Q Group Slides...efficient portfolio construction and management. International Journal of Forecasting, 31, 550-560. 11 Public Model Risk Preferences, 1999-2011 Table 2: Efficient

C L I M B Y O U R M O U N T A I N

Q GROUP MEETING – LAJOLLA, CA

October 29, 2019

Harry Markowitz, PhDHarry Markowitz & Co.

San Diego, CA

John B. Guerard, Jr., PhDMcKinley Capital Management, LLC

Anchorage, AK

October 2019

THE EXISTENCE AND PERSISTENCE OF FINANCIAL ANOMALIES

P O R T F O L I O C O N S T R U C T I O N A N D M E A S U R E M E N T

3

Research Includes:

• Markowitz (1959) Mean-Variance Portfolio Selection

• Sharpe (1964), Lintner (1965), Mossin (1966) CAPM

• Treynor and Mazuy (1966) CAPM Performance Measurement

• Fama (1970, 1976, 1991) Efficient Markets Hypothesis

• Ross (1976) and Roll and Ross (1980) APT

• Friend, Dhrymes, and Gultekin (1983) APT

• Blin, Bender, and Guerard (1997)

APT Applied Investment Management and Portfolio Measurement

• Guerard, Markowitz, and Xu (2015) APT and Axioma Risk Models Tested

3

E X P E C T E D R E T U R N S M O D E L I N G T O E X P L O I T F I N A N C I A L A N O M A L I E S

4

Research Includes:

• Dimson (1988) Size Anomalies

• Jacobs and Levy (1988) Financial Anomalies

• Bloch, Guerard, Markowitz, Todd, and Xu (1993) Financial Anomalies

with the Markowitz Risk Model

• Blin, Bender, and Guerard (1997) Financial Anomalies and the APT

Model

• Guerard (1997) CTEF Introduced with BARRA Risk Model

• McKinley Quant (2006) MQ Introduced with APT Risk Model

• Guerard, Markowitz, and Xu (2015) MQ and CTEF Updated and Verified

for Axioma Stock Selection

4 4

T H R E E TA K E - H O M E S F O R O U R A U D I E N C E

5

1. The Financial Anomalies stock selection models recommended in 1991 have worked to produce statistically significant Active Returns and positive Specific Returns (or Asset Selection), including transactions costs;

2. The Japanese and R1 models initially estimated produce higher Active Returns in R3, Non-U.S., and EM stock markets;

3. Real-time portfolio statistically significant Active Returns can be achieved, but only by implementing large active stock weights in portfolios.

5 4

6

Figure 1: MCM Portfol io Construction and Modeling Process

7

Figure 1: MCM Portfol io Construction and Modeling Process

Publ ic Model Expected Returns Stock Select ion Model , GLER

8

TRt + 1 = a0 + a1EPt + a2 BP t + a3CPt + a4SPt + a5REPt + a6RBPt + a7RCPt

+ a8RSPt + a9CTEFt + a10PMt + et , (1)

where: EP = [earnings per share]/[price per share] = earnings-price ratio;

BP = [book value per share]/[price per share] = book-price ratio;

CP = [cash flow per share]/[price per share] = cash flow-price ratio; SP = [net sales per share]/[price per share] = sales-price ratio;

REP = [current EP ratio]/[average EP ratio over the past five years];

RBP = [current BP ratio]/[average BP ratio over the past five years];

RCP = [current CP ratio]/[average CP ratio over the past five years];

RSP = [current SP ratio]/[average SP ratio over the past five years]; CTEF = consensus earnings-per-share I/B/E/S forecast, revisions and

breadth;

PM = price momentum; and

e = randomly distributed error term.


9

Table 1: Top/Bottom decile spreads of FSGLER

1997 - 2011

Variable Top 3 decile spreads (t)

AnnualizedTop 3 decile

spreads

Top decile spread (t)

AnnualizedTop decile

spread EP 0.42% (1.66) 4.43% 0.20% (0.66) 1.40%BP 0.50 (1.67) 5.21 0.96 (1.45) 9.54FEP 0.50 (1.95) 5.38 0.54 (2.02) 5.11CTEF 0.72 (5.85) 8.85 1.16 (7.27) 14.54EWC 0.70 (3.67) 8.37 1.06 (3.55) 12.36GLER 1.12 (4.54) 13.55 1.48 (3.67) 17.19

Source: FactSet and APT. Past performance is not indicative of future returns. Guerard, J.B., Jr., Markowitz, H.M., & Xu, G. (2015).

Earnings forecasting in a global stock selection model and efficient portfolio construction and management. International Journal of

Forecasting, 31, 550-560.


10

Table 1 cont.: Top/Bottom decile spreads of FSGLER

2003 – 2011

Variable Top 3 decile spreads (t)

AnnualizedTop 3 decile

spreads

Top decile spread (t)

AnnualizedTop decile

spread EP 0.23% (0.80) 2.267 0.25% (1.08) 2.65BP 0.80 (1.78) 8.74 0.47 (1.57) 5.21FEP 0.38 (1.33) 4.12 0.23 (1.39) 2.66CTEF 1.07 (6.48) 3.37 0.58 (4.88) 7.12EWC 0.97 (5.79) 12.07 0.60 (1.83) 7.41GLER 1.33 (3.98) 16.32 0.96 (4.59) 11.78Source: FactSet and APT. Past performance is not indicative of future returns. Guerard, J.B., Jr., Markowitz, H.M., & Xu, G. (2015). Earnings forecasting in a global stock selection model and

efficient portfolio construction and management. International Journal of Forecasting, 31, 550-560.

11

Publ ic Model Risk Preferences, 1999-2011

Table 2: Efficient Frontier of the Global Stock Selection Model with Various Portfolio Optimization Techniques1999 – 2011APT Risk Model

Earnings Model or

Component

Mean Variance

MethodologyLambda Annualized

ReturnStandard Deviation

Sharpe Ratio

Info Ratio

Tracking Error

GLER M59 1000 15.84 24.97 0.590 0.78 13.11500 16.34 24.85 0.590 0.82 12.08200 16.37 24.38 0.610 0.85 12.68100 15.90 24.61 0.580 0.81 12.66

5 10.11 19.36 0.440 0.51 8.81Benchmark 5.59 0.240Source: FactSet and APT. Past performance is not indicative of future returns. Guerard, J.B., Jr., Markowitz, H.M., & Xu, G. (2015). Earnings forecasting in a global stock selection model

and efficient portfolio construction and management. International Journal of Forecasting, 31, 550-560.

12

Publ ic Model Risk Preferences, 1999-2011

Table 2 cont.: Efficient Frontier of the Global Stock Selection Model with Various Portfolio Optimization Techniques

1999 – 2011APT Risk ModelGLER TaR 1000 16.10 21.93 0.660 0.94 11.18

500 15.91 21.99 0.651 0.90 11.44200 16.09 20.95 0.691 0.97 10.83100 14.18 21.24 0.591 0.77 11.23

5 8.51 20.03 0.344 0.33 8.75

GLER EAWTaR2 1000 14.80 21.96 0.600 0.94 11.07500 14.30 21.65 0.590 0.80 10.87200 14.15 20.92 0.600 0.85 10.04100 13.49 20.82 0.570 0.80 9.84

5 10.77 20.79 0.440 0.43 12.18Source: FactSet and APT. Past performance is not indicative of future returns. Guerard, J.B., Jr., Markowitz, H.M., & Xu, G. (2015). Earnings forecasting in a global stock selection

model and efficient portfolio construction and management. International Journal of Forecasting, 31, 550-560.

13

Source: FactSet and Axioma. Past performance is not indicative of future returns.

Guerard, J.B., Jr., & Chettiappan, S. (2017). Active quant: Applied investment analysis in emerging markets”, Journal of Investing 26, 138-152.

Table 3: Axioma Statistical Risk Model and OptimizerJanuary 2003 – December 2016

STAT Risk Model

Tracking ErrorModel: XUS GLER 4.00 5.00 6.00 7.00 8.00 9.00 10.00Ann. Port Return 13.18 14.13 14.47 15.22 15.80 15.95 15.95Ann. STD 20.16 20.56 20.66 20.99 21.20 21.63 21.63Ann. Active Return 5.33 6.29 6.62 7.37 7.94 8.10 8.10Ann. Active Risk 6.22 7.14 7.55 7.96 9.16 8.64 8.64ShR 0.573 0.605 0.619 0.645 0.668 0.662 0.662IR 0.856 0.880 0.876 0.925 0.975 0.937 0.937

14



Table 3 cont.: Axioma Statistical Risk Model and OptimizerJanuary 2003 – December 2016

STAT Risk Model

Tracking ErrorModel: GL GLER 4.00 5.00 6.00 7.00 8.00 9.00 10.00Ann. Port Return 12.42 14.17 14.78 15.88 16.30 16.80 17.24Ann. STD 17.82 18.92 19.60 19.98 20.12 20.54 20.64Ann. Active Return 4.29 6.04 6.65 7.75 8.17 8.67 9.11Ann. Active Risk 6.04 7.88 8.00 8.57 8.69 9.14 9.41ShR 0.601 0.659 0.667 0.710 0.726 0.775 0.753IR 0.710 0.852 0.832 0.905 0.940 0.949 0.968

15



Table 3 cont.: Axioma Statistical Risk Model and OptimizerJanuary 2003 – December 2016

STAT Risk Model

Tracking ErrorModel: EM GLER 4.00 5.00 6.00 7.00 8.00 9.00 10.00Ann. Port Return 18.79 19.79 20.15 20.76 21.16 21.82 22.67Ann. STD 26.09 26.22 26.34 26.45 26.74 26.95 27.25Ann. Active Return 8.48 9.47 9.84 10.46 10.45 11.61 12.36Ann. Active Risk 8.99 9.16 9.36 9.55 10.09 10.22 10.40ShR 0.655 0.689 0.708 0.721 0.751 0.746 0.769IR 0.944 1.033 1.062 1.095 1.085 1.128 1.180

16


Guerard, J.B., Jr., & Chettiappan, S. (2017). Active quant: Applied investment

analysis in emerging markets”, Journal of Investing 26, 138-152.

Figure 2: Return vs. Realized Tracking Error

17


Guerard, J.B., Jr., & Chettiappan, S. (2017). Active quant: Applied investment

analysis in emerging markets”, Journal of Investing 26, 138-152.

Figure 3: Return vs. Tracking Tracking Error

18

REG8=OIF99 Regression(EP,BP,CP,SP,REP,RBP,RCP,RSP)

REG9=OIF99Regression(EP,BP,CP,SP,REP,RBP,RCP,RSP,CTEF)

REG10=OIF99 Regression(EP,BP,CP,SP,REP,RBP,RCP,RSP,CTEF,PM71)

R E G R E S S I O N M O D E L D E F I N I T I O N S

1919

Table 4: Portfolio DashboardMean-Variance OptimizationAXIOMA Fundamental Risk Model1/2002 - 11/2018

RiskRisk Stock Risk Risk

Sharpe Info Specific Effect Factors Effect Total Transaction TotalPortfolios Ratio Ratio Effect T-Stat Effect T-Stat Effect Effect Effect

JAPAN_REG8_8TE 0.56 0.54 5.64 4.00 -0.18 0.44 5.46 -2.24 3.26

JAPAN_REG8_6TE 0.54 0.51 4.73 3.74 -0.23 0.20 4.50 -1.49 3.04

JAPAN_REG8_4TE 0.50 0.46 2.75 2.88 0.17 0.63 2.92 -0.79 2.17

JAPAN_REG9_8TE 0.42 0.28 2.14 1.65 0.74 1.34 2.87 -2.00 0.95

JAPAN_REG9_4TE 0.44 0.31 1.54 1.59 0.55 1.45 2.09 -0.82 1.32

20

Table 4 cont.: Portfolio DashboardMean-Variance OptimizationAXIOMA Fundamental Risk Model1/2002 - 11/2018 Risk

Risk Stock RiskStock Specific Risk Factors Risk Risk


R1000_REG8_6TE 0.69 0.30 2.52 2.07 -0.55 0.16 1.97 -0.88 1.10

R1000_REG8_8TE 0.58 0.13 2.36 1.57 -1.40 -0.18 0.96 -1.10 -0.15

R1000_REG8_4TE 0.71 0.27 0.81 1.00 0.44 0.84 1.25 -0.43 0.82

R1000_REG9_8TE 0.58 0.15 1.25 0.99 0.07 0.59 1.32 -1.15 0.17

R1000_CTEF_4TE 0.75 0.44 0.63 0.89 1.53 2.34 2.16 -0.69 1.47

2121

Table 4 cont.: Portfolio DashboardMean-Variance OptimizationAXIOMA Fundamental Risk Model – Non-U.S. 5000 Universe1/2002 - 11/2018



XUS_CTEF_6TE 0.85 1.45 9.35 6.89 5.58 6.80 14.93 -4.16 10.77

XUS_CTEF_8TE 0.91 1.44 11.51 6.60 5.24 5.27 16.74 -4.98 11.76

XUS_CTEF_4TE 0.70 1.26 6.40 5.77 4.66 7.71 11.05 -3.07 7.98

XUS_REG10_6TE 0.65 1.21 4.16 3.53 7.23 6.53 11.39 -4.27 7.12

XUS_REG10_4TE 0.49 1.07 2.66 3.09 4.67 5.85 7.33 -3.06 4.26

2222

Table 4 cont.: Portfolio DashboardMean-Variance OptimizationAXIOMA Fundamental Risk Model1/2002 - 11/2018


Sharpe Info Specific Effect Factors Effect Total Transaction Total

Portfolios Ratio Ratio Effect T-Stat Effect T-Stat Effect Effect Effect

EM_REG8_4TE 0.51 0.31 3.99 2.38 1.79 2.11 5.78 -2.05 3.73

EM_REG9_4TE 0.55 0.49 3.45 2.20 3.28 3.60 6.73 -2.11 4.63

EM_CTEF_8TE 0.59 0.61 3.54 2.18 5.76 3.64 9.30 -3.58 5.72

EM_CTEF_4TE 0.58 0.72 3.20 2.16 3.84 4.42 7.04 -2.10 4.94

EM_CTEF_6TE 0.60 0.65 3.67 2.13 4.67 4.02 8.34 -2.75 5.59

2323

Table 5: CTEF and REG9 Portfolio Analysis using Axioma STAT and FUND Risk Models Mean-Variance OptimizationAXIOMA Fundamental Risk Model12/ 2002 - 11/ 2018

Portfolios

RiskStock

SpecificEffect

RiskStock

SpecificEffectT-Stat

RiskFactorsEffect

RiskFactorsEffectT-Stat

RiskTotalEffect

RiskTrans.Effect

TotalEffect

EM_CTEF_STAT_6TE 1.92 1.97 4.69 4.15 6.62 -3.04 3.58

XUS_CTEF_STAT_6TE 3.89 3.66 4.24 3.87 8.13 -4.77 3.37

ACW_CTEF_STAT_6TE 4.47 3.67 2.95 3.08 7.42 -4.06 3.37

XUS_CTEF_FUND_6TE 3.63 3.50 3.98 3.83 7.61 -4.58 3.02

EM_CTEF_FUND_6TE 1.90 1.93 3.82 4.04 5.72 -2.86 2.86

ACW_CTEF_FUND_6TE 4.13 3.32 2.31 2.81 6.43 -3.80 2.63

2424

Table 5 cont.: CTEF and REG9 Portfolio Analysis using Axioma STAT and FUND Risk Models Mean-Variance OptimizationAXIOMA Fundamental Risk Model12/ 2002 - 11/ 2018

Portfolios Div

iden

dYi

eld

Earn

ings

Yiel

d

Gro

wth

Med

ium

-Te

rmM

omen

tum

Prof

itabi

lity

Size

Valu

e

Vola

tility

EM_CTEF_STAT_6TE 0.43 0.70 -0.01 2.18 0.14 0.67 0.78 -0.91

XUS_CTEF_STAT_6TE 0.09 0.70 -0.15 2.66 0.18 0.90 0.67 -1.78

ACW_CTEF_STAT_6TE 0.00 0.70 -0.09 2.75 0.05 1.27 0.91 -2.14

XUS_CTEF_FUND_6TE 0.08 0.73 -0.19 2.60 0.27 0.89 0.54 -1.88

EM_CTEF_FUND_6TE 0.35 0.67 -0.02 2.08 0.05 0.60 0.75 -1.01

ACW_CTEF_FUND_6TE 0.04 0.66 -0.15 2.61 0.07 1.09 0.80 -2.01

2525


Portfolios

RiskStock

SpecificEffect

RiskStock


RiskFactorsEffect


RiskTotalEffect

RiskTrans.Effect

TotalEffect

EM_REG9_STAT_6TE 0.52 1.02 4.44 3.52 4.96 -3.18 1.78

EM_REG9_FUND_6TE 0.60 0.94 3.55 3.39 4.15 -2.86 1.29

ACW_REG9_STAT_6TE 0.79 1.05 2.93 2.51 3.72 -3.52 0.20

XUS_REG9_STAT_6TE 0.05 0.31 3.02 2.94 3.06 -3.98 -0.92

ACW_REG9_FUND_6TE -0.31 -0.10 2.35 2.33 2.04 -3.19 -1.15

XUS_REG9_FUND_6TE -0.11 0.26 2.47 2.55 2.35 -3.85 -1.50

2626


Portfolios Div

iden

dYi

eld

Earn

ings

Yiel

d

Gro

wth

Med

ium

-Te

rmM

omen

tum

Prof

itabi

lity

Size

Valu

e

Vola

tility

EM_REG9_STAT_6TE 0.49 0.39 0.29 -0.04 -1.03 1.19 2.99 -0.63

EM_REG9_FUND_6TE 0.45 0.31 0.22 -0.09 -0.97 0.98 2.59 -0.62

ACW_REG9_STAT_6TE 0.25 0.14 0.03 0.41 -1.14 2.06 3.33 -2.19

XUS_REG9_STAT_6TE 0.19 0.23 0.07 0.36 -0.84 1.47 2.68 -1.67

ACW_REG9_FUND_6TE 0.11 0.16 0.00 0.26 -0.99 1.67 2.63 -1.61

XUS_REG9_FUND_6TE 0.13 0.23 0.03 0.24 -0.79 1.31 2.36 -1.42

2727


Portfolios

RiskStock

SpecificEffect

RiskStock


RiskFactorsEffect


RiskTotalEffect

RiskTrans.Effect

TotalEffect

R1_CTEF_STAT_6TE 0.57 0.80 2.62 2.08 3.19 -0.86 2.33

R1_CTEF_FUND_6TE 0.64 0.83 2.27 1.98 2.90 -0.81 2.09

R3_CTEF_STAT_6TE 0.03 0.25 3.32 2.35 3.35 -1.33 2.02

R3_CTEF_FUND_6TE 0.30 0.52 2.56 2.23 2.86 -0.86 2.00

R1: Russell 1000, R3: Russell 3000

2828


Portfolios Div

iden

dYi

eld

Earn

ings

Yiel

d

Gro

wth

Med

ium

-Te

rmM

omen

tum

Prof

itabi

lity

Size

Valu

e

Vola

tility

R1_CTEF_STAT_6TE 0.26 1.58 0.02 1.16 -0.27 -0.15 2.44 0.19

R1_CTEF_FUND_6TE 0.25 1.57 0.04 1.00 -0.26 -0.11 2.34 0.15

R3_CTEF_STAT_6TE 0.20 1.65 0.04 1.30 -0.20 0.05 3.72 0.19

R3_CTEF_FUND_6TE 0.14 1.66 0.06 1.08 -0.19 0.05 3.20 0.07


2929


Portfolios

RiskStock

SpecificEffect

RiskStock


RiskFactorsEffect


RiskTotalEffect

RiskTrans.Effect

TotalEffect

R1_REG9_STAT_6TE 1.47 1.19 0.18 0.71 1.65 -1.08 0.57

R3_REG9_STAT_6TE 2.19 1.60 0.00 0.51 2.19 -1.63 0.56

R1_REG9_FUND_6TE 0.82 0.70 0.33 0.75 1.16 -0.97 0.19

R3_REG9_FUND_6TE 1.37 1.16 0.17 0.57 1.53 -1.43 0.10


3030


Portfolios Div

iden

dYi

eld

Earn

ings

Yiel

d

Gro

wth

Med

ium

-Te

rmM

omen

tum

Prof

itabi

lity

Size

Valu

e

Vola

tility

R1_REG9_STAT_6TE 0.07 0.58 -0.14 -0.20 -0.41 -0.70 2.93 0.86

R3_REG9_STAT_6TE 0.11 0.31 -0.17 -0.24 -0.15 -0.65 3.65 0.73

R1_REG9_FUND_6TE 0.05 0.60 -0.16 -0.13 -0.43 -0.71 2.66 0.82

R3_REG9_FUND_6TE 0.00 0.43 -0.13 -0.13 -0.17 -0.68 2.94 0.56


3131

Table 6: Donut Portfolio AnalysisAXIOMA Fundamental Risk ModelTime Period: 12/2002 - 11/2018

Portfolios

RiskStock

SpecificEffect

RiskStock


RiskFactorsEffect


RiskTotalEffect

RiskTrans.Effect

TotalEffect

ACW_DONUT_CTEF_STAT_6TE 4.43 3.65 3.00 3.11 7.42 -3.96 3.46

EM_DONUT_CTEF_STAT_6TE 1.82 1.94 4.37 3.94 6.19 -2.95 3.25

XUS_DONUT_CTEF_STAT_6TE 3.14 3.27 4.18 3.73 7.32 -4.56 2.76

EM_DONUT_CTEF_FUND_6TE 1.49 1.76 4.00 4.01 5.49 -2.83 2.66

3232

Table 6 cont.: Donut Portfolio AnalysisAXIOMA Fundamental Risk ModelTime Period: 12/2002 - 11/2018

Portfolios Div

iden

dYi

eld

Earn

ings

Yiel

d

Gro

wth

Med

ium

-Te

rmM

omen

tum

Prof

itabi

lity

Size

Valu

e

Vola

tility

ACW_DONUT_CTEF_STAT_6TE -0.01 0.69 -0.11 2.67 0.08 1.17 0.87 -0.01

EM_DONUT_CTEF_STAT_6TE 0.37 0.59 -0.03 2.09 0.27 0.46 0.61 0.37

XUS_DONUT_CTEF_STAT_6TE 0.07 0.72 -0.13 2.63 0.14 0.86 0.60 0.07

EM_DONUT_CTEF_FUND_6TE 0.31 0.60 -0.03 2.00 0.19 0.43 0.61 0.31

3333


Portfolios

RiskStock

SpecificEffect

RiskStock


RiskFactorsEffect


RiskTotalEffect

RiskTrans.Effect

TotalEffect

ACW_DONUT_CTEF_FUND_6TE 3.93 3.17 2.19 2.66 6.12 -3.76 2.36

XUS_DONUT_CTEF_FUND_6TE 2.46 2.72 3.82 3.48 6.28 -4.34 1.94

EM_DONUT_REG9_STAT_6TE 0.27 0.79 4.57 3.76 4.83 -3.15 1.68

EM_DONUT_REG9_FUND_6TE 0.31 0.71 3.82 3.60 4.14 -2.92 1.22

3434


Portfolios Div

iden

dYi

eld

Earn

ings

Yiel

d

Gro

wth

Med

ium

-Te

rmM

omen

tum

Prof

itabi

lity

Size

Valu

e

Vola

tility

ACW_DONUT_CTEF_FUND_6TE 0.03 0.66 -0.16 2.53 0.11 1.00 0.77 0.03

XUS_DONUT_CTEF_FUND_6TE 0.06 0.72 -0.18 2.48 0.22 0.83 0.53 0.06

EM_DONUT_REG9_STAT_6TE 0.43 0.27 0.25 0.06 -0.83 0.84 2.70 0.43

EM_DONUT_REG9_FUND_6TE 0.43 0.27 0.20 -0.03 -0.84 0.74 2.45 0.43

3535


Portfolios

RiskStock

SpecificEffect

RiskStock


RiskFactorsEffect


RiskTotalEffect

RiskTrans.Effect

TotalEffect

XUS_DONUT_REG9_STAT_6TE 0.51 0.58 2.85 2.87 3.35 -4.03 -0.68

ACW_DONUT_REG9_STAT_6TE -0.50 -0.21 2.78 2.47 2.29 -3.51 -1.22

XUS_DONUT_REG9_FUND_6TE 0.18 0.51 2.30 2.39 2.48 -3.86 -1.38

ACW_DONUT_REG9_FUND_6TE -0.57 -0.27 2.34 2.29 1.77 -3.21 -1.43

3636


Portfolios Div

iden

dYi

eld

Earn

ings

Yiel

d

Gro

wth

Med

ium

-Te

rmM

omen

tum

Prof

itabi

lity

Size

Valu

e

Vola

tility

XUS_DONUT_REG9_STAT_6TE 0.22 0.21 0.06 0.30 -0.87 1.27 2.48 0.22

ACW_DONUT_REG9_STAT_6TE 0.23 0.15 0.01 0.42 -1.03 1.84 3.11 0.23

XUS_DONUT_REG9_FUND_6TE 0.15 0.19 0.06 0.23 -0.85 1.21 2.30 0.15

ACW_DONUT_REG9_FUND_6TE 0.14 0.17 -0.03 0.29 -1.03 1.57 2.66 0.14

3737


Portfolios

RiskStock

SpecificEffect

RiskStock


RiskFactorsEffect


RiskTotalEffect

RiskTrans.Effect

TotalEffect

R3000_DONUT_CTEF_STAT_6TE 0.38 0.54 3.39 2.33 3.78 -1.84 1.94

R3000_DONUT_CTEF_FUND_6TE 0.56 0.74 2.71 2.22 3.27 -1.52 1.74

R3000_DONUT_REG9_STAT_6TE 2.43 1.79 0.19 0.59 2.62 -1.85 0.77

R3000_DONUT_REG9_FUND_6TE 1.23 1.06 0.40 0.68 1.63 -1.43 0.20

3838


Portfolios Div

iden

dYi

eld

Earn

ings

Yiel

d

Gro

wth

Med

ium

-Te

rmM

omen

tum

Prof

itabi

lity

Size

Valu

e

Vola

tility

R3000_DONUT_CTEF_STAT_6TE 0.17 1.53 0.00 1.27 -0.20 -0.01 3.87 0.17

R3000_DONUT_CTEF_FUND_6TE 0.15 1.49 0.04 1.06 -0.20 0.02 3.40 0.15

R3000_DONUT_REG9_STAT_6TE 0.11 0.23 -0.17 -0.25 -0.11 -0.73 3.74 0.11

R3000_DONUT_REG9_FUND_6TE -0.02 0.36 -0.14 -0.14 -0.16 -0.71 3.23 -0.02

W H AT D O W E B E L I E V E ?

39

• Diversified Portfolios can Offer Positive Active Returns;

• Financial Anomalies, known in 1988, have Existed and Persisted;

• Portfolio Construction Requires Statistically Significant Tilt;

• Portfolio Constraints are Useful with several Anomalies;

• Higher tracking errors are preferred, no one likes a benchmark hugger

• Portfolio Implementation Donuts allow one to have its cake and eat it too!

• The Vast Source of Active Management is Derived from Forecasted EarningsAcceleration in Stock Selection; Stronger in non-U.S. and EM universes than inU.S. universes; Price Momentum Risk Premium is MUCH Larger in non-U.S. andEM universes than in U.S. universes!

D I S C L A I M E R

40

All information contained herein is believed to be acquired from reliable sources but accuracy cannot be guaranteed. This presentation is for informationalpurposes only, was prepared for academics and financially sophisticated and institutional audiences, and is not intended to represent specific financialservices or recommendations for any targeted investment purposes. McKinley Capital Management, LLC (“McKinley Capital“) is a registered investmentadviser under the Securities and Exchange Commission Investment Advisers Act of 1940. This material may contain confidential and/or proprietaryinformation and may only be relied upon for this report. The data is unaudited and may not correspond to calculated performance for any other client orinvestor in the stated discipline. Neither Mr. Guerard nor McKinley Capital makes any representations or warranties as to the appropriateness or merit of thisanalysis for individual use. Investors must seek individualized professional financial advice before investing.

Investments and commentary were based on information available at the time and are subject to change without notice. Any references to specific indexesare for informational purposes only, may or may not have been owned by McKinley Capital in the past, may or may not be owned by McKinley Capital in thefuture and may or may not be profitable. No one security is profitable all the time and there is always the possibility of selling it at a loss. This is not an offer topurchase or sell any security or service, is not reflective of composite or individual portfolio ownership and may not be relied upon for investment purposes.Past performance is not indicative of future returns.

All returns are gross of investment management fees, broker commissions, taxes, and all other fees, costs and expenses associated with client account tradingand custodial services, and therefore individual returns may be materially negatively affected. Returns do include the reinvestment of gains, dividends andother income. International investing involves special risks including greater economic, political, currency fluctuation and accounting differencesrisks. McKinley Capital’s proprietary investment process considers additional factors such as additional guidelines, restrictions, weightings, allocations, marketconditions and thus returns may at times materially differ from the stated benchmark.

Charts, graphs and other visual presentations and text information are provided for illustrative purposes, derived from internal, proprietary, and/or servicevendor technology sources and/or may have been extracted from other firm data bases. As a result, the tabulation of certain reports may not precisely matchother published data. Certain data may have originated from various third-party, and/or sources including but not limited to Bloomberg, FactSet, Clarifi,MSCI/Barra, Russell, FTSE, broker research, and/or other systems and programs. The authors and/or their employers may use and/or rely on specific indexnames, other financial data and certain analysis without the infringement of copyright materials. However, recipients of this information may not assumethose same rights are transferrable. With regards to any materials accredited to MSCI/Barra: Neither MSCI nor any other party involved in or related tocompiling, computing or creating the MSCI data makes any express or implied warranties or representations with respect to such data (or the results to beobtained by the use thereof), and all such parties hereby expressly disclaim all warranties of originality, accuracy, completeness, merchantability or fitness fora particular purpose with respect to any of such data. Without limiting any of the foregoing, in no event shall MSCI, any of its affiliates or any third partyinvolved in or related to compiling, computing or creating the data have any liability for any direct, indirect, special, punitive, consequential or any otherdamages (including lost profits) even if notified of the possibility of such damages. No further distribution or dissemination of the MSCI data is permittedwithout MSCI’s express written consent.

Please refer to the specific service provider’s website for complete details on all indices. McKinley Capital makes no representation or endorsementconcerning the accuracy or proprietary of information received from any other third party. Future investments may be made under different economicconditions, in different securities and using different investment strategies. International investing also carries additional risks and/or costs including but notlimited to, political, economic, financial market, currency exchange, liquidity, accounting, and trading capability risks. To receive a copy of the McKinleyCapital Form ADV please contact the firm at 3800 Centerpoint Drive, Suite 1100, Anchorage, Alaska 99503, 1.907.563.4488 or visit the firm’s website atwww.mckinleycapital.com. All information is believed to be correct but accuracy cannot be guaranteed.

Documents

Q Group Slides...efficient portfolio construction and management. International Journal of Forecasting, 31, 550-560. 11 Public Model Risk Preferences, 1999-2011 Table 2: Efficient