is the Russell 1000 Growth. The S&P 500 is shown for informational purposes only. Characteristics are shown for the date indicated and will evolve over time. Estimated P/E and 3-5 year EPS growth data are weighted averages derived from consensus earnings estimates provided by First Call.
• The Russell 1000 Growth Index measures the performance of the large cap growth segment of the US equity universe. It includes those Russell 1000 companies with higher price-to-book ratios and higher forecasted growth values.
• The Russell 3000 Index measures the performance of the largest 3000 US companies representing approximately 98% of the investable US equity market.
• The S&P 500 Index includes 500 leading companies in leading industries of the US economy, capturing 75% coverage of US equities.
· Standard Deviation is a measure of the dispersion of a set of data from its mean. The more spread apart the • data, the higher the deviation.
· Information Ratio is a ratio of portfolio returns above the returns of a benchmark relative to the volatility of • those returns.
· Sharpe Ratio is calculated by subtracting the risk-free rate–such as that of the 10-year US Treasury • bond–from the rate of return for a portfolio and dividing the result by the standard deviation of the portfolio returns.
(c) Loomis Sayles