o add value we don’t have to analyze companies or forecast the future, as long as we have a disciplined strategy that can capture a superior risk premium.1

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UV5669 Rev. Mar. 13, 2012
This case was prepared by Assistant Professor Richard B. Evans with the assistance of Richard Green. It was written as a basis for class discussion rather than to illustrate effective or ineffective handling of an administrative situation. Copyright  2012 by the University of Virginia Darden School Foundation, Charlottesville, VA. All rights reserved. To order copies, send an e-mail to sales@dardenbusinesspublishing.com. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means— electronic, mechanical, photocopying, recording, or otherwise—without the permission of the Darden School Foundation.
RESEARCH AFFILIATES
To add value we don’t have to analyze companies or forecast the future, as long as we have a disciplined strategy that can capture a superior risk premium.1
—Research Affiliates Chairman Robert D. Arnott
Sitting in the conference room of the Towers Watson (TW) London office, Philip Tindall thought about that morning’s presentation from Jason Hsu, CIO of Research Affiliates (RA). Hsu had discussed an innovative investing concept called the “Fundamental Index methodology.” Over many years, TW has conducted its own research into alternative approaches to market cap investing and already had a positive view. While Tindall was impressed by the presentation and thought that the investment strategy suggested by RA might be an important innovation in applying nonmarket cap approaches, he had some concerns about the approach and whether or not it would be appropriate for TW clients. As an investment consultant to some of the largest pension funds and endowments in Europe and the world, when TW spoke, investors listened. At the same time, although the clients depended on TW for keeping them on the cutting edge of institutional investing, recommending an untried investment strategy and deviating from status quo investment practice could either generate outperformance relative to their investment consulting competitors, thereby attracting new clients, or it could result in underperformance and defection of their clients to those competitors. Towers Watson
As a senior investment consultant with TW, Tindall’s job was to stay abreast of the latest investment product developments, ensuring that his colleagues were equipped to provide clients with the most informed assistance on their asset allocation and manager selection decisions.
1 Pauline Skypala, “Rob Arnott: Fundamental Pioneer,” Financial Times, June 19, 2011.
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While TW was a global professional services company with offices in 37 countries and more than 14,000 associates worldwide, its actuarial origins and early focus on pension plans had made it a leader in institutional and retirement plan investment consulting. The reason the investment consulting industry had arisen was due to both economies of scale and fiduciary duty.
With the increased complexity and thousands of managers to evaluate, working with an investment consultant was often the most efficient way for the pension funds, endowments, and institutional investors to go. Institutional investors could purchase more in-depth analysis from TW than they could on their own and at a much lower cost than if they had to conduct the analysis themselves. Additionally, pension fund trustees and directors relied on the investment advice of consultants to help them satisfy their fiduciary responsibility to plan participants. Investment consultants generally were far more familiar with the diverse ranks of money managers, providing critical oversight on firms’ investment processes, personnel, and operations, in addition to performing the rigorous analysis typically beyond the capability and purview of a trustee. Birth of the Fundamental Index Idea
The Fundamental Index (FI) idea can be traced to the build-up and subsequent bursting of the 1990s tech stock bubble. In March 2000, equity indexes were at record highs. In many investors’ view, market valuations were too high and not supportable by the future earning potential of those firms. Nevertheless, skyrocketing prices meant skyrocketing market capitalizations and, as a result, market cap-weighted indices such as the Standard and Poor’s 500 (S&P 500) took larger and larger positions in the highest multiple stocks. As Hsu’s presentation showed (Exhibit 1), in March of 2000, tech and telecom stocks such as Cisco, Nortel, Nokia, and Ericsson had much larger market cap weights in their respective indices (i.e., percentage in Russell 1000, FTSE Canada, FTSE Finland, FTSE Sweden) than their firm fundamentals suggested (i.e., relative fundamental size). For example, while Cisco was 4.09% of the Russell 1000 due to its enormous market capitalization, it only accounted for an average of 0.20% of the aggregate sales, cash flow, dividends, and book value for all 1,000 stocks in the index. By holding portfolios that were similar to the S&P 500, the Russell 1,000, or other market cap- weighted indices, institutional investors were effectively betting that these higher stock prices were justified in spite of the companies’ fundamentals.
By March 2002, the tech bubble had burst. As Hsu explained in his presentation, the collapse of the tech bubble devastated cap-weighted passive investors, particularly those who invested near the top of the valuation bubble. Given his reputation for problem solving and research, several investment professionals challenged Rob Arnott, founder and chairman of RA,
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to find a way to index without overweighting the overvalued companies.2 In 2003, RA embarked on a research project to determine whether there was a better way to design an index for passive investors. The Fundamental Index Approach
While most pension funds, endowments, and other institutional investors still relied significantly on actively managed investments, many of them had a significant portion of their portfolios invested in index funds, exchange traded funds (ETFs), or other passive vehicles. As a firm, RA agreed with the benefits of passive investing (highlighted in Hsu’s presentation shown in Exhibit 2), but its rationale differed from the traditional market cap-weighted approach. While proponents of passive investing often touted the efficiency of markets—that stock prices accurately reflected firm valuation—as the reason to index, Tindall was surprised that the view of RA regarding markets sounded more like the investment philosophy of an active value manager. Not only did RA believe that market prices were not always efficient, but the firm also used firm fundamentals such as sales, cash flow, dividends, and book value to establish the portfolio weights. Hsu had explained that RA favored an economy-centric view of the world compared to the market-centric view favored by most indexers (Exhibit 3).
Believing that pricing errors existed, RA wanted to design an approach that preserved the attractive attributes of passive investing while breaking the link with price in the selection and weighting process. Most business owners quantified the economic scale of a company using financial measurements of size such as sales, profits, net assets, or the number of employees and not by market capitalization, Hsu had explained. For example, RA ranked companies from largest to smallest by sales and selected the largest 1,000 for inclusion in a sales index. Weights were assigned to individual companies by calculating the percentage of total sales of all 1,000 firms contributed by each individual firm. RA calculated the return on a buy-and-hold portfolio that was rebalanced annually. Compared to the return on the traditional cap-weighted S&P 500 index, RA found the sales-weighted portfolio outperformed by 2.6%. Repeating the exercise for cash flow, gross dividends, and the book value of assets, RA found that in each case selecting and weighting the stocks using firm fundamentals resulted in outperformance relative to the market cap-weighted index.
In his presentation, Hsu showed the results for the universe of the 1,000 largest U.S. stocks (Exhibit 4). While the S&P 500 and the Cap-1000 (a market-weighted portfolio of the 1,000 largest U.S. stocks by market capitalization) had an average annual return of 9.3% from 1962 to 2009, the fundamentally weighted portfolios had returns ranging from 10.9% to 11.9% over that same period. Hsu had pointed out that the outliers were the cap-weighted indices.
2 Arnott and Hsu credit their friends, George Keane, a trustee of New York State Common Retirement Fund,
and Martin Leibowitz, CIO at TIAA-CREF, for first encouraging them to examine an alternative weighting scheme for indices.
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Recognizing that current data might retain some links with price, RA chose to average each firm’s sales, cash flow, and dividends over five years. As Hsu had noted in his presentation (Exhibit 5), using five years of “stale” data had the added benefits of smoothing peaks and valleys in financial data and reducing exposure to firms with aggressive accounting.
While each of the single-metric fundamentally weighted portfolios outperformed individually, RA realized that there were good reasons to combine the different metrics into a composite (Exhibit 6). The composite portfolio equally weighted the four fundamental metrics: book value, cash flow, sales, and dividends.3 Exhibit 7 gives an example of the five stocks with the highest Research Affiliates Fundamental Index (RAFI) weights taken from the universe of the 1,000-largest U.S. equities by market cap: Exxon Mobil, General Electric, Bank of America, Wal-Mart, and Microsoft (market-cap weights and ranks are given as well). Implementing the RAFI Concept
The business model of RA was highly unusual within the investment industry. Most investment advisors worked hard to keep their investment formula a well-kept secret, and they generated revenues from their advisory fees on assets managed directly. RA chose to patent its ideas and then make its approach as transparent as possible. In 2005, Arnott and Hsu, along with another colleague, Tim Moore, published the details of their investment strategy in the Financial Analysts Journal,4 and they contracted with the FTSE RAFI Index Series (FTSE) to calculate and license the methodology as a strategy index with full transparency. Late in 2005, Invesco PowerShares launched the first ETF tracking the FTSE RAFI US Large-Cap strategy (ticker PRF). Within several years, numerous partners licensed the methodology from FTSE or RA. According to Michael Larsen, director of affiliate relations at RA, the firm’s strategy was “to identify and develop great investment products. In short, we focus on research, and then we partner with others to bring those investment ideas to market.”5 RA managed some FI strategy assets directly, but the majority of its assets were managed by third parties, and the company received an asset-based license fee for the use of its intellectual property (indices) and the data provided. During his presentation, Hsu had commented that the FI had garnered assets rather quickly for a new idea. By the end of 2009, assets managed using FI methodology had grown to $29.1 billion (Exhibit 8), and the strategies spanned most global markets (Exhibit 9).
For Phil Tindall, the simulated FI results positively confirmed the effectiveness of the strategy but did not extend to trading costs, management, administrative and other fees, and any other implementation costs. To help validate Hsu’s results, Tindall had compiled data on a
3 It was decided to drop the employment metric because of the lack of consistent company data. The revenue metric was also dropped due to its high correlation of results with the income and sales portfolios and, therefore, the relatively low value added.
4 Robert D. Arnott, Jason Hsu, and Philip Moore, “Fundamental Indexation,” Financial Analysts Journal (March/April, 2005): 86.
5 From an interview with Rich Evans at RA in late 2005.
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publicly traded FI product with the longest record—the PowerShares FTSE RAFI US 1000 ETF. He compared his information with a cap-weighted analogue, the iShares Russell 1000 ETF (Exhibits 10, 11, 12, 13, and 14). He also accessed the returns of the two ETFs and several relevant benchmarks (Exhibit 15) that would be useful in his analysis. The Decision
In advance of Hsu’s presentation, Tindall had researched the RAFI strategy, reviewing various press and journal articles as well as speaking with colleagues and other investment professionals. Now it was time to organize his thoughts before deciding whether to recommend that his colleagues offer this strategy to clients.
From his research, three concerns seemed important to address. First, what exactly was this strategy? He had run across significant criticism about calling a fundamentals-based strategy an index. To these skeptics, only cap-weighted strategies should be called an index, because the market itself was cap-weighted. The FI strategy appeared to lie somewhere between traditional passive investment vehicles and an actively managed fund.
Second, while the performance for the PowerShares FTSE RAFI US Large ETF seemed impressive, he believed that the recent live performance was due, in part, to luck. Unlike cap- weighted indexes, an index ranked by fundamentals needed to rebalance as the fundamentals changed over time. To index purists, one of the benefits of the cap-weighted approach was that the weights in the index automatically adjusted without the need to rebalance. Consequently, turnover for cap-weighted indices was relatively low. Rebalancing for the FI, however, required adjusting the weights of the securities back to their fundamentals, which meant potential adjustments for each stock, not just the housekeeping associated with replacing stocks that left the index. As cash flows, sales, book value, and dividends changed, a company’s weighting in the FI also changed. Tindall noted that the FTSE RAFI series was rebalanced annually on the third Friday of March. In 2009, the rebalance date had been extremely fortuitous for the FTSE RAFI series. He wondered whether this one event determined the strategy’s outperformance or if the strategy was, in fact, a robust design that would perform well over time.
Third, one of the more quantitative managers Tindall spoke with suggested that the observed FI outperformance was due to the additional risk inherent in the strategy or other well- known investment phenomena. Tindall was aware of the academic papers by Fama and French6 and by Jegadeesh and Titman7 that the manager referenced. Fama and French documented that both small market cap and value (as measured by the book-to-market ratio) stocks outperformed
6 Eugene Fama and Kenneth French, “Common Risk Factors in the Returns on Stocks and Bonds,” Journal of
Financial Economics 33 (1993): 3–56. 7 Narasimham Jegadeesh and Sheridan Titman, “Returns to Buying Winners and Selling Losers: Implications
for Stock Market Efficiency,” Journal of Finance 48 (1993): 65–91.
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large market cap and growth stocks on average. Similarly, Jegadeesh and Titman documented a return momentum effect where stocks that had better past returns as measured over the previous 3 to 12 months, continued to outperform for the following 12 months. Tindall knew that the Fama-French and Jegadeesh-Titman view that the return premiums from investing in value, small-cap, and momentum stocks were simply compensation for taking the additional risk inherent in these strategies was not universally accepted. He did think it was possible, however, that the RA weighting scheme would emphasize small cap and value stocks relative to large cap and growth stocks but that it might avoid momentum stocks due to the focus on fundamentals and away from price. Was the strategy’s outperformance just a value/size premium? And how did it relate to the momentum effect? With the presentation and the ETF data in hand, Tindall began his analysis.
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Exhibit 1
RESEARCH AFFILIATES
Source: Research Affiliates; used with permission.
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Exhibit 2
RESEARCH AFFILIATES
Source: Research Affiliates; used with permission.
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Exhibit 3
RESEARCH AFFILIATES
Source: Research Affiliates; used with permission.
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Exhibit 4
RESEARCH AFFILIATES
Source: Research Affiliates; used with permission.
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Exhibit 5
RESEARCH AFFILIATES
Source: Research Affiliates; used with permission.
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Exhibit 6
RESEARCH AFFILIATES
Source: Research Affiliates; used with permission.
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Exhibit 7
RESEARCH AFFILIATES
Source: Research Affiliates; used with permission.
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Exhibit 8
RESEARCH AFFILIATES RAFI Managed Asset Growth since Inception (in billions of dollars)
Source: Research Affiliates; used with permission.
$0.1 $0.9 $5.2
$16.0 $17.5
$29.1
0
5
10
15
20
25
30
35
Dec ’04 Dec ’05 Dec ’06 Dec ’07 Dec ’08 Dec ’09
Bi lli on
s ( U S$ )
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Exhibit 9
RESEARCH AFFILIATES RAFI Managed Assets by Market Exposure (in millions of dollars and %)
December 2008
December 2009
Data source: December 2009 data based on estimates and includes RAFI and eRAFI assets managed or subadvised by RA and RAFI licensees; used with permission.
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Exhibit 10
RESEARCH AFFILIATES ETF Summary Information
PowerShares FTSE RAFI US 1000
Ticker PRF Inception date 12/19/2005 Net expense ratio 0.39% Total net assets ($ millions) $527 Turnover ratio 15% Managers
Joshua Betts/John Browning/Peter Hubbard/Michael Jeanette/Brian Picken
iShares Russell 1000
Ticker IWB Inception date 5/15/2000 Net expense ratio 0.15% Total net assets ($ millions) $5,034 Turnover ratio 8% Managers Diane Hsiung/Greg Savage
Definitions:
Net Expense Ratio: The percentage of total net assets of the fund paid to the investment adviser annually to cover advisory, distribution, and other administrative expenses. This does not include brokerage costs and other direct costs incurred from trading—such costs are captured in the net prices at which a fund transacts and are therefore reflected in fund returns.
Turnover: The minimum of either fund purchases or fund sales divided by the average total net assets of the fund over the period. This is a measure of trading activity.
Source: Morningstar Direct, August 29, 2011.
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Exhibit 11
RESEARCH AFFILIATES Top 10 ETF Holdings by Portfolio Weight
PowerShares FTSE RAFI US 1000*
Name Ticker Portfolio Weight
1 Citigroup Inc. C 8.37% 2 Bank of America Corporation BAC 6.40% 3 General Electric Co GE 2.29% 4 Ford Motor Co F 1.83% 5 JPMorgan Chase & Co JPM 1.68% 6 Avis Budget Group, Inc. CAR 1.51% 7 Exxon Mobil Corporation XOM 1.42% 8 Wells Fargo & Co WFC 1.33% 9 Invesco Ltd. IVZ 1.20%
10 Microsoft Corporation MSFT 1.19%
iShares Russell 1000**
Name Ticker Portfolio Weight
1 Exxon Mobil Corporation XOM 3.33% 2 Microsoft Corporation MSFT 1.88% 3 Johnson & Johnson JNJ 1.65% 4 JPMorgan Chase & Co JPM 1.61% 5 Procter & Gamble Co PG 1.56% 6 International Business Machines Corp IBM 1.54% 7 AT&T Inc. T 1.52% 8 Bank of America Corporation BAC 1.50% 9 Apple, Inc. AAPL 1.48%
10 General Electric Co GE 1.45%
* Holdings reported on Oct. 31, 2009 ** Holdings reported on Sept. 30, 2009
Data source: Morningstar Direct, August 29, 2011.
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Exhibit 12
RESEARCH AFFILIATES ETF Portfolio Characteristics (value-weighted across the ETF’s portfolio holdings)
RAFI 1000
Russell 1000
Average market cap (in billions of dollars) 20.8 33.2 Price to book ratio 1.25 2.08 Price to cash flow ratio 3.45 6.22 Price to earnings ratio 14.22 14.88 Price to sales ratio 0.49 0.94 Debt to capital ratio 45.26 35.69 Dividend yield 3.35% 2.64%
Definitions
Average market cap: The total value of a firm’s equity calculated as the product of the stock price and the number of outstanding shares.
Price to book ratio: The ratio of a firm’s stock price to its book value of assets.
Price to cash flow ratio: The ratio of a firm’s stock price to its cash flow from operations.
Price to earnings ratio: The ratio of a firm’s stock price to its reported earnings.
Price to sales ratio: The ratio of a firm’s stock price to its reported sales.
Debt to capital ratio: The ratio of a firm’s long-term debt (excluding other liabilities) to its total capitalization (common and preferred equity plus long-term debt).
Dividend yield: The ratio of a firm’s stock price to its annual dividend. Data source: Morningstar Direct, August 29, 2011.
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