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Risk / Return of Equity Portfolio Construction Strategies
Practitioner Demand Driven Academic Research Initiative (PDDARI)
Drafted by the CFA Society of Chicago
Request for Research Abstract
Measure and compare the risk / returns of major equity portfolio construction
strategies currently in use by the practitioner community. Include free float cap
weighted indexation, fundamental indexation, multi-factor, and DCF. Develop mental
models of the market to reflect empirically observed behavioral finance anomalies
and market inefficiency within a range of bounded rationality.
The primary Society practitioner “thought partners” have performed relevant
research and illustrative data analysis on stock selection, portfolio construction, and
risk measurement. Consequently, please study those practitioner citations and
disclosures, as well as the traditional academic literature. As a starting point, these
qualified “thought partners” have suggested research designs and measurement
methodologies as one possible approach. Please revise, modify, or replace the
suggested research designs and measurement methodologies based on the primary
objectives of the study if better ones exist. We seek the best approaches and the
most qualified academics to perform the research.
The academics selected may publish wherever they wish. We seek no influence over
the results.
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Risk / Return of Equity Portfolio Construction Strategies
Practitioner Demand Driven Academic Research Initiative (PDDARI)
Drafted Primarily by Rawley Thomas, 1 Mike Lindh,2 Amit Dugar,3 and Ralph
Goldsticker4
For the CFA Society of Chicago
Request for Research
Goal. To measure the risk / returns of major equity portfolio construction strategies
currently in use by the practitioner community.
Introduction. Much academic research for the past half century has been dedicated
to proving the efficient market hypothesis (EMH). Over the last one to two decades,
behavioral finance professionals have begun to offer evidence of anomalies which
conflict with EMH. Consequently, this paper offers an economic rationale that market
inefficiencies may exist. That rationale bases itself on the violation of the assumption
of homogeneous expectations due to market segmentation by risk and return.
Investors seek the highest rates of return on their investment with the least risk.
They hire portfolio managers if they feel that they personally lack the professional
expertise or the time to achieve their risk/return objectives. Portfolio management
organizations develop products to fill the risk/return needs of investor segments.
Four Strategies. Perhaps four major classes5 of strategies of portfolio construction
and stock selection exist, depending on investor beliefs surrounding efficient
markets:
1. Free float cap weighted indexation (including Exchange Traded Funds) with
minimum management costs for those who believe beating the efficient
market is impossible. (Passive strategy)
2. Fundamental indexation based on non-cap weights (sales, assets,
employment, dividends, etc.) with small management costs for those who
believe that the market experiences some inefficiencies without knowing
precisely what they are, but wishing to avoid overweighting over valued
stocks and underweighting under valued stocks. (Passive strategy)
3. Multi-Factor models for those who believe the market is sufficiently inefficient
that out-performance can be achieved by regressing stock price returns
against a series of factors. (Active strategy)
4. Discounted Cash Flow models for those who believe the market is sufficiently
inefficient that out-performance can be achieved by estimating the stock’s
intrinsic value and assuming that the market will migrate toward that intrinsic
value over some time period. (Active strategy)
President and Co-Founder of LifeCycle Returns. Responsible for structure of this Research Request for
Proposal and the DCF section. For clarification, e-mail [email protected] (preferable) or call 630-3770761.
2
President of the CFA Society of Chicago; Director of Richards and Tierney. Responsible for fundamental
indexation. For clarification e-mail [email protected] (preferable) or call (312) 461-1100.
3
Performs research for the Schwab equity ratings department. The Schwab equity ratings model is a
multi-factor model consisting of 15 factors. Responsible for multi-factor models. For clarification e-mail
[email protected] (preferable) or call (312) 931-1522.
4
Managing Director of Research of Mellon Capital Management in San Francisco, CA. For clarification,
please e-mail [email protected] (preferred) or call (415) 975-2383.
5
A variety of less disciplined strategies exist: for example, simple stock pickers. Hybrids also exist.
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Mental Market Model: The four classes of investment strategies above represent a
concrete expression of the non-homogeneous behavior institutionalized into the fund
management industry. First, this non-homogeneous behavior explains trading.
People trade because they possess different expectations about the security, are rebalancing their portfolio for risk/diversification purposes, following price trends, or for
liquidity. Excess trading on the buy side or the sell side causes prices to move.
Second, non-homogeneous behavior contributes to market inefficiency. As more
funds become indexed, the market likely becomes less efficient. Momentum traders
may speed price adjustments, often overreacting to news.6 Active management
keeps the market within a range of bounded rationality. The range defines where the
securities become too under or too over valued, so active investors enter the market
to reverse the price trend, thereby producing an inflection point. The balance of
passive indexation and active management determines the dispersion of prices
around intrinsic values.7
Request for Proposals
1. Mental Models. Comment on the mental model above and recommend
additional mental models of the market. Describe the prior beliefs and biases
of the researcher.8
2. Portfolio Construction. To add independent, unbiased empirical evidence, the
CFA Society of Chicago requests the Financial Management Association to ask
its academic members to empirically compare the risk / return of the four
classes of portfolio construction strategies. The FMA Practitioner Research
Committee will very likely approve multiple proposals for academic research
due to the scope of this effort. The approved proposals will likely include
opposing views. We are also interested in estimates of the proportion of funds
employing each strategy or hybrid to develop conceptually sound, empirical
models of price dispersion and formation.
a. Indexation.
b. Fundamental indexation. Consider including factors like book value,
operating income, revenue, assets, employment, dividends, and other
Narasimhan Jegadeesh and Sheridan Titman, “”Returns on Buying Winners and Selling Losers:
Implications for Stock Market Efficiency,” Journal of Finance, 1993, 48, pp. 65-91.
Narasimhan Jegadeesh and Sheridan Titman, “Profitability of Momentum Strategies: An Evaluation of
Alternative Explanations,” Journal of Finance, 2001, vol. 56, pp. 699-720.
7
Robert Shiller, “Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends,”
American Economic Review, January 1981, pp. 421-436.
8
For example, does the researcher believe in perfect instantaneous market efficiency or market
inefficiency within a range of bounded rationality? Does he or she believe in return distributions which are
normal, lognormal, fat tailed with finite upper moments, or fat tailed with indeterminate upper moments?
Does the researcher believe in models based on estimating the price change (multi-factor) or price level
(price formation)? Does he or she believe in cash as the best driver of DCF valuation in modeling or is
earnings a good enough proxy? Does the researcher believe the balance sheet and operating return
measures derived from the operating assets are crucial to a valid DCF valuation model or are operating
earnings or cash flow sufficient? Does she or he believe in placing leverage and size “risk” in the discount
rate or in the certainty equivalent cash flows? Does she or he believe in separating the valuation effects of
the historical baseline from the analyst forecasts? Does the researcher believe in separating the reaction
of price to financial disclosures from trading, weighting, and rebalancing strategies?
The authors of this request for proposal assume that the researcher will let the research design and data
lead to the appropriate conclusions.
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fundamental factors. Please replicate & extend the Rob Arnott
empirical work9 with back tests and actual portfolios.
c. Multi-factor models. Employ the best factors and model structures with
non-linear functions if necessary. Please effectively address the serious
problem of multicollinearity of the “independent” variables. Univariate
analysis should precede the multi-variate analysis.
Academics should explain how multi-factor models provide guidance to
the business unit manager or the owner of a privately held firm on
what internal metrics he should employ to create shareholder wealth. 10
Consider whether there ought to be one Framework that investors can
use to beat the market and the identical Framework that corporations
can use to create value for their shareholders.
d. Discounted Cash Flow. Determine the most accurate and predictive
models. Please include intrinsic valuations based on the security
analyst DCF forecasts of brokerage firms.
The Society encourages academics to recommend additional important
segmentations of strategies.
3. Hybrid Models. A critical question relates to the feasibility of combining active
approaches. Should results of a multi-factor model feed into a DCF or viceversa? Are classification and regression trees (CART) a more appropriate way
to combine models? Are there other construction techniques which provide
superior risk-return characteristics? For example, where do long-short
strategies fit the continuum? Another critical question relates to applying
active approaches to a fundamental index, as opposed to the traditional free
float cap weighted index. Can these active approaches add similar value to
fundamental indexes? Should these active approaches be modified when they
are applied to fundamental indexes? Alternatively, do fundamental indexes
add value to value-oriented active approaches?
4. Measurement Methodologies.
a. Risk. Please consider employing the risk measurements outlined in two
separate requests for proposal: for time series:
“Risk-Adjusted
Ranking in a Non Mean Variance World;” for cross sectional: “Cross
Sectional Risk / Return Measurement of Portfolio Results over an
Interval of Time in a Non Mean-Variance World.”
b. Regression Analysis used in Multi-Factor Models. Since Mandelbrot
suggests that much financial data and most prices follow Stable
Paretian distributions with infinite variances, the residuals from the
regressions should be tested for normality and finite upper moments.11
Robert Arnott, Jason Hsu, Phil Moore, “Redefining Indexation,” Patent Pending, © 2004 Research
Affiliates.
10
Bartley J. Madden, “For Better Corporate Governance, A Shareholder Value Review,“
www.LearningWhatWorks.com
11
See for example, John Neter, Michael H. Kutner, Christopher J. Nachsheim, and William Wasserman,
Applied Linear Statistical Models, McGraw-Hill, 1996, pp. 106-108.
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McCulloch12 recommends a quantile method for estimating the four
parameters of the Stable distribution. McCulloch includes significance
tests to measure how distant from Gaussian Normal the distributions
lie. Consider fat tailed distributions of the residuals with finite upper
moments. If the statistical tests on the residuals confirm nonnormality with infinite variances, then Blattberg and Sargent state: 13
The central limit theorem is important because it establishes
a foundation for hypothesis testing. It states that the sum
of a large number of independently distributed variables,
each of which follows a distribution of finite variance, will
tend to be normally distributed. … If the omitted variables
whose impacts are summarized by the [residuals] have
infinite variances, the assumptions of the central limit
theorem fail to hold and the [residuals] will not be normally
distributed.
Blattberg and Sargent go on to recommend using least absolute
deviations14 instead of ordinary least squares in cases of variables with
infinite variances. This approach significantly reduces biases in the
regression coefficients or the factor weights.
c. DCF Model Accuracy. Consider measuring DCF model accuracy with
value charts and price level tracking errors (see citation #4 below).
d. Back Testing Predictive Methods. Propose traditional methods of back
testing with periodic trading and portfolio re-balancing. Consider
determining portfolio returns with price formation models which track
performance from disclosure dates with panel and other data (see
citation #5 below).
5. Productization. The academics should include an appendix to their papers with
an outline of productization for rapid practitioner decisions in real time with
available data. The outline should show data sources, information flow,
frequency, resulting reports, and describe how practitioners make the
decisions from the reports.
Citations and Disclosures
1. Fundamental Indexation. The September/October 2006 Journal of Indexation
(pages 8, 10-21, 22-24) contains articles and arguments on both sides of the
fundamental indexation issue.15 Much of the discussion revolves around the
theme of whether the market is efficient or not.
J. Huston McCulloch, “Simple Consistent Estimators of Stable Distribution Parameters,” Commun.
Statist. – Simula., 15(4), 1986, pp. 1109-1136.
13
Robert Blattberg and Thomas Sargent, “Regression with Non-Gaussian Stable Disturbances: Some
Sampling Results, Econometrica, Vol. 39, No. 3, (May 1971), pp. 501-511.
14
SPSS may implement a procedure to minimize absolute deviations instead of least squares as the loss
function.
15
For a recent academic article on fundamental indexation, see Javier Estrada, “Fundamental Indexation
and International Diversification” for a current discussion and citation list.
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2. Multi-Factor Models. Multi-factor models are regression models which began
as an outgrowth of traditional screening of stocks. They use multiple criteria
to find stocks likely to out-perform the market. Multi-Factor models seek the
best factors and parameters applied to those factors. Using historical data,
model architects regress price changes against the factors. Chen, Roll and
Ross applied the technique to macro economic factors. 16 Fama and French
related returns to size and book/equity market multiples. 17 Haugen and Baker
tried multiple factors, listed in their article.18 Grinold and Kahn tried factors
listed in their book.19 Joel Greenblatt published the book with two key factors
– return on capital and earnings yield.20, 21
Besides the variables suggested by the authors above, consider the best sites
recommended by the American Association of Individual Investors for stock
screening:
www.Morningstar.com
www.moneycentral.msn.com www.investor.reuters.com and
www.smartmoney.com .
3. Creating Multi-Factor Models.22 Different reasons exist for the creation of
multi-factor models:
a. Quantify and capitalize on market inefficiencies.
b. Quantify the factors which analysts and portfolio managers use to
generate ideas for their security and portfolio recommendations.
c. Run enhanced index funds.
d. Expand product lines in areas where the firm lacks fundamental
expertise. Success in outperforming the market arises if the multifactor model is created properly.
Different methods exist to create multi-factor models:
a. On the one end of the spectrum, the fundamental analyst or portfolio
manager simply places multiple factors together to produce a relative
ranking of all stocks on his coverage list or selection universe with
little or no back tests.
b. The other end of the spectrum incorporates data mining, testing all
factor levels and changes in factor levels over several different
N. Chen, R. Roll, and S.A. Ross, “Economic Factors and the Stock Market,” Journal of Business, Vol. 59,
1986, pp. 383-404.
17
E.F. Fama and K.R. French, “The Cross Section of Returns,” Journal of Finance, 47, 1992, pp. 427-466.
18
Robert A. Haugen and Nardin L. Baker, “Commonality in the Determinants of Expected Stock Returns,”
Journal of Financial Economics, Summer 1996.
19
Richard C. Grinold and Ronald N. Kahn, Active Portfolio Management, McGraw-Hill, 2000, pages 501502.
20
Joel Greenblatt, The Little Book that Beats the Market, Wiley, 2006, pp. 138-143.
21
Ralph Goldsticker. Insert links to relevant published and unpublished articles. Goldsticker has a
bias toward a belief in inefficient markets and multi-factor models as a means to capitalize on that
inefficiency. Based on the results of this academic research, Mellon Capital may refine or expand its
product offerings.
22
Amit Dugar of Schwab. For a description of the Schwab equity model, see Vito Racanelli, “The Schwab
Advantage,” Barron’s, November 27, 2006, pp. 22-23. For a comparison of brokerages’ top share
recommendations, see Vito J. Racanelli, Barron’s On-Line, September 25, 2006. Insert links to relevant
published and unpublished articles. Dugar has a bias toward a belief in inefficient markets and multifactor models as a means to capitalize on that inefficiency. Based on the results of this academic research,
Schwab may refine or expand its product offerings.
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intervals to produce one or more models to capitalize on any market
inefficiencies that can be found.
c. Most reasonable approaches only examine factors that reflect the
fundamental operating condition of the company or other factors cited
in the academic literature.
4. Discounted Cash Flow Models. On the one hand, Multi-Factor models try to
find the best factors and associated parameters by regressing price changes
against the factors with historical data. On the other hand, DCF models try to
estimate intrinsic values as levels for individual stocks, with the view that
price likely migrates toward those intrinsic value levels. Fundamental
indexation also assumes a migration of price toward intrinsic values for the
portfolio as a whole, which is better accomplished with fundamental weights
instead of cap weights.
Traditional DCF forecasts cash flows for several years and places a terminal
value on the final year. The concept relies on forecasting the number of years
necessary for the business to achieve stability in cash flows on which to place
a terminal value. The terminal value is often a perpetuity. The forecast is a
multi-period one, while the perpetuity is a single period capitalization method
to reflect the present value of the cash flows beyond the forecasting time
horizon. This forecasting procedure represents the traditional security analyst
process, where the analyst remains responsible for defining both the cash
flow forecast and the structure of the single period terminal value model.
In contrast to this multi-period security analysis forecast process, commercial
firms, like Stern Stewart, McKinsey, and off-shoots of Callard-Madden
Associates, have developed single period capitalization models for
determining intrinsic value. Single period capitalization models may rely on a
single year’s23 worth of historical data without analyst intervention to forecast
future cash flows for 40-50 years. The Callard-Madden offshoots rely on a
CFROI®24 relative to a real market derived cost of capital, while Stern
Stewart25 and McKinsey rely on a residual income or EVA®26 using a CAPM
cost of capital. Little academic literature has addressed these methodologies,
because most of them were developed within the practitioner community for
portfolio and corporate value based planning applications.27 For example,
Sometimes the method smoothes 2-3 years of data.
CFROI® is a world-wide registered trademark of CSFB HOLT.
25
Stern Stewart offers the ProveIt portfolio management product.
26
EVA® is a registered trademark of Stern Stewart.
27
Bartley J. Madden, Cash Flow Return on Investment: A Total System Approach to Valuing the Firm,
Butterworth Heinemann, 1999.
G. Bennett Stewart, The Quest for Value: A Guide for Senior Managers, Harper Collins, 1991.
Tom Copeland, Tim Koller, and Jack Murrin, Valuation: Measuring and Managing the Value of Companies,
Wiley, 2000.
Gary C. Biddle, Robert M. Bower, and James S. Wallace, “Does EVA® beat earnings? Evidence on
association with stock returns and firm values,” Journal of Accounting and Economics, 24 (1997), pp. 301336.
Thomas G. Lewis, Steigerung des Unternehmens-wertes (Total Value Management), Verlag Moderne
Industrie, 1994.
Steffen Lehmann, Neue Wege in der Bewertung borsennotierter Aktiengesellschaften Ein-Cash-floworientiertest Ertragswertmodell, DeutscherUniversitatsVerlag, 1993.
John D. Martin and J. William Petty, Value Based Management, Harvard Business School Press, 2000.
23
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Thomas and Schostag published an article describing the single period fading
Cash Economic Return discounted cash flow intrinsic valuation methodology. 28
Some DCF methodologies empirically separate the intrinsic valuations based
(A) purely on history without analyst intervention from (B) those intrinsic
valuations based on history with analysts’ forecasts. In this former case (A),
the intrinsic values based purely on history form a baseline, which can be
illustrated with a value chart. The value chart overlays the intrinsic value
against the high-low-closing prices, often for ten years of history. In the latter
case (B), the value chart extends into the forecast time period based on the
analyst forecast.
In addition to CFROI® and residual income based models, Feltham-Ohlson,29
Diliddo of Vector Vest, 30 and ValuTrac31 have created models of intrinsic
valuation.
5. DCF model accuracy methodologies and back tests of price formation models.
Callard-Madden offshoots created value charts with price level tracking
errors32 and the back testing methods for determining portfolio returns with
price formation models which track performance from disclosure dates with
panel and other data.33
6. Hybrid Approaches. For one example, the Center for Research and Analysis
(CFRA) offers a service to identify abnormal accruals. Howard Schilit wrote
the book34 describing the forensic accounting process. Richardson et. al.
provided the methodology for the calculations. The methodology displayed an
average 18% excess annual rate of return between the top and bottom
deciles.35 Are these decile results best placed into a multi-factor model or the
abnormal accrual reversed between the income statement and balance sheet
28
Rawley Thomas and Randy Schostag, “Discounted Cash Flow Method: Using New Modeling to Test
Reasonableness,”
Valuation
Strategies,
September/October
2006,
pp.
24-41.
http://www.LCRT.com/Updates/NACVA PAPER LCRT & DCF.pdf . Thomas serves as VP Practitioner
Director of the Financial Management Association and heads the FMA’s Practitioner Research Committee.
Under the Committee’s Policies and Procedures, Thomas must disclose his commercial interest as selling
products, consulting services, and IT integration related to advanced DCF and risk measurement. Any
involvement of Thomas or his company, LifeCycle Returns, in the Practitioner Demand Driven Academic
Research Initiative requires full disclosure and approval of the Executive Committee of the FMA.
Based on the empirical evidence produced so far, Thomas has a bias toward DCF models and Stable
Distributions with infinite variances.
29
Jing Liu and James A. Ohlson, “The Feltham-Ohlson (1995) Model: Empirical Implications,” Journal of
Accounting, Auditing and Finance, 2000.
Kim Lo and Thomas Lys, “The Ohlson Model: Contribution to Valuation Theory, Limitations and Empirical
Applications,” Journal of Accounting, Auditing, and Finance, 2000.
30
Bart Diliddo, Stocks, Strategies & Common Sense, HSC Publishers, 1995-1998, pp. 22-23.
31
http://www.valu-trac.com/ based on ROE.
32
Exhibits 2-4, 11-12 of Rawley Thomas and Randy Schostag, “Discounted Cash Flow Method,” Valuation
Strategies, September/October 2006, pp. 24-41. Pages 7-10 and 26-28 of
http://www.LCRT.com/Updates/NACVA PAPER LCRT & DCF.pdf .
“Advanced DCF Valuation Measurement Methodology: Predictive Capability, Accuracy, and Robustness,”
presented to the Midwest Finance Association, March 23, 2006 by Rawley Thomas, slides 45-47,
www.lcrt.com/Updates/MidwestFinance3-24-06.pps .
34
Howard Schilit, Financial Shenanigans: How to Detect Accounting Gimmicks & Fraud in Financial
Reports, McGraw-Hill, 2002.
35
Scott Richardson, Mark Soliman and Irem Tuna, “Implications of Accounting Distortions and Growth for
Accruals and Profitability,” November 2005 version.
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to incorporate those results into a valuation model? Should security analysts
explicitly estimate how much of the abnormal accruals reflect the changing
economics of the business and how much represent fraudulent management
reporting?
Firms Who May Be Willing to Donate Data
1.
2.
3.
4.
5.
6.
7.
8.
Northern Trust (fundamental indexation)
Harris Bank (fundamental indexation)
Richards and Tierney (fundamental indexation)
Schwab (multi-factor)
Mellon Capital Management (multi-factor)
LifeCycle Returns36 (DCF – definitely willing)
Standard & Poors (various strategies)
Morningstar (various strategies)
Subject to the approval of the FMA Executive Committee, LCRT will offer not only data, but also access
to the LCRT Research Platform for testing the accuracy and predictive capability of DCF valuation models,
along with technical support. This access requires that the academic(s) and its institution execute a license
agreement / non-disclosure agreement with LCRT at no cost.
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