Russ Wermers Bank of America Professor of Finance

Director, Center for Financial Policy

University of Maryland

Active vs. Passive Management: How to Separate “SAMs” from “IAMs”

Agenda

1.  Does active management add value?

2.  Can we identify superior active managers (SAMs) on an ex ante basis?

3.  What are the best quantitative tools for assessing active managers?

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Summary of Findings: 1. The average active manager does not add

value, when strictly measured using returns vs. benchmark

2. Many active managers do add value and it may be possible to identify them in advance

3. (A) Portfolio-holdings based, and

(B) Macroeconomic returns based are two recent quantitative advances of importance to find superior managers

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Does active management add value?

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The Efficient Market Hypothesis Market prices reflect all (public) information (?)

Average active manager cannot add value

A tautology? (The Arithmetic of Active Management, Sharpe) § Market = Σ (All Active Managers) + Σ (All Index Funds) + Σ (All Individual

Investors)

§ Active management is a “zero sum game”? Depends on costs of indexing

§ Active managers charge fees and incur expenses

§ Return Σ (Active Managers) = Overall market return minus fees and expenses, unless they provide liquidity to index funds or overweight securities/sectors in aggregate

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EMH Empirical Results Numerous studies support the EMH § Early studies ‒  Jensen (1968), no CAPM alpha in average mutual fund returns, net of

fees and expenses

§ More recent studies ‒ Barra, Scaillet and Wermers (2010) study mutual funds and adjust for

4-factor model ‒ Goyal and Wahal (2010) study institutional separate accounts and also

adjust for risk

§ After tax comparisons are even worse

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Recent Tests of the EMH

Source: The mutual fund data is from Barras, Scaillet and Wermers (2010), while the institutional separate account data is from Busse, Goyal and Wahal (2010)

Ann

ualiz

ed F

our

Fact

or A

lpha

(n

et o

f fe

es a

nd e

xpen

ses)

Four factor alphas, in percent per year, for Equity Mutual Funds and Equity Institutional Separate Accounts, net of fees and expenses

-0.6

0

0.6

All Funds

All Growth Funds

Aggressive Growth

Growth and Income

Equally- Weighted

Net

Value- Weighted

Net

Based on Mutual Fund Data (from Jan 1975–Dec 2006)

Based on Institutional Separate Account Data

(from 1991–2007)

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Why Does Active Management Persist?

Are investors irrational? § If so, how can the market be efficient?

§ Empirical evidence of biases, but may cancel out

Grossman and Stiglitz (1980) § Information is costly

§ Markets must be “mostly but not completely efficient” to encourage investors to gather and analyze information

§ An efficient degree of inefficiency (“informationally efficient markets”)

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A Zero-Sum Game? Contest between active managers may be a zero-sum game (but, not a certainty at this point)

Active managers are essential to efficient capital markets and will always exist: § Improves market efficiency and capital allocation

§ Leads to greater economic efficiency and growth

§ Producing greater wealth for society as a whole

§ And, do not hold market portfolio in aggregate!

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Can we identify superior active managers

(SAMs)

on an ex ante basis?

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Playing the Zero-Sum Game Active management is a SAM-IAM contest (“Superior Active Managers” and “Inferior Active Managers”)

SAMs exploit IAMs (and other investors) by § Better information or analysis (skill) § Supplying liquidity and immediacy

But… § Skill and liquidity needs can vary over time and across

stocks § Today’s SAMs can be tomorrow’s IAMS § So who is whom?

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Σαµ Ι αµ

Finding Tomorrow’s SAMs Today Academic studies focus on four basic approaches,

looking at 1) Past performance (properly adjusted) 2) Macro economic relationships 3) Fund/manager characteristics 4) Fund holdings

Investors should consider all of these when selecting funds

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Past Performance If skill persists, alpha should persist

Offsetting effects

§ Poor performers get replaced

§ Strong performers may grow too big or raise fees

§ Changing opportunity set (e.g., internet bubble)

§ Hence, lack of persistence does not necessarily imply lack of skill

Evidence indicates modest persistence in properly-adjusted returns

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Proof of Persistence: Recent Techniques Show that Skill is Persistent! Theme: Must use proper methods.

Harlow and Brown (2006) §  Style-adjusted returns improve odds of finding SAMs from 45% to 60% §  That is, controlling for value/growth and small cap/large cap tilts allows a more

precise capture of stock-selection skills

Pastor and Stambaugh (2002) §  Better results after adjusting for sector biases

§  For example, evaluating technology funds is better conducted by adding a technology index to the model; for financials, a financial industry index

Kosowski, Timmerman, Wermers and White (2006) §  Better results adjusting for non-normality (fat tails, skewness) § Dynamic strategies of funds create non-normally distributed returns §  Even relatively static strategies can hold portfolios with non-normally dist’d returns

§  Poses a challenge to standard modeling, which usually assumes normality §  “Bootstrapping”—which is a simulation procedure that takes random samples from

real fund return data helps to correct this

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Persistence D

ecile

by

Prio

r Al

pha

-4.0 -2.0 0.0 2.0

10

9

8

7

6

5

4

3

2

1

Kosowski, Timmerman, Wermers and White Four Factor Alpha Model*

Harlow and Brown Three Factor Alpha Model**

Persistence in Past Performance

* Harlow and Brown use a three factor alpha methodology rebalanced quarterly using the time period 1979–2003 ** Kosowski, Timmerman, Wermers and White use a four factor alpha methodology using a three-year ranking period, with a bootstrapping technique to model non-normality,

rebalanced annually using the time period 1978–2002

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Past Performance: Conclusions Need a sophisticated performance attribution system to distinguish luck from skill § Even then, persistence is modest

When changing managers, make sure expected gains exceed switching costs § Goyal and Wahal (2008) find that, for institutions, hired

managers do not outperform fired managers, on average

Past performance is less useful after a manager change

§ Baks (2003) finds 10%–50% of persistence is from the manager, rest from other factors (firm, research team, etc.)

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Macro Economic Correlations Moskowitz (2000) and Kosowski (2006) § Active managers do better in periods of greater uncertainty (recessions, volatile

markets)

§ But it is hard to accurately forecast these periods in advance

Alpha performance in recession and expansion periods, Kosowski (2006)

Annu

aliz

ed F

our

Fact

or A

lpha

All Funds

Annu

aliz

ed F

our

Fact

or A

lpha

All Growth Funds

Annu

aliz

ed F

our

Fact

or A

lpha

Aggressive Growth

Annu

aliz

ed F

our

Fact

or A

lpha

Growth

Annu

aliz

ed F

our

Fact

or A

lpha

Growth and Income

Annu

aliz

ed F

our

Fact

or A

lpha

Balanced Income

-2

0

2

4

Full Sample (1962–2005)

Expansion -2

0

2

4

Full Sample (1962–2005)

Expansion -2

-1

0

1

Full Sample (1962–2005)

Expansion

-2

0

2

4

Full Sample (1962–2005)

Expansion -2

0

2

4

Full Sample (1962–2005)

Expansion -2

0

2

4

Full Sample (1962–2005)

Expansion

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Macro Economic Forecasts Avramov and Wermers (2006) § Relate future U.S. equity mutual fund returns to current macro

conditions

‒  Short rates; dividend yield; default spread; term structure

§ Style-adjusted (4-factor) alphas of 600 bps/yr when able to rotate to expected top-performing funds

Banegas, Gillen, Timmerman and Wermers (2009) § Use additional predictor variables and find similar results for

European equity mutual funds

Avramov, Kosowski, and Teo (2007) § Include VIX and get even better results (1200 bps of alpha) for

hedge funds

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Some Cautions These strategies have high turnover § 200%–300% annually

§ Not practical when manager transition costs are high ‒  E.g., early withdrawal penalties, long lock-up periods, high trading costs

§ Possible solution: Diversify and rebalance at the margin

May conflict with a return-persistence strategy § Buy recent losers when macro conditions change

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Characteristics: Fund Manager § Experienced managers of large funds outperform; not so for small funds ‒ Ding and Wermers (2009)

§ Social connections lead to better performance ‒  Cohen, Frazzini and Malloy (2008)

§ Graduates of “better” colleges ‒  Chevalier and Ellison (1999): Undergraduate degree

‒  Gottesman and Morey (2006): MBA degree

§  CFAs manage risk better, but don’t outperform ‒ Dincer, Gregory-Allen and Shawky (2010)

§ Hedge fund managers who invest in their own funds ‒ De Souza and Gokcan (2003)

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Characteristics: Fund Management Company § Larger companies with more research resources ‒ Chen, Hong, Huang and Kubik (2004)

‒ Busse, Goyal and Wahal (2010)

§ More independent directors ‒ Ding and Wermers (2009)

§ Flatter organizational structure ‒ Massa and Zhang (2009)

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Characteristics: Fund Itself § Lower expense ratios outperform in every time period and fund category (but this doesn’t mean low ER is the only factor that matters!) ‒ Kinnel, Morningstar (2010)

§ Less “cash drag” = better performance ‒ Edelen (1999)—indicates need to examine flow volatility

§ Industry/sector specialist funds outperform ‒ Kasperczyk, Sialm and Zheng (2005)

§ More “style drift” = better performance ‒ Wermers (2002)

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Characteristics: Hedge Funds § High water marks, higher incentive fees and longer lock-up periods ‒ Liang (1999)

§ “Goldilocks” funds: not too large or too small ‒ Getmansky (2005): concave relationship of performance and fund size

§ Conflicting evidence on fund age ‒ Howell (2001): Young funds better

‒ De Souza and Gokcan (2003): Seasoned funds better

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Fund Holdings Smaller/positive “return gap” = better performance § Kasperczyk, Sialm and Zheng (2008)

Risk “shifting” = worse performance § Huang, Sialm and Zheng (2010)

“Contrarian” managers outperform “herding” managers § Wei, Wermers and Yao (2009)

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“Active Share” Cremers and Petajisto (2009) § Higher active share = better performance ‒  CP claim this is due to higher “conviction”

§ Higher tracking error ≠ better performance

Qualifications §  Don’t control for benchmark capitalization

§  Small-cap portfolios tend to have higher active shares

§  Better performance of SC funds consistent with costly information thesis of Grossman and Stiglitz

§  Overconfidence literature warns against excessive “conviction”

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Recent Research Provides Further Valuable Insights on Active Management… § 1. Low-turnover mutual funds outperform (on average) high-turnover funds, all else equal A. Cremers and Pareek (2016) find that high Active Share is especially predictive when coupled with low turnover (Q5 Duration is the lowest turnover; Q5 Active Share is the highest Active Share):

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Recent Research Provides Further Valuable Insights on Active Management… § 2. Competition reduces the probability of achieving active alpha

A. Hoberg, Kumar, and Prabhala (2016) measure competition as the number of rival funds that are “close” (in Euclidian distance) to a subject fund in the style dimensions of (1) size (2) book-to-market and (3) momentum.

B. Funds fare better, in the future, when they have fewer competitors that are “close” to their “style space”

High past alpha funds in style areas with low competition generate an alpha of 4.5%/year

High past alpha funds in style areas with high competition generate an alpha of only 1%/year

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Recent Evidence of Active vs. Passive § We can draw some evidence from Paul (2009), who shows that the advantage of active management is highly reliant on the dispersion of returns of stocks In other words, periods of high correlations among stocks are not conducive to active manager outperformance; the recent drop in such correlations should prove advantageous to active management

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Conclusions Average active manager does not outperform

But active management is essential for market efficiency

§ SAMs should continue to earn an economic rent

Academic evidence suggests it may be possible to identify SAMs in advance § Multiple methods seem promising: (1) past returns properly adjusted for risk,

(2) holdings-based, (3) macroeconomic returns-based, and (4) manager/fund/company characteristics

§ Comprehensive approach works best

Portfolio holdings-based measures and macroeconomic returns-based measures are components most underused by investors; however, even past returns are often misused!

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