1

1) UNCONDITIONAL CAPM

2) CRUCIFIXION OF THE CAPM

Fama, E.F. and K.R. French, «The Cross-Section of Expected Stock Returns», TheJournal of Finance, Vol.46, No.2, Juin 1992, pp 427-465.

Fama, E.F. and K.R. French, «The CAPM is wanted, dead or alive», The Journal ofFinance, Vol.LI, No.5, December 1996, pp 1947-1958.

3) FROM THE CAPM TO A THREE-FACTOR MODEL ?

Fama E. F. and K. R. French, “Multifactor explanations of asset pricing anomalies”, TheJournal of Finance, Vol. LI, No.1, March 1996, 55-83

Fama E. F. and K. R. French, “Value versus growth: the international evidence”,Working paper, 1997.

2

4) RESUSCITATION OF THE CAPM : FROM AN UNCONDITIONAL CAPM TO ACONDITIONAL CAPM

«The CAPM is alive and well»

Jagannathan and Wang, «The conditional CAPM and the cross-section of expectedreturns», The Journal of Finance, Vol. LI, No,1, March 1996, pp 3-53.

Pettengill, Sundaram et Mathur, «The conditional relation between beta and return»,Journal of Financial and Quantitative Analysis, March 1995, pp 101-116.

5) CONCLUSION : ESTIMATION OF COST OF EQUITY

Fama, E.F. et K.R. French, «Industry costs of equity», Journal of Financial Economics,Vol.43, 1997, pp 153-193.

3

1 CAPM INCONDITIONNEL

CAPM : E(Rit) = Rft + βi*(E(Rmt) - Rft) (1)

Expressed in terms of expectations ⇒ ex-ante model

However, expected returns are not observable and we must use ex-post data⇒ ex-post version of the CAPM

Assumption : on average, expected returns are realized (Market Model or Single IndexModel: Rit = ai + βi*Rmt + eit et E(Rit) = ai + βi*E(Rmt) , see BKMRP pp 353-354)

Rit = E(Rit) + βi*πit + eit (2)

Where πit = Rmt - E(Rmt)

Rit different from E(Rit), for Rmt different from E(Rmt) and firm-specific factors

4

Substitution of (1) in (2)

Rit = Rft + βi*(E(Rmt) - Rft) + βi*[Rmt - E(Rmt)] + eit

Ex-post version of the CAPM (time series model)

Rit - Rft = βi*(Rmt - Rft) + eit (3)

Or Rit - Rft = ai + bi*(Rmt - Rft) + eit (3')

5

1.1 Test du CAPM inconditionnel au Canada (OUMAR SY, tableaux.doc)

1.1.1 Statistiques descriptives

Données mensuelles: TSE Western

40 ans, 480 mois : janvier 1956 à décembre 1995.

Tableaux I.1 et I.2

1.1.2 Forme testable du CAPM

itit10fit eˆRR +βγ+γ=− (4)

Hypothèses: 0γ̂ = 0 et 1γ̂ = E(Rm)- Rf

itˆet βitR représentent respectivement le rendement du titre (ou portefeuille) i au temps t

et un estimé du risque systématique du titre i provenant d’une étape antérieure.

6

Vraie relation :

ititfit eRR ++=− βγγ 10 (5)

Erreur de mesure :

ititit v+= ββ̂ (6)

Estimation :

ititfit uRR ++=− βγγ ˆ10 (7)

7

1

2

21

22

21

1

1lim γ

σσ

γσσ

σγγ <

+=

+=

∧

vv

p

xyvoirrrpv

fm 10

2

21

0 ,1*1

)(lim γγ

σσ

γγ −>

+−−=

∧

⇒ problème de l'erreur de mesure du risque systématique (Miller and Scholes, 1972) :l'ordonnée à l'origine est surestimée et la pente sous-estimée

8

Comment règler ce problème ?

Regroupement des titres en portefeuilles sur la base d’un critère corrélé avec le vrai bêta,mais non corrélé avec l’erreur de mesure.

Si les erreurs de mesure ne sont pas corrélées entre elles, plus le nombre de titrescomposant les portefeuilles croît, plus la variance de l’erreur de mesure des bêtas deportefeuilles tend vers zéro.

Critères possibles: taille ou ßt-1

Taille : mauvais critère, car corrélé avec l’erreur de mesure

ßt-1 individuel estimé sur une période ne chevauchant pas la période d’estimation desbêtas de portefeuille : bon critère.

Si les rendements ne sont pas corrélés dans le temps, les erreurs de mesure dansles bêtas estimés ne le sont pas non plus.

9

1.1.3 Méthodologie en 3 étapes (Fama et MacBeth, 1973)

ETAPE I: estimation des bêtas individuels sur la période de formation (5 ans)

360 périodes de formation (première: janvier 1956 à décembre 1960; dernière:décembre 1985 à novembre 1990) ⇒ perte des 60 premiers mois.

Tri ascendant des bêtas individuels en janvier de chaque année afin de former 20portefeuilles.

10

ETAPE II : estimation des bêtas et des rendements de portefeuille sur la périoded'estimation (5 ans)

Calcul des bêtas mensuels moyens de chaque portefeuille (ou bêta de portefeuille) etdes rendements mensuels équipondérés sur les 5 ans subséquents

⇒ 360 périodes d'estimation (début: janvier 1961 à décembre 1965; fin : décembre1990 à novembre 1995)

⇒ perte des 120 premiers mois.

11

ETAPE III : estimation de (4) sur périodes de tests (360 mois):

Estimation en 2 étapes (FM) : 360 régressions en coupe transversale, puis estimationde la moyenne des primes mensuelles.

Estimation en 1 étape (on empile les données indépendamment du mois considéré ⇒360*20 observations): permet d’augmenter le nombre d’observations, d’obtenir destests plus puissants et un R2 et F global. Permet aussi tests sur effets conditionnels.

Voir tableau illustratif à la fin du document (p.48)

12

1.1.4 Résultats

Tableau II.1

Les primes estimées 1ã sont toujours inférieures à la moyenne observée sur lemarché et ne sont pas significatives

L'ordonnée à l'origine 0ã̂ est toujours positive

Meilleurs résultats avec l’indice équipondéré.

Effets mensuels: Tableau II.3A et II.3B

eˆD + R R pnpnm1m

12

1=m0fnpn + βγ∑γ=− ⋅⋅

où Dm = 1 si le mois est égal à m et 0 sinon, m=1 à 12

13

2 CRUCIFIXION OF THE CAPM

Fama, E.F. et K.R. French, «The Cross-Section of Expected Stock Returns», The Journalof Finance, Vol.46, No.2, Juin 1992, pp 427-465.Fama, E.F. and K.R. French, «The CAPM is wanted, dead or alive», The Journal ofFinance, Vol.LI, No.5, December 1996, pp 1947-1958.

The two main implications of the CAPM is that the value-weight market portfolio, M, is

mean-variance efficient ⇒

i) Beta, the slope in the regression of a security's return on the market return, is the

only risk needed to explain expected return

ii) There is a positive expected premium for beta risk

Main point : evidence of (ii) is support for the CAPM only if (i) holds

14

However, there exists many anomalies in the literature.

FF, 1992

Size (<0) ME Banz, 1981Earnings /price (U) E/P Basu, 1983Book-to-market equity (>0) B/M Rosenberg, Reid, and Landstein,1985

Chan, Hamao et Lakonishok, 1991Leverage (>0) AME, ABE Bhandari, 1988

FF, 1993, 1995, 1996

Cash flow/price C/PPast sales growth PSG Lakonishok, Shleifer and Vishny, 1994Long-term past return LTPR DeBondt and Thaler, 1985Short-term past return STPR Jegadeesh and Titman, 1993; Asness, 1994

Analysts' forecasts AF Womack, 1995; La Porta, 1996Equity issues IPO, SEO Loughran and Ritter, 1995

15

2 main results

i) The relation between beta and average return disappears during the more recent

1963-1990 period, even when beta is used alone to explain average returns. The

relation is also weak in the 50-year 1941-1990 period.

Tests do not support the most basic prediction of the SLB model, that average

returns are positively related to market betas.

Betas does not seem to help explain the cross-section of average returns.

ii) Two easily measured variables, size and book-to-market equity, combine to

capture the cross-sectional variation returns associated with market beta.

The combination of size and book-to-market equity seems to absorb the roles of

leverage and E/P in average stock returns, at least during the 1963-1990 period.

16

2.1 Preliminaries

CRSP and COMPUSTAT: 1963-1990 and 1962-1989

Six-month gap between fiscal yearend (accounting data) and annual returns

Rit = α0t+α1tßit-1+α2tLN(ME)it-1 + α3tLN(BE/ME)it-1 + α4t(E/P)it-1 + α5tDummy(E-/P)it-1 + eit-1

2.2 Beta and size

Measurement error in individual beta ⇒ portfolio beta

However individual beta are needed here ! Reassignment that is not standard !

Betas are highly correlated to size (negatively)

Orthogonalization : sort on size (10 portfolios), then sort on pre-ranking betas (10

portfolios) ⇒ 100 portfolios.

17

2.2.1 Informal tests

Table II, p.436

- Sort on size alone, panel A (12 portfolios: 1A, 1B to 10A, 10B) : negative relation

between size and return; positive relation between ß and return

- Sort on ß alone, panel B: no relation between ß and returns

Table I, p.434-435

- Although the post-ranking ßs increase strongly in each size decile (panel B, rows),

average returns are flat (panel A, rows) ⇒ The second-pass ß sort produces little

variation in average returns.

- The two-pass sort on size and ß says that variation in ß that is tied to size is positively

related to average return (panel A, columns), but variation in ß unrelated to size is not

compensated in average returns

18

2.2.2 Fama-MacBeth regressions

Table III, p 439

- size helps explain (-0.15%/month) the cross-section of average stock returns, no

matter which explanatory variables are in the regressions

- ß does not help explain the cross-section of average stock returns. When size is

included, the slope is even negative.

2.2.3 Can beta be saved ?

- identical results on different subperiods

19

2.3 BE/ME, A/ME, A/BE and E/P

2.3.1 Informal tests

Table IV, p.442

- BE/ME: strong > 0 relation between average return and BE/ME, no relation with ß

- E/P: U-shape relation

2.3.2 Fama-MacBeth regressions

- BE/ME: strong > 0 (0.50% per month) and significant relation (stronger than the

size effect)

20

- Leverage:

. LN(A/ME) and LN(A/BE) have opposite signs

. LN(A/ME) : market leverage, > 0 relation (0.50%)

. LN(A/BE) : book leverage, < 0 relation (-0.57%)

for αLN(BE/ME) = αLN(A/ME) - αLN(A/BE)

. A/BE = (BE+D)/BE = 1+D/BE and A/ME = (BE+D)/ME = BE/ME + D/ME

Caveat : distress factor or market leverage ?

- E/P: right if current earnings proxy for earnings forecasts, (4.52%)

- Nothing if current earnings are < 0 ⇒ Dummy (E-/P)

. E/P: 4.52% per month

. Dummy (E-/P) : 0.57% per month

. Dummy (E-/P) does not resist to the introduction of size

. E/P does not resist to the introduction of size and BE/ME

21

2.4 A parsimonious model for average returns

Conclusion (FF, JF, 1996): rejection of the CAPM based on evidence that size andbook-to-market-equity capture cross-sectional variation in average returns that ismissed by univariate market betas.

- There exists a negative correlation between ME and BE/ME: -0.26 ⇒ IntroducingME or BE/ME in the same regression changes the coefficient, but they remainsignificant.

Table V, p.446:

- orthogonalization of ME and BE/ME: 2 dependent sorts (on size first and onBE/ME: 100 portfolios) : still a positive relation between returns and BE/ME withina size decile.

- On average 0.99% per month (1.63% - 0.64%) between the lowest and highestBE/ME portfolios within a size decile

- On average 0.58% per month (1.47% - 0.89%) between the smallest and biggestportfolios within a BE/ME decile

22

2.5 Conclusions

2.5.1 Econometric caveats

. Heteroscedasticity : see ME (besides, $ vs % !)

. Multicollinearity : ME is everywhere !

. Misspecification : omitted variables (January, overreaction effect, continuation effect,

forecasts revision effect, etc..)

. Functional form : Ln ?

23

. Robustness of results

Instability of risk premia

Impact of extreme data

Knez P.J. and M.J. Ready, «On the Robustness of Size and Book-to-Market inCross-Sectional Regressions», The Journal of Finance, Vol.LII, No,4,September 1997, pp 1355-1382.

The risk premium on size that was estimated by FF (1992) completelydisappears when the 1% most extreme observations are trimmed eachmonth.

The negative average of the monthly size coefficients reported by FF(1992) can be entirely explained by the 16 months with the most extremecoefficients.

24

Kothari S. P. , J. Shanken and R. G. Sloan, «Another look at the cross-section of expectedstock returns», The Journal of Finance, Vol.L, 1995, pp 185-224.

. Sample selection bias = Survivor bias ?

The relation between average return and book-to-market equity is seriouslyexaggerated by survivor bias

Data sources overstates the proportion of high-B/M.

However, Not sufficient to explain the importance of the premium

Chan L. C., N. Jegadeesh and J. Lakonishok, «Evaluating the performance of valueversus glamour stocks : the impact of selection bias», Journal of FinancialEconomics, Vol.38, 1995, pp 269-296.

25

. Betas: non standard procedure

Betas from annual returns produce a stronger positive relation between beta andaverage return than monthly returns

Not true for NYSE stocks, only true if AMEX stocks are included (size effect)

Smaller spread in ßs computed from annual returns than from monthly returns ⇒larger slopes for annual ßs

However, near-perfect correlation between ßs ⇒ same t-statistics

26

. Market proxies : Roll’s critics

Kandel S. and R. F. Stambaugh, «Portfolio inefficiency and the cross-section ofexpected returns», Journal of Finance, Vol.50, 1995, pp 157-184.

The true market is mean-variance-efficient, but the proxies used in empirical testsare not. The bad-market-proxy argument does not justify the way the CAPM isapplied to estimate the cost of equity or to evaluate portfolio managers

. Data snooping, data mining : Lo and MacKinlay, 1988 and Black, 1993 1995

Sample-specific effects for in-sample studies

. Equally-weighted or weighted monthly premia ? No results on intercepts and R2

. One-step estimation procedure : pooling ⇒ Conditional models

27

2.5.2 Economic caveats

Are these size and book-to-market effects generated by a multifactor ICAPM or APT ?

Economic explanation for the roles of size and book-to-market equity ?

One necessary condition : multiple common (undiversifiable) sources of variance inreturns.

Evidence of covariation in returns related to size and B/M (captured by loadings onSMB and HML), above and beyond the covariation explained by the market return.

Fama, E.F. et K.R. French, «Common risk factors in the returns on stocks andbonds», Journal of Financial Economics, Vol.33, 1993, pp 3-56.

Evidence of common factors in fundamentals like earnings and sales.

Fama, E.F. et K.R. French, «Size and book-to-market factors in earnings andreturns», The Journal of Finance, Vol.50, 1995, pp 131-155.

28

Chan and Chen, 1991 : evidence that there is covariation in returns related torelative distress that is not explained by the market return and is compensated inaverage returns.

Book-to-market equity : proxy for relative distress

Huberman and Kandel , 1987 : evidence that there is covariation in returns on smallstocks that is not captured by the market return and is compensated in averagereturns.

29

3 FROM THE CAPM TO A THREE-FACTOR MODEL

Fama E. F. and K. R. French, “Multifactor explanations of asset pricing anomalies”, TheJournal of Finance, Vol.LI, No.1, March 1996, 55-83

Except for the continuation of short-term past returns (STPR), the anomalies largelydisappear in a three-factor pricing model (TFPM)

TFPM : The excess return on a portfolio in excess of the risk-free rate (Ri)-Rf isexplained by the sensitivity of it return to 3 factors :

1) the return on a broad market portfolio (E(Rm)-Rf)

2) the difference between the return on a portfolio of small stocks and the return on aportfolio of large stocks (E(SMB), small minus big)

3) the difference between the return on a portfolio of high-book-to-market stocksand the return on a portfolio of low-book-to-market stocks (E(HML), high minuslow)

30

E(Ri) - Rf = bi*(E(Rm)-Rf) + si*E(SMB) + hi*E(HML) (1)

Where the factor sensitivities or loadings, bi, si and hi are the slopes in the time-series regression

Ri-Rf = ai + bi(Rm-Rf) + si*SMB + hi*HML + ei (2)

Strong firms : low BE/ME, low E/P, low C/P and high sales growth, no FCFnegative slopes on HML (the average HML: 6.33%/year)lower expected returns

Weak firms : high BE/ME, high E/P, high C/P and low sales growth, FCFpositive slopes on HML (relatively distressed)higher expected returns

Strong firms = growth (glamour) stocks versus weak firms = value stocks

31

3.1 Tests on the 25 FF Size-BE/ME portfolios

3.1.1 Computation of SMB and HML

At the end of June of each year t (1963-1993) stocks are allocated to :

2 groups based on their market equity (small, S, or big, B, see median)

3 groups based on their B/M (low, medium, high; L (30%), M (40%), H (30%))

Computation of value-weight monthly returns

SMB = S - B : 4.92%/yearHML = H - L (M not included) : 6.33%/year

32

Descriptive statistics

Table I, A, p.58

2 independent sorts:

Sort on size : 5 portfoliosSort on B/M : 5 portfolios

Except for low book-to-market, small stocks tend to have higher returns than bigstocks (see columns)

High book-to-market stocks tend to have higher returns than low-book-to-marketstocks (see rows)

ME-S and BM-H : 1.08% monthly excess returns (small losers, small valuestocks)

versusME-B and BM-L : 0.37% monthly excess returns (big winners, big growthstocks)

33

3.1.2 Regressions (2) for the 25 portfolios

Table I, B, p. 59The model captures most of the variation in the average returns on the portfolios: theaverage absolute intercept of 0.093% is small; the average R2 is important, 93% (p.71,Table IX, second row)

Exceptions :

Negative intercepts for the portfolio of stocks in the smallest size and lowest B/Mquintiles (S and L). Consistent with Table I, Panel A

Positive intercepts for the portfolio of stocks in the biggest size and lowest B/Mquintiles (B and L). Non consistent with Table I, Panel A !

ai : should be equal to 0 (Analogy with the Jensen's alpha in the CAPM:)]([ fmPfpP RRRR −+−= βα , see Table VIII)

bi : close to 1si : positive except for big stockshi : positive except for low B/M

34

3.2 LSV deciles

Formation of deciles from sorts on B/M, E/P, C/P and five-year sales rank as in LSV

Strong positive relations between average excess returns and B/M, E/P, C/PNegative relation between average excess returns and five-year sales rank

The TFPM does very well on B/M, E/P and C/P portfolios

Higher-B/M, E/P and C/P portfolios produce larger slopes on SMB andespecially HML.

Dividing an accounting variable by P produces a characterization of stocksthat is related to their loadings on HML

As loadings on HML proxy for relative distress,Low B/M, E/P and C/P are typical of strong (growth) stocksHigh B/M, E/P and C/P are typical of relatively distressed (value) stocks

35

The TFPM does not very well on sales-rank portfolios

3.3 LSV Double-Sort Portfolios

LSV : Sorting stocks on two accounting variables more accurately distinguishesstrong and distressed stocks and produces larger spreads in average returns

For B/M, E/P and C/P are correlated, sort on sales-rank and on B/M, E/P or C/P

9 portfolios (30%, 40%, 30%)

Lowest excess returns on low B/M, E/P or C/P with high sales growth, id estgrowth stocks

Highest excess returns on high B/M, E/P or C/P with low sales growth, id estvalue stocks

The TFPM does well for those double-sort portfolios

36

3.4 Portfolios formed on past returns

Table VI, p.66

Overreaction, reversal behavior, long-term past losers (winners) tend to be future winners(losers) : DeBondt and Thaler, 1985, 1987; Kryzanowski and Zhang, 1992.

The overreaction effect is not observed for the 60-1 period Exception: losers over the 60-13 period (average monthly excess return: 1.16%),winners (average monthly excess return: 0.42%)However, the TFPM explains well the patterns in the future returns (Table VII)LT past losers (winners) behave like small distressed stocks (strong stocks)

Continuation behavior : Jegadeesh and Titman, 1993, Asness, 1994

Short-term past losers (winners) tend to be future losers (winners)Losers over the 12-2 period (-0.00%), winners (1.31%) (see spread)The TFPM does not explain the patterns in the future returns on portfolios (TableVII, p(GRS)=0): intercepts are strongly negative (positive) for ST losers (STwinners)

37

3.5 Exploring Three-Factor Models

3.5.1 Spanning tests (pp. 68-72, not to study !)

The so-called anomalies are captured by the three-factor model.

But many other combinations of the three portfolios describe returns as well as Rm-Rf,SMB and HML

In a two-state-variable Intertemporal CAPM : Multifactor-Minimum-Variance (MMV)portfolios are spanned by the risk-free security and any three linearly independent MMVportfolios (a third portfolio is needed to capture the tradeoff of expected return for returnvariance that is unrelated to the state variables)

In the usual representation of the three-factor ICAPM, the 3 explanatory portfolios are themarket and MMV portfolios that mimic the two state variables of special hedgingconcern to investors.

38

Ross’ APT : as in the ICAPM, the risk-free security and three MMV portfolios areneeded to span MMV portfolios and describe returns.

In principle, the explanatory variables in the ICAPM (or the APT) are the expectedreturns on MMV portfolios in excess of the risk-free rate.

SMB and HML : differences between 2 portfolio returns

Equation (1) is still a legitimate three-factor risk-return relation as long as the twocomponents of SMB and HML are MMV.

RB-Rf and RL-Rf are then exact linear combinations of Rm-Rf, RS-Rf and RH-Rf, sosubtracting RB from RS and RL from RH has no effect on the intercepts or theexplanatory power of the three-factor regressions.

39

If Rm-Rf, SMB and HML do a good job in describing average returns, then M, S, B, H,and L are close to MMV. Then all combinations of 3 of the portfolios M, S, B, H and Lshould provide similar descriptions of average returns.

Regressions that use 4 different triplets of Rm-Rf, RS-Rf, RH-Rf and RL-Rf (RB isexcluded for correlation between RM and RB is 99%) to explain the excess return inexcess of the excluded MMV proxy or the deciles portfolios formed on E/P, C/P, salesrank, LT past returns give good results.

Intercepts and R2 are almost the same

All regressions missed the continuation behavior of returns

The model described by equation (1) has one advantage : Rm-Rf, SMB and HML areless correlated with one another and slopes are easier to interpret.

For a more precise discussion

Fama E., «Determining the number of priced state variables in the ICAPM»,Journal of Financial and Quantitative Analysis, Vol.33, No.2, 1998, pp 217-296.

40

3.5.2 Additional proxies

E/P, C/P LSV proxies work like H or L in describing returns (accounting variablesscaled by price). High correlation with BE/ME

LSV sales rank or LT past returns proxies do not work like H or L in describingreturns. Low correlation with BE/ME

3.5.3 CAPM versus TFPM (To study !) (Table IX, p. 71)

CAPM:The average absolute pricing errors (intercepts) are large (25 to 30 basis pointsper month)Betas show little relation to variables like B/M, E/P, C/P or sales rank

TFPM :The average absolute pricing errors (intercepts) are small (5 to 10 basis pointsper month)

The TFPM dominates the CAPM except for portfolios formed on ST past returns.

41

3.6 Interpreting the results (see introduction)

The TFPM captures the reversal behavior of long-term returns

Stocks with low long-term past returns (losers) tend to have positive SMB and HMLslopes (they are smaller and distressed) and higher future average returns

Stocks with high long-term past returns (winners) tend to have negative SMB andHML slopes (they are strong stocks) and lower future average returns

The TFPM does not capture the continuation behavior of short-term returns

Stocks with low short-term past returns (losers) tend to have positive SMB andHML slopes (they are smaller and distressed) but lower future average returns

Stocks with high long-term past returns (winners) tend to have negative SMB andHML slopes (they are strong stocks) but higher future average returns

The TFPM captures much of the variation in cross-section of average stock returns andabsorbs most of the anomalies that have plagued the CAPM.

42

3.7 Conclusion

1) At a minimum, the evidence suggests that the TFPM, with intercepts equal to 0, isa parsimonious description of returns.

2) More aggressively, the TFPM is an equilibrium pricing model : a three-factor ofMerton’s (1973) ICAPM or Ross’s (1976) APT

SMB and HML : mimic combinations of two underlying risk factors or statevariables of special hedging concern to investors.

Asset pricing model is rational and conforms to a three factor ICAPM or APT that doesnot reduce to the CAPM

Is the average HML return a premium for a state-variable risk related to relative distress ?

Evidence that low B/M is typical of firms that have persistently strong earnings(strong firms) and that high B/M is typical of firms that have persistently lowearnings (weak firms)

43

HML loadings reflect periods of industry strength or distress

Why should relative distress be a state variable of special hedging concern to investors ?

Human capital (Mayers, 1972; JW, 1996) ?

Different impact of a negative shock if you have specialized human capitaland you are tied to a growth firm or a distressed firm

In a growth firm, does not reduce the value of specialized human capital

In a distressed firm, reduces the value of specialized human capital, so theseinvestors avoid holding their firms’ stocks. If returns in distressed firms arepositively correlated, workers in distressed firms have an incentive to avoid thestocks of all distressed firms.

44

Parallèle avec EBO et VÉA ⇒ création de valeur

Hypothèses :PerpétuitéE = bénéficeBN = ke*BE (bénéfice normal de l'entreprise)

EBO (Edwards, Bell et Ohlson)

VM = E/ke = (ke*BE+E- ke*BE )/ke = BE +(E- ke*BE )/ke = BE +(E - BN)/ke

Et VM = BE + bénéfice anormal/ke

VÉA : Valeur Économique Ajoutée + VMA : Valeur Marchande Ajoutée

VM = E/ke = ROE*BE/ke = (ke*BE+ROE*BE- ke*BE )/ke = BE+(ROE- ke)*BE/ke

Et, VM = BE + VÉA/ke = BE + VMA

45

Skepticism and questions about the HML premium (distress premium):

a) Real but irrational : overreaction that leads to underpricing of distressed stocks andoverpricing of growth stocks , Lakonishok, Shleifer and Vishny, 1994; Haugen1995; MacKinlay, 1995

The premium for relative distress is too large to be explained by rational pricingClose to an arbitrage opportunity ⇒ small standard deviation

In fact, the standard deviation is 13.11% per year and is not a sure thing

Table XI, FF, JF, 1996

1964-1993 Rm-Rf SMB HMLMean 5.94 4.92 6.33Std Dev 16.33 15.44 13.11T=Mean/(Std Dev)/29^0.5 1.96 1.72 2.60Negative 10/30 9/30 10/30

46

b) Survivor bias, Kothari, Shanken and Sloan, 1995Data sources overstates the proportion of high-B/M.

Chan L. C., N. Jegadeesh and J. Lakonishok, «Evaluating the performance of valueversus glamour stocks : the impact of selection bias», Journal of FinancialEconomics, Vol.38, 1995, pp 269-296.

Not sufficient to explain the importance of the premium.

c) Data snooping, data mining : Lo and MacKinlay, 1988; Black, 1993 andMacKinlay, 1995

Sample-specific effects for in-sample studies

d) Bad market proxies : Roll’s critics

Other tests are necessary, see FF, “Value versus growth: the international evidence”,Working paper, 1997.

47

What about the continuation effect ?

a) data mining or data snooping

b) irrationality : behavioral finance

c) rationality, but the TFPM does not capture this effect

48

ETAPE I ETAPE II ETAPE III1/56 12/60 1) Estimation

des i

∧

β

2) Tri 3) Formationde 20portefeuillesP

1/61 12/65 Estimations

des P

∧

β et

RP

1/66 Test de la relation

121,121,121,1121,0121,121,ˆ

ppfp eRR ++=− βγγ

1 60 61 120 121A

A

∧

βB

B

∧

β

Z

Z

∧

βAA

AA

∧

βBB

BB

∧

β

ZZ

ZZ

∧

β

360 itérations de ces trois étapes2 estimations possibles ensuite:1) Estimation en 2 étapes: 360 régressions en CT, puis moyennes des coefficients et tests de moyenne [moyenne/(sd/360^(0,5))]2) Estimation en 1 étape: 1 régression en données de panel sur 360*20 observations et tests directs