The Inefficient Market

The Inefficient Market

Prentice Hall 1999

Visit our web-site at HaugenSystems.com

What Pays Off and Why

Part 1: What Pays Off

Abridged

BackgroundBackground

The evolution of academic financeThe evolution of academic finance

The Evolution of Academic FinanceThe Evolution of Academic Finance

1930’s 40’s 50’s 60’s 70’s 80’s 90’s beyond

The Old FinanceThe Old Finance

Theme: Analysis of Financial Statements and the Nature of Financial Claims

Paradigms:Security Analysis Uses and Rights of Financial Claims

(Graham & Dodd) (Dewing)

Foundation: Accounting and Law

The Old Finance

Old Finance

Best investment strategy = – Stock-picking / value-investing approach, such

as Warren Buffett uses

1930’s 40’s 50’s 60’s 70’s 80’s 90’s beyond

The Old Finance

Modern Finance

Bob goes to college

Modern FinanceModern Finance

Theme: Valuation Based on Rational Economic Behavior

Paradigms: Optimization Irrelevance CAPM EMH

(Markowitz) (Modigliani & Miller) (Sharpe, Lintner & Mossen) (Fama)

Foundation: Financial Economics

The Evolution of Academic FinanceThe Evolution of Academic Finance

Modern Finance

Optimal investment strategy = – Invest in index funds, try to match market as

closely as possible at as low a cost as possible

1930’s 40’s 50’s 60’s 70’s 80’s 90’s beyond

The Old Finance

Modern Finance

The New Finance Bob goes to college

The New FinanceThe New Finance

Theme: Inefficient Markets

Paradigms: Inductive ad hoc Factor Models Behavioral Models

Expected Return Risk

(Haugen) (Chen, Roll & Ross) (Kahneman &

Tversky)

Foundation: Statistics, Econometrics, and Psychology

The Evolution of Academic FinanceThe Evolution of Academic Finance

New Finance

Market is inefficient, but hard to beat nonetheless

Optimal investment approach = – Use Markowitz optimization to create optimal

portfolios• APT Risk-factor model to model risk• Ad hoc inductive expected return factor model to model

expected returns

– Quantitative hedge fund, such as• Enhanced index fund• Long / short fund

Hedge Fund Risk/Return Profile

Hedge Funds en totalConvertible Arb

Short biased

Market Neutral

Event Driven

Distressed

Event Driven Multi

Risk Arb

Fixed Income Arb

Global Macro

Long/Short Equity

Managed Futures

Dow Jones

Lehman Aggregate

MSCI EAFE

Nasdaq

Russell 2000

S&P 500

T-Bill

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

18.00%

0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%

Standard Deviation

An

nu

alized

Retu

rn

Ten Years Ending 2/03

Rest of BookRest of Book

Part I: Describes one approach to developing a Part I: Describes one approach to developing a quantitative hedge fundquantitative hedge fund– Focus of this classFocus of this class

Part II: Discusses why that approach worksPart II: Discusses why that approach works– Chapters 9 – 12 won’t be covered in class, but can Chapters 9 – 12 won’t be covered in class, but can

read for own pleasureread for own pleasure

Part I:What Pays Off

Probability Distribution For Returns to a PortfolioProbability Distribution For Returns to a Portfolio

Possible Rates of Returns

Probability

Expected Return

Variance of Return

Risk Factor ModelsRisk Factor Models

The variance of stock returns can be split into two components: Variance = systematic risk + diversifiable risk

Systematic risk is modeled using an APT-type risk-factor model

Measures extent to which stocks’ returns [jointly] move up and down over time

Estimated using time-series data

Diversifiable risk is reduced through optimal diversification

Expected Return Factor Expected Return Factor ModelsModels

Expected return factor models measure / predict the extent to which the stocks’ returns are different from each other within a given period of time.

Expected Return Factor Expected Return Factor ModelsModels

The factors in an expected return model represent the character of the companies.

They might include the history of their stock prices, its size, financial condition, cheapness or dearness of prices in the market, etc.– Unlike CAPM and APT, not only risk factors such as

market beta or APT betas are included

Factor payoffs are estimated by relating individual stock returns to individual stock characteristics over the cross-sectioncross-section of a stock population (here the largest 3000 U.S. stocks).

Five Factor FamiliesFive Factor Families

Risk – Market and APT betas, TIE, debt ratio, etc.,

values and trends thereof Liquidity

– Market cap., price, trading volume, etc. Price level

– E/P, B/P, Sales/P, CF/P, Div/P Profitability

– Profit margin, ROE, ROA, earnings surprise, etc. Price history (technical factors)

– Excess return over past 1, 2, 3, 6, 12, 24, & 60 months

The Most Important FactorsThe Most Important Factors

The monthly slopes (payoffs) are averages over the period 1979 through mid 1986.

“T” statistics on the averages are computed, and the stocks are ranked by the absolute values of the “Ts”.

Most Important FactorsMost Important Factors

1979/01 through1979/01 through1986/061986/06

1986/07 through 1993/121986/07 through 1993/12

FactorFactor MeanMean ConfidenceConfidence MeanMean ConfidenceConfidence

One-month excess returnOne-month excess return -0.97%-0.97% 99%99% -0.72%-0.72% 99%99%

returnreturnTwelve-month excessTwelve-month excess 0.52%0.52% 99%99% 0.52%0.52% 99%99%

Trading volume/marketTrading volume/marketcapcap

-0.35%-0.35% 99%99% -0.20%-0.20% 98%98%

Two-month excess returnTwo-month excess return -0.20%-0.20% 99%99% -0.11%-0.11% 99%99%

Earnings to priceEarnings to price 0.27%0.27% 99%99% 0.26%0.26% 99%99%

Return on equityReturn on equity 0.24%0.24% 99%99% 0.13%0.13% 97%97%

Book to priceBook to price 0.35%0.35% 99%99% 0.39%0.39% 99%99%

Trading volume trendTrading volume trend -0.10%-0.10% 99%99% -0.09%-0.09% 99%99%

Six-month excess returnSix-month excess return 0.24%0.24% 99%99% 0.19%0.19% 99%99%

Cash flow to priceCash flow to price 0.13%0.13% 99%99% 0.26%0.26% 99%99%

The Most Important FactorsThe Most Important Factors

Among the factors that are significant (i.e., that can be used to distinguish between which companies will have higher returns and which will have lower returns) are:

– A number of liquidity factors

– Various fundamental factors, indicating value with growth

– Technical factors, indicating short-term reversals and intermediate term momentum

• Suggest that technical factors provide marginal value when used in conjunction with fundamental analysis

– Notably, no CAPM or APT risk factors are included!

Projecting Expected Projecting Expected ReturnReturn

The components of expected return are obtained by multiplying the projected payoff to each factor (here the average of the past 12) by the stock’s current exposure to the factor.

Exposures are measured in standard deviations from the cross-sectional mean.

The individual components are then summed to obtain the aggregate expected return for the next period (here a month).

Factor Exposure Payoff ComponentBook\Price 1.5 S.D. x 20 B.P. = 30 B.P.

Short-Term Reversal 1.0 S.D. x -10 B.P. = -10 B.P.. . . .. . . .. . . .. . . .. . . .. . . .

Estimating Expected Stock Estimating Expected Stock ReturnsReturns

Trading Volume -2 S.D. x -20 B.P. = 40 B.P.Total Excess ReturnTotal Excess Return 80 B.P.80 B.P.

The Model’s Out-of-sample The Model’s Out-of-sample Predictive PowerPredictive Power

The 3000 stocks are ranked by expected return and formed into deciles (decile 10 highest).

The performance of the deciles is observed in the next month.

The expected returns are re-estimated, and the deciles are re-ranked.

The process continues through 1993.

Logarithm of Cumulative Decile PerformanceLogarithm of Cumulative Decile Performance

1

2

3

45

678

9

10

-1

-0.5

0

0.5

1

1.5

2

2.5

80Q1 81Q1 82Q1 83Q1 84Q1 85Q1 86Q1 87Q1 88Q1 89Q1 90Q1 91Q1 92Q1 93Q1 94Q1 95Q1 96Q1 97Q1 98Q1

Date

33 44 55 66 77 88 99 1010DecileDecile

-40%-40%

-30%-30%

-20%-20%

-10%-10%

0%0%

10%10%

20%20%

30%30%

00 11 22

Realized ReturnRealized Return

Realized Return for 1984 by DecileRealized Return for 1984 by Decile

(Y/X = 5.5%)(Y/X = 5.5%)

YY

XX

Extension of Study to Other Extension of Study to Other PeriodsPeriods

Nardin BakerNardin Baker The same family of factors is used on a

similar stock population.

Years before and after initial study period are examined to determine slopes and spreads between decile 1 and 10.

19971997

0%0%

10%10%

20%20%

30%30%

40%40%

50%50%

60%60%

70%70%

80%80%

90%90%

100%100%

19751975 19771977 19791979 19811981 19831983 19851985 19871987 19891989 19911991 19931993 19951995

YearsYears

19981998

differencedifference

slopeslope

Slope and SpreadSlope and Spread

Decile Risk CharacteristicsDecile Risk Characteristics

The characteristics reflect the character of the deciles over the period 1979-1993.

Fama-FrenchFama-FrenchThree- Factor ModelThree- Factor Model

Monthly decile returns are regressed on monthly differences in the returns to the following:– S&P 500 and T bills

– The 30% of stocks that are smallest and largest

– The 30% of stocks with highest book-to-price and the lowest.

Sensitivities (Betas) to Market ReturnsSensitivities (Betas) to Market Returns

1010

DecileDecile

11 22 33 44 55 66 77 88 99

0.950.95

11

1.051.05

1.11.1

1.151.15

1.21.2

1.251.25

Market BetaMarket Beta

Sensitivities (Betas) to Relative Performance of Small and Large StocksSensitivities (Betas) to Relative Performance of Small and Large Stocks

22 33 44 55 66 77 88 99 1010DecileDecile00

0.10.1

0.20.2

0.30.3

0.40.4

0.50.5

11

Size BetaSize Beta

Sensitivities (Betas) to RelativeSensitivities (Betas) to Relative Performance of Value and Growth StocksPerformance of Value and Growth Stocks

DecileDecile88 99 1010

11 22 33 44 55 66 77

-0.2-0.2

-0.1-0.1

00

0.10.1

0.20.2

0.30.3

Value/GrowthValue/Growth BetaBeta

Fundamental Fundamental CharacteristicsCharacteristics

Averaged over all stocks Averaged over all stocks in each decile and over all in each decile and over all

months (1979-83).months (1979-83).

RiskRisk

Decile Risk CharacteristicsDecile Risk Characteristics

Debt to Equity

1.03 0.85

StockVolatility

1 2 3 4 5 6 7 8 9 10

Decile

0%0

1

2

3

4

5

6

7

8

Interest CoverageMarket Beta

Debt to Equity

Volatility

41.42%

33.22%

10%

20%

30%

40%

50%

Coverage

1.76

6.63

Beta 1.001.21

LiquidityLiquidity

Size and Liquidity CharacteristicsSize and Liquidity Characteristics

$0$0

$10$10

$20$20

$30$30

$40$40

$50$50

$60$60

$70$70

11 22 33 44 55 66 77 88 99 1010

DecileDecile

Stock PriceStock Price

Trading VolumeTrading Volume

$400$400

$500$500

$600$600

$700$700

$800$800

$900$900

$1,000$1,000

$1,100$1,100

SizeSize

$14.93$14.93

$30.21$30.21

PricePrice

$470$470

$1011$1011

SizeSize

$42.42$42.42

$60.89$60.89

Trading VolumeTrading Volume

Price HistoryPrice History

Technical HistoryTechnical History

11 22 33 44 55 66 77 88 99 1010

DecileDecile

-20%-20%

-10%-10%

0%0%

10%10%

20%20%

30%30%

Excess ReturnExcess Return

2 months2 months

-1.80%-1.80%

1.21%1.21%

12 months12 months

-15.74%-15.74%

30.01%30.01%

3 months3 months

-6.89%-6.89%

8.83%8.83%

6 months6 months

-12.14%-12.14%

16.60%16.60%

1 month1 month

0.09%0.09%

-0.14%-0.14%

ProfitabilityProfitability

Current ProfitabilityCurrent Profitability

Asset TurnoverAsset Turnover115%115%

Return on EquityReturn on Equity15.39%15.39%

Profit MarginProfit Margin 7.86%7.86%

Return on AssetsReturn on Assets 6.50%6.50%

90%90%

100%100%

110%110%

120%120%

Asset TurnoverAsset Turnover

22 33 44 55 66 77 88 99 1010

DecileDecile

80%80%-10%-10%

0%0%

10%10%

20%20%

11

Profit Margin Profit Margin Return on Assets Return on Assets Return on Equity Return on Equity Earnings GrowthEarnings Growth

Earnings GrowthEarnings Growth 0.95%0.95%

Trends in Trends in ProfitabilityProfitability

1 2 3 4 5 6 7 8 9 10

DecileDecile

5 Year 5 Year Trailing GrowthTrailing Growth

-1.5%

-1.0%

-0.5%

0.0%

Profitability TrendsProfitability Trends(Growth In)

Asset Turnover -0.13%

Profit Margin

-0.95% Return on Assets

-1.11% Return on Equity

-1.18%

Cheapness in Cheapness in Stock PriceStock Price

Price Level Price Level

Sales-to-PriceSales-to-Price214%214%

207%207%

Cash Flow-to-Price

6%

17%

Earnings-to-Price

-1.55%

10%

Dividend-to-Price2.19%

3.69%

50%

100%

150%

200%

Sales-to-Price Book-to-Price

3 4 5 6 7 8 9 10

Decile

0%-10%

0%

10%

20%

1 2

Cash Flow-to-Price Earnings-to-Price Dividend-to-Price

Book-to-Price81%

80%

Simulation of Simulation of Investment PerformanceInvestment Performance

Efficient portfolios are constructed Efficient portfolios are constructed quarterly, assuming 2% round-trip quarterly, assuming 2% round-trip transactions costs within the Russell 1000 transactions costs within the Russell 1000 population.population.– Turnover controlled to 20% to 40% per annum.Turnover controlled to 20% to 40% per annum.– Maximum stock weight is 5%.Maximum stock weight is 5%.– No more that 3X S&P 500 cap weight in any stock.No more that 3X S&P 500 cap weight in any stock.– Industry weight to within 3% of S&P 500.Industry weight to within 3% of S&P 500.– Turnover controlled to within 20% to 40%.Turnover controlled to within 20% to 40%.

10%10%

12%12%

14%14%

18%18%

16%16%

20%20%

12%12%

An

nu

aliz

ed

tota

l re

turn

An

nu

aliz

ed

tota

l re

turn

17%17% 18%18%13%13% 14%14% 15%15% 16%16%

Annualized volatility of returnAnnualized volatility of return

1000 1000 IndexIndex

GGII

HH

LL

Optimized Portfolios in the Russell 1000 Optimized Portfolios in the Russell 1000 PopulationPopulation 1979-19931979-1993

PPossible Sources of ossible Sources of BiasBias

Survival bias:Survival bias:– Excluding firms that go inactive during test period.Excluding firms that go inactive during test period.

Look-ahead bias:Look-ahead bias:– Using data that was unavailable when you trade.Using data that was unavailable when you trade.

Bid-asked bounce:Bid-asked bounce:– If this month’s close is a bid, there is 1 chance in 4 that next If this month’s close is a bid, there is 1 chance in 4 that next

and last month’s close will be at an asked, showing and last month’s close will be at an asked, showing reversals.reversals.

Data snooping:Data snooping:– Using the results of prior studies as a guide and then Using the results of prior studies as a guide and then

testing with their data.testing with their data. Data mining:Data mining:

– Spinning the computer.Spinning the computer.

Using the Ad Hoc Expected Using the Ad Hoc Expected Return Factor Model Return Factor Model

InternationallyInternationally The most important factors across the 5 The most important factors across the 5

largest stock markets (1985-93).largest stock markets (1985-93). Simulating investment performance:Simulating investment performance:

– Within countries, constraints are those stated Within countries, constraints are those stated previously.previously.

– Positions in countries are in accord with Positions in countries are in accord with relative total market capitalization.relative total market capitalization.

Mean Payoffs and Confidence Probabilities for theMean Payoffs and Confidence Probabilities for theTwelve Most Important Factors of the World (1985-93)Twelve Most Important Factors of the World (1985-93)

One-month stock return

Book to price

Twelve-month stock return

Cash flow to price

Earnings to price

Sales to price

Three-month stock return

Debt to equity

Variance of total return

Residual variance

Five-year stock return

Return on equity

United StatesUnited States

Mean Confidence Level(DifferentFrom Zero)

-0.32% 99%

0.14% 99%

0.23% 99%

0.18% 99%

0.16% 99%

0.08% 99%

-0.01% 38%

-0.06% 96%

-0.06% 94%

-0.08% 99%

-0.01% 31%

0.11% 99%

GermanyGermany

Mean Confidence Level(DifferentFrom Zero)

-0.26% 99%

0.16% 99%

0.08% 99%

0.08% 99%

0.04% 83%

0.10% 99%

-0.14% 99%

-0.06% 96%

-0.04% 83%

-0.04% 80%

-0.02% 51%

0.01% 31%

FranceFrance

Mean Confidence Level(DifferentFrom Zero)

-0.33% 99%

0.18% 99%

0.12% 99%

0.15% 99%

0.13% 99%

0.05% 99%

-0.08% 99%

-0.09% 99%

-0.12% 99%

-0.09% 99%

-0.06% 94%

0.10% 99%

United United KingdomKingdom

Mean Confidence Level(DifferentFrom Zero)

-0.22% 99%

0.12% 99%

0.21% 99%

0.09% 99%

0.08% 99%

0.05% 91%

-0.08% 99%

-0.10% 99%

-0.01% 38%

-0.03% 77%

-0.06% 96%

0.04% 80%

JapanJapan

Mean Confidence Level(Different

From Zero)-0.39% 99%

0.12% 99%

0.04% 86%

0.05% 91%

0.05% 94%

0.13% 99%

-0.26% 99%

-0.01% 31%

-0.11% 99%

0.00% 8%

-0.07% 98%

0.05% 92%

Optimization in France, Germany, U. K., Japan and Optimization in France, Germany, U. K., Japan and across the five largest countries. 1985-1994across the five largest countries. 1985-1994

19.0%19.0%

17.0%17.0%

15.0%15.0%

13.0%13.0%

11.0%11.0%

9.0%9.0%

7.0%7.0%

5.0%5.0% 10% 12% 14% 16% 18% 20% 22% 24%

G

I

HFranceFrance

FranceFranceindexindex

U. K.U. K. H

I

G U. K.U. K.indexindex

GermanyGermany

GermanyGermanyindexindex

H

I

G

JapanJapan

H

I

GG

JapanJapanindexindex

five largest five largest countries countries

(including U.S.)(including U.S.)

H

I

G

index ofindex offive largestfive largestcountriescountries

Annualized Annualized total total

returnreturn

Annualized volatility of returnAnnualized volatility of return

Expansion of the Expansion of the 1996 Study1996 Study

Nardin BakerNardin Baker

Performance In Different CountriesPerformance In Different Countries 1985 - 1998 1985 - 1998 (September)(September)

0%0%

5%5%

10%10%

15%15%

20%20%

25%25%

30%30%

12%12% 14%14% 16%16% 18%18% 20%20% 22%22% 24%24% 26%26% 28%28% 30%30% 32%32%

VolatilityVolatility

ReturnReturn

AUS BEL CAN CHE DEU ESP FRAGBR HKG ITA JPN NLD SWE USA

Actual Actual PerformancePerformance

Performance before fees, after transactions costs and includes reinvested dividendsPerformance before fees, after transactions costs and includes reinvested dividendsIndustrifinans Contact: Ole Jakob Wold +47.22.473300Industrifinans Contact: Ole Jakob Wold +47.22.473300 Measured in Norwegian Krone (NOK), Managed to stay neutral in country and sector weightsMeasured in Norwegian Krone (NOK), Managed to stay neutral in country and sector weightsPast performance is not a guarantee of future resultsPast performance is not a guarantee of future results Managed using modified (Haugen-Baker) JFE Expected Return Model by Baker at Grantham Mayo Van Otterloo, Inc.Managed using modified (Haugen-Baker) JFE Expected Return Model by Baker at Grantham Mayo Van Otterloo, Inc.

Industrifinans ForvaltningIndustrifinans ForvaltningGlobal FundGlobal Fund

170.65%170.65%

144.04%144.04%

-20%-20%

0%0%

20%20%

40%40%

60%60%

80%80%

100%100%

120%120%

140%140%

160%160%

180%180%

jan.95jan.95aprapr juljul octoct jan.96jan.96aprapr juljul octoct jan.97jan.97aprapr juljul octoct jan.98jan.98aprapr juljul octoct jan.99jan.99aprapr

Cumulative return since inception (31 October 1994Cumulative return since inception (31 October 1994))

Industrifinans WorldIndustrifinans World

Morgan Stanley World NOKMorgan Stanley World NOK

Industrifinans ForvaltningIndustrifinans Forvaltning

Probability that the expected return to the Global Fund Probability that the expected return to the Global Fund has been higher than the Morgan Stanley World Indexhas been higher than the Morgan Stanley World Index

92.2%92.2%

0%0%

10%10%

20%20%

30%30%

40%40%

50%50%

60%60%

70%70%

80%80%

90%90%

100%100%

Performance measured before fees, after transactions costs and includes reinvested dividendsPerformance measured before fees, after transactions costs and includes reinvested dividendsIndustrifinans Contact: Ole Jakob Wold +47.22.473300Industrifinans Contact: Ole Jakob Wold +47.22.473300 Measured in Norwegian Krone (NOK), Managed to stay neutral in country and sector weightsMeasured in Norwegian Krone (NOK), Managed to stay neutral in country and sector weightsPast performance is not a guarantee of future resultsPast performance is not a guarantee of future resultsManaged using modified (Haugen-Baker) JFE Expected Return Model by Baker at Grantham Mayo Van Otterloo, Inc.Managed using modified (Haugen-Baker) JFE Expected Return Model by Baker at Grantham Mayo Van Otterloo, Inc.

dec.94dec.94marmar junjun sepsep dec.95dec.95marmar junjun sepsep dec.96dec.96marmar junjun sepsep dec.97dec.97marmar junjun sepsep dec.98dec.98marmar

Probability of out-performing the Morgan Stanley World Index since inception (31 October 1994)Probability of out-performing the Morgan Stanley World Index since inception (31 October 1994)

130.31%130.31%

Analytic InvestorsAnalytic InvestorsEnhanced Equity Institutional CompositeEnhanced Equity Institutional Composite

102.73%102.73%

0%0%

20%20%

40%40%

60%60%

80%80%

100%100%

120%120%

140%140%

AI Contact: Dennis Bein 213.688.3015AI Contact: Dennis Bein 213.688.3015 Performance before fees, after transactions costs and includes reinvested dividendsPerformance before fees, after transactions costs and includes reinvested dividendsPast performance is not a guarantee of future resultsPast performance is not a guarantee of future results Managed using Haugen expected return model & Barra optimizer & risk modelManaged using Haugen expected return model & Barra optimizer & risk model

nov.96nov.96jan.97jan.97 marmar maymay juljul sepsep novnov jan.98jan.98 marmar maymay juljul sepsep novnov jan.99jan.99 marmar

Cumulative return since inception (30 Sep 1996)Cumulative return since inception (30 Sep 1996)

Institutional CompositeInstitutional Composite

S&P 500S&P 500

Analytic InvestorsAnalytic Investors

Probability that the expected return to the Enhanced Equity Probability that the expected return to the Enhanced Equity Institutional Composite has been higher than the S&P 500 IndexInstitutional Composite has been higher than the S&P 500 Index

93.3%93.3%

0%0%

10%10%

20%20%

30%30%

40%40%

50%50%

60%60%

70%70%

80%80%

90%90%

100%100%

AI Contact: Dennis Bein 213.688.3015AI Contact: Dennis Bein 213.688.3015 Performance before fees, after transactions costs and includes reinvested dividendsPerformance before fees, after transactions costs and includes reinvested dividendsPast performance is not a guarantee of future resultsPast performance is not a guarantee of future results Managed using Haugen expected return model & Barra optimizer & risk modelManaged using Haugen expected return model & Barra optimizer & risk model

nov.96nov.96 feb.97feb.97 maymay augaug novnov feb.98feb.98 maymay augaug novnov feb.99feb.99

Probability of out-performing the S&P 500 Index since inception (30 Sep 1996)Probability of out-performing the S&P 500 Index since inception (30 Sep 1996)

Performance of 413 Mutual Performance of 413 Mutual Funds 10/96 - 9/98Funds 10/96 - 9/98

““T” stat. on mean monthly out-performance T” stat. on mean monthly out-performance to S&P 500.to S&P 500.

Large funds with highest correlation with Large funds with highest correlation with S&P with a 36 month history.S&P with a 36 month history.

Three YearThree Year OutOut--(Under)(Under)-Performance T-Distribution-Performance T-Distribution

0%0%

5%5%

10%10%

15%15%

20%20%

25%25%

to -5.0 -5.0 to-4.5

-4.5 to-4.0

-4.0 to-3.5

-3.5 to-3.0

-3.0 to-2.5

-2.5 to-2.0

-2.0 to-1.5

-1.5 to-1.0

-1.0 to-0.5

-0.5 to0.0

0.0 to0.5

0.5 to1.0

1.0 to1.5

1.5 to2.0

2.0 to

T-statistics for mean T-statistics for mean outout--(under)(under) performance performance

Per

cen

t o

f sa

mp

leP

erce

nt

of

sam

ple

Part II:Why

Can read Chapters Can read Chapters 9 through 12 at 9 through 12 at

your own leisure.your own leisure.

The Great Race(From Ch. 13)

A Test of RelativeA Test of Relative Predictive Power Predictive Power

1980 -19971980 -1997

Model employing factors Model employing factors exploiting the market’s tendencies exploiting the market’s tendencies

to over- and under-reactto over- and under-react

vs.vs.

Models employing risk factors only Models employing risk factors only (“deductive” models of modern (“deductive” models of modern

finance).finance).

The The Ad HocAd Hoc Expected Expected Return Factor ModelReturn Factor Model

RiskRisk LiquidityLiquidity ProfitabilityProfitability Price levelPrice level Price historyPrice history Earnings revision and surpriseEarnings revision and surprise

Decile Returns for the Ad Hoc Factor Model Decile Returns for the Ad Hoc Factor Model (1980 through mid 1997)(1980 through mid 1997)

2 3 4 5 6 7 8 9 10Decile0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

1

AverageAverage Annualized Annualized

ReturnReturn

The Capital Asset The Capital Asset Pricing ModelPricing Model

Market beta measured over the trailing Market beta measured over the trailing 3 to 5-year 3 to 5-year periods).periods).

Stocks ranked by beta and formed into Stocks ranked by beta and formed into deciles monthly.deciles monthly.

Decile Returns for CAPM ModelDecile Returns for CAPM Model

33 44 55 66 77 88 99 1010 DecileDecile0%0%

5%5%

10%10%

15%15%

20%20%

25%25%

30%30%

35%35%

40%40%

45%45%

11 22

Average Average Annualized Annualized

ReturnReturn

The Arbitrage Pricing The Arbitrage Pricing TheoryTheory

Macroeconomic FactorsMacroeconomic Factors– Monthly T-bill returnsMonthly T-bill returns

– Long-term T-bond returns less short-termLong-term T-bond returns less short-term

– T-bond returns less low-gradeT-bond returns less low-grade

– Monthly inflationMonthly inflation

– Monthly change in industrial productionMonthly change in industrial production Beta EstimationBeta Estimation

– Betas re-estimated monthly by regressing stock returns Betas re-estimated monthly by regressing stock returns on economic factors over trailing 3-5 yearson economic factors over trailing 3-5 years

Payoff ProjectionPayoff Projection– Next month’s payoff is average of trailing 12 monthsNext month’s payoff is average of trailing 12 months

Average Returns for APT ModelAverage Returns for APT Model

Annualized Annualized

22 33 44 55 66 77 88 99 1010 DecileDecile0%0%

5%5%

10%10%

15%15%

20%20%

25%25%

30%30%

35%35%

40%40%

45%45%

11

Average Average

ReturnReturn

Overall ResultsOverall Results Ad Hoc Expected Return Factor Model

– Average Annualized Spread Between Deciles 1 & 10 46.04%46.04%– Years with Negative Spreads 0 years

Models Based on MODERN FINANCE– CAPM

• Average Annualized Spread Between Deciles 1 & 10 -6.94%-6.94%• Years with Negative Spreads 13 years

– APT• Average Annualized Spread Between Deciles 1 & 10 6.06%• Years with Negative Spreads 6 years

Getting to Heaven and Hell in the Stock Market(From Ch. 14)

The Position of Portfolios in Abnormal Profit SpaceThe Position of Portfolios in Abnormal Profit Space

Effici

ent M

arke

t

Effici

ent M

arke

t

Line

Line

TrueTrue Abnormal Profit Abnormal Profit

Super StocksSuper Stocks

Stupid StocksStupid Stocks

PricedPriced Abnormal ProfitAbnormal Profit

The Position of Portfolios in Abnormal Profit SpaceThe Position of Portfolios in Abnormal Profit Space

Effici

ent M

arke

t

Effici

ent M

arke

t

Line

Line

TrueTrue Abnormal Profit Abnormal Profit

Investment Investment HeavenHeaven

Stupid StocksStupid Stocks

PricedPriced Abnormal Profit Abnormal Profit

The Position of Portfolios in Abnormal Profit SpaceThe Position of Portfolios in Abnormal Profit Space

Effici

ent M

arke

t

Effici

ent M

arke

t

Line

Line

TrueTrue Abnormal Profit Abnormal Profit

Investment Investment HeavenHeaven

InvestmentInvestmentHellHell

PricedPriced Abnormal Profit Abnormal Profit

The Position of Portfolios in Abnormal Profit SpaceThe Position of Portfolios in Abnormal Profit Space

Effici

ent M

arke

t

Effici

ent M

arke

t

Line

Line

TrueTrue Abnormal Profit Abnormal Profit

Investment Investment HeavenHeaven

InvestmentInvestmentHellHell

PricedPriced Abnormal ProfitAbnormal Profit

Can’t get to heaven by going around

the corner

You must go directly to heaven

How do you get to How do you get to Investment Heaven?Investment Heaven?

Three main steps:Three main steps:– Use risk factor models to estimate variances and Use risk factor models to estimate variances and

covariancescovariances– Use ad hoc expected return factor models to Use ad hoc expected return factor models to

determine desired stock characteristics and determine desired stock characteristics and estimate expected returnsestimate expected returns

• Cannot just screen sequentially (“going around the Cannot just screen sequentially (“going around the corner”) for stocks with the desired characteristicscorner”) for stocks with the desired characteristics

– Combine this information into optimal portfolios Combine this information into optimal portfolios through Markowitz optimizationthrough Markowitz optimization