1. Value versus growth: Some
statistical evidence
Bruce Greenwald & Tano Santos
Columbia Business School
Columbia University

2. Introduction
• We focus in this course on the fundamental value
of the investment at the expense of any other
consideration.
• In particular: Value investing is about levels and
the comparison between value and the market
price.
• Specifically, we can classify the approaches to
investing along the following dimensions.

3. Approaches to investing
3
Approaches to investment
Efficient Markets
•Diversification
•Asset Allocation
•Cost minimization

4. Approaches to investing
4
Approaches to investment
Efficient MarketsShort term
•Diversification
•Asset Allocation
•Cost minimization
Fundamental
Value
Changes
•Current Price vs. forecast change
•Micro
•Macro
Technical
•Value strategies
•Momentum
•Price/Volume patterns

5. Approaches to investing
5
Approaches to investment
Efficient MarketsShort termLong term
Fundamental
Value
Levels
•Diversification
•Asset Allocation
•Cost minimization
•Mkt. price
vs. value
Fundamental
Value
Changes
•Current Price vs. forecast change
•Micro
•Macro
Technical
•Value strategies
•Momentum
•Price/Volume patterns

6. Introduction
• The essential value investing principles are:
1. Identification of the firms whose value is reliably calculable
by you (circle of competence.)
2. Among these firms, invest in those whose market price
(equity plus debt) is below your calculated value by an
appropriate margin of safety (1/3 to 1/2).
• Thus, value investing emphasizes specialization and it
focuses on specific names and emphasizes prudence
to guarantee the protection of principal.
• Understanding the principles above is the purpose of
this course.
6

7. Introduction
• Today though we ask the following question:
- Are there superior returns associated with investing in
companies which are “cheap” relative to some measure of
fundamentals?
• For instance, if we classify firms according to their
book-to-market, do firms which have high book-to-
market (BE/ME) yield on average higher returns than
do firms with low BE/ME?
• To preview the answer:
- Value (high BE/ME) stocks command higher average
returns though they are not riskier.
7

8. Outline
• In what follows we divide the presentation in two
parts:
1. The cross section of stock returns:
a) Value strategies
b) Momentum
c) Long term reversal
d) Industry
2. The market portfolio
• Throughout it is important to remember that none
of this is what we refer to as value investing, which is
the application of simple principles to the analysis of
individual names.
8

9. Outline
• In what follows we divide the presentation in two
parts:
1. The cross section of stock returns:
a) Value strategies
b) Momentum
c) Long term reversal
d) Industry
2. The market portfolio
• Throughout it is important to remember that none
of this is what we refer to as value investing, which is
the application of simple principles to the analysis of
individual names.
9
Not today

10. THE CROSS SECTION
10

11. The value premium
• To assess whether value stocks, stocks with high
book-to-market, systematically yield high returns than
growth stocks we proceed as follows:
- We take all publicly traded stocks and sort them according
to BE/ME into ten portfolios
- Then we construct a “value strategy” by going long the
portfolio of stocks with high book-to-market which we fund
by shorting a portfolio of stocks with low book-to-market.
- The resulting portfolio is called the “High-Minus-Low”
portfolio, or HML.
- We then compute the monthly returns of this HML
portfolio since 1927.
11

12. Value sorts
12
Stock 1
Stock 2
Stock 3
Stock 4
Stock 5
Stock 6
Stock 7
Stock 8
Stock 9
Stock 10
June of year t

13. Value sorts
13
Stock 1
Stock 2
Stock 3
Stock 4
Stock 5
Stock 6
Stock 7
Stock 8
Stock 9
Stock 10
June of year t
Low book-to-market
High book-to-market

14. Value sorts
14
Stock 1
Stock 2
Stock 3
Stock 4
Stock 5
Stock 6
Stock 7
Stock 8
Stock 9
Stock 10
Stock 1
Stock 2
Stock 3
Stock 4
Stock 5
Stock 6
Stock 7
Stock 8
Stock 9
Stock 10
Growth portfolio
Value portfolio
June of year t

15. Value sorts
15
Stock 1
Stock 2
Stock 3
Stock 4
Stock 5
Stock 6
Stock 7
Stock 8
Stock 9
Stock 10
Stock 1
Stock 2
Stock 3
Stock 4
Stock 5
Stock 6
Stock 7
Stock 8
Stock 9
Stock 10
Growth portfolio
Value portfolio
Stock 3
Stock 2
Stock 7
Stock 9
Stock 8
Stock 1
Stock 10
Stock 4
Stock 6
Stock 5
Stock 3
Stock 2
Stock 7
Stock 9
Stock 8
Stock 1
Stock 10
Stock 4
Stock 6
Stock 5
Growth portfolio
Value portfolio
June of year t June of year t+1

16. Value versus growth: 1927-2008
16
-60
-40
-20
0
20
40
60
1927
1929
1931
1933
1935
1937
1939
1941
1943
1945
1947
1949
1951
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Mkt-RF
Annual market returns in excess of the one year treasury bill. 1927-2008. Source: Ken French database

17. Value versus growth: 1927-2008
17
-60
-40
-20
0
20
40
60
1927
1929
1931
1933
1935
1937
1939
1941
1943
1945
1947
1949
1951
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Mkt-RF
Great Depression – I (1929)
Great Depression – II (1937) Oil shock – I (1973)
Oil shock – II (1981)
End of tech. bubble (2000)
Great Recession - (2008)
Annual market returns in excess of the one year treasury bill. 1927-2008. Source: Ken French database

18. Value versus growth: 1927-2008
18
-60
-40
-20
0
20
40
60
1927
1929
1931
1933
1935
1937
1939
1941
1943
1945
1947
1949
1951
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Mkt-RF HML
Great Depression – I (1929)
Great Depression – II (1937) Oil shock – I (1973)
Oil shock – II (1981)
End of tech. bubble (2000)
Great Recession - (2008)
Annual market returns in excess of the one year treasury bill rate and the annual returns on a zero investment portfolio that is
long value stocks and short growth. 1927-2008. Source: Ken French database

19. Value versus growth: 1927-2008
Growth Port. 2 Port. 3 Port. 4 Port. 5 Port. 6 Port. 7 Port. 8 Port. 9 Value
Avge.
BE/ME .20 .37 .49 .61 .72 .84 .97 1.14 1.40 2.21
Avge.
excess
return
(ann. %)
3.84 5.45 5.71 5.67 5.52 6.60 7.35 8.17 9.36 10.96
Average book-to-market, and excess returns for ten book-to-market sorted portfolios of all stocks in
Compustat; annualized; value weighted; 1963-01 to 2009-09; the portfolios are resorted at the end of June
and BE/ME is book equity at the last fiscal year end of the prior calendar year divided by ME at the end of
December of the prior year.
• So what are the returns of those ten sorted portfolios?

20. Value versus growth: 1927-2008
• So in conclusion the value portfolio, the portfolio of
stocks with high book to market, earns, on average a
bit over 7% excess return over the growth portfolio.
• The evidence so far has exclusively focused on the
US.
• But, what about the international evidence?
• Unfortunately, the available data does not extend
back as far as it does in the US, but the overall picture
is identical:
20

21. Value vs. growth: international evidence
Market Value Growth
Japan 1.00 1.53 .64
UK 1.21 1.61 1.16
Germany 1.47 1.63 1.36
France 1.34 1.71 1.23
Spain 1.13 1.17 .99
Australia 1.33 1.65 1.15
Canada 1.13 1.15 .98
21
Monthly returns 1975-01 to 2007-12 (except for Canada where the sample starts in 1977-01) in percentages
Source: Ken French

22. The value premium: Is it risk?
• To reiterate: Value, the strategy of buying high BE/ME
stocks, yields superior returns to one that focuses on
growth or glamour stocks.
• Why? Two possible answers:
1. Risk - The higher average return of value portfolios simply
reflects a compensation for risk.
2. Mispricing - The higher average return of value portfolios
reflect a systematic undervaluation of value stocks relative
to growth
• Let’s try to shed some light on this issue.
22

23. The value premium: Is it risk?
• If the CAPM is a proper representation of risk then it
must be the case that value stocks have higher betas
than growth stocks.
• Do they? Does the value portfolio have a higher beta
than the growth portfolio? To answer this:
- Estimate βs by running a time series regression of the
excess returns of each of the ten portfolios on the market
excess return (the market model).
- Calculate the fitted CAPM average excess return for each
of the ten portfolios and compare them to the actual
average excess return in sample.
23

24. The value premium: Is it risk?
3
4
5
6
7
8
9
10
11
3 4 5 6 7 8 9 10 11
CAPMfittedexcessreturns
Average excess returns
Average excess returns of ten book-to-market sorted portfolios against CAPM fitted excess
returns. 1963-1 – 2009-09. Source: Ken French database.

25. The value premium: Is it risk?
3
4
5
6
7
8
9
10
11
3 4 5 6 7 8 9 10 11
CAPMfittedexcessreturns
Average excess returns
This is where the dots should be
if the CAPM betas captured risk
Average excess returns of ten book-to-market sorted portfolios against CAPM fitted excess
returns. 1963-1 – 2009-09. Source: Ken French database.

26. Is it risk? The CAPM and the value premium
3
4
5
6
7
8
9
10
11
3 4 5 6 7 8 9 10 11
CAPMfittedexcessreturns
Average excess returns
Average excess returns of ten book-to-market sorted portfolios against CAPM fitted excess
returns. 1963-1 – 2009-09. Source: Ken French database.
Extreme Growth Extreme Value

27. The value premium: Is it risk?
3
4
5
6
7
8
9
10
11
3 4 5 6 7 8 9 10 11
CAPMfittedexcessreturns
Average excess returns
Average excess returns of ten book-to-market sorted portfolios against CAPM fitted excess
returns. 1963-1 – 2009-09. Source: Ken French database.
Extreme growth yields less than
what is predicted by the CAPM
Extreme value yields more than
what is predicted by the CAPM

28. The value premium: Is it risk?
Growth Port. 2 Port. 3 Port. 4 Port. 5 Port. 6 Port. 7 Port. 8 Port. 9 Value
Avge.
BE/ME .20 .37 .49 .61 .72 .84 .97 1.14 1.40 2.21
Avge.
excess
return
(ann. %)
3.84 5.45 5.71 5.67 5.52 6.60 7.35 8.17 9.36 10.96
CAPM βs
1.07 1.01 .98 .99 .91 .92 .86 .89 .92 1.05
CAPM
fitted excess
returns
5.49 5.17 5.02 5.07 4.66 4.71 4.40 4.56 4.71 5.38
Average book-to-market, excess returns, CAPM betas and CAPM fitted excess returns for ten book-to-market sorted portfolios
of all stocks in Compustat; annualized; value weighted; 1963-01 to 2009-09; the portfolios are resorted at the end of June and
BE/ME is book equity at the last fiscal year end of the prior calendar year divided by ME at the end of December of the prior
year.

29. The value premium: Is it risk?
• The conclusion is striking: Value stocks have 7%
(annual) higher average returns than growth stocks
but the betas cannot explain any of it:
- Technically: Whereas average excess returns are an
increasing function of BE/ME betas have a flat relation with
returns.
- That is, the CAPM cannot explain the superior returns of
value strategies.
• But the CAPM may not be the right description of risk
- Are value stocks risky in that they do specially bad in bad
times relative to growth and thus the higher premium?
29

30. Value versus growth: 1927-2008
30
-60
-40
-20
0
20
40
60
1927
1929
1931
1933
1935
1937
1939
1941
1943
1945
1947
1949
1951
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Mkt-RF HML
Great Depression – I (1929)
Great Depression – II (1937) Oil shock – I (1973)
Oil shock – II (1981)
End of tech. bubble (2000)
Great Recession - (2008)
Annual market returns in excess of the one year treasury bill rate and the annual returns on a zero investment portfolio that is
long value stocks and short growth. 1927-2008. Source: Ken French database

31. The value premium: Is it risk?
-40
-30
-20
-10
0
10
20
30
40
192607
192610
192701
192704
192707
192710
192801
192804
192807
192810
192901
192904
192907
192910
193001
193004
193007
193010
193101
193104
193107
193110
193201
193204
193207
193210
193301
193304
193307
193310
193401
193404
193407
193410
193501
193504
193507
193510
193601
193604
193607
193610
193701
193704
193707
193710
193801
193804
193807
193810
193901
193904
193907
193910
Mkt-RF
Monthly market returns in excess of the one month treasury bill – 1926-07 – 1939-12.
Source: Ken French database.

32. The value premium: Is it risk?
-40
-30
-20
-10
0
10
20
30
40
192607
192610
192701
192704
192707
192710
192801
192804
192807
192810
192901
192904
192907
192910
193001
193004
193007
193010
193101
193104
193107
193110
193201
193204
193207
193210
193301
193304
193307
193310
193401
193404
193407
193410
193501
193504
193507
193510
193601
193604
193607
193610
193701
193704
193707
193710
193801
193804
193807
193810
193901
193904
193907
193910
Mkt-RF
Monthly market returns in excess of the one month treasury bill – 1926-07 – 1939-12.
Source: Ken French database.
1929-10 1931-09
1932-05
1938-03

33. The value premium: Is it risk?
-40
-30
-20
-10
0
10
20
30
40
192607
192610
192701
192704
192707
192710
192801
192804
192807
192810
192901
192904
192907
192910
193001
193004
193007
193010
193101
193104
193107
193110
193201
193204
193207
193210
193301
193304
193307
193310
193401
193404
193407
193410
193501
193504
193507
193510
193601
193604
193607
193610
193701
193704
193707
193710
193801
193804
193807
193810
193901
193904
193907
193910
Mkt-RF Value-Growth
Monthly market returns in excess of the one month treasury bill and the monthly returns
on a zero investment portfolio that is long value stocks and short growth. 1926-07 – 1939-
12. Source: Ken French database
1929-10 1931-09
1932-05
1938-03

34. The value premium: Is it risk?
-20
-15
-10
-5
0
5
10
15
200301
200303
200305
200307
200309
200311
200401
200403
200405
200407
200409
200411
200501
200503
200505
200507
200509
200511
200601
200603
200605
200607
200609
200611
200701
200703
200705
200707
200709
200711
200801
200803
200805
200807
200809
200811
200901
200903
200905
200907
200909
Mkt-RF
Monthly market returns in excess of the one month treasury bill – 2003-01 –2009-10.
Source: Ken French database.
2008-10
2008-09

35. The value premium: Is it risk?
-20
-15
-10
-5
0
5
10
15
200301
200303
200305
200307
200309
200311
200401
200403
200405
200407
200409
200411
200501
200503
200505
200507
200509
200511
200601
200603
200605
200607
200609
200611
200701
200703
200705
200707
200709
200711
200801
200803
200805
200807
200809
200811
200901
200903
200905
200907
200909
Mkt-RF Value-Growth
2008-10
2008-09
Monthly market returns in excess of the one month treasury bill and the monthly returns
on a zero investment portfolio that is long value stocks and short growth. 2003-01 –2009-10.
Source: Ken French database
2008-09

36. The value premium: Is it risk?
-30
-20
-10
0
10
20
30
40
50
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Mkt-RF
Annual market returns in excess of the one year treasury bill. 1993-2006. Source: Ken
French database

37. The value premium: Is it risk?
-30
-20
-10
0
10
20
30
40
50
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Mkt-RF Value-Growth
Annual market returns in excess of the one year treasury bill rate and the annual returns
on a zero investment portfolio that is long value stocks and short growth. 1993-2006.
Source: Ken French database

38. The value premium: Is it risk?
38
-30
-20
-10
0
10
20
30
40
50
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Growth Value
Annual market returns of a value weighted portfolio of the top (value) and bottom (growth) 20% of stocks sorted by BE/ME .
1993-2006. Source: Ken French database

39. The value premium: Is it risk?
• Thus it looks that value stocks do relatively better
than growth stocks in bad (market) times (though not
always!):
- The Great Depression first shock of 1929
- The oil shocks of 1970s and early 1980s
- The end of the technology bubble.
• And worse than growth during the tech bubble.
• If value stocks were riskier than growth they should
be doing worse in bad times, as measured by the
market, but they don’t seem to, at least in some
significant episodes.
39

40. The value premium: Is it risk?
• Before we turn to a potential behavioral explanation
it is worth going deeper into the issue of whether
value was always less risky than growth (according to
the CAPM!)
• For this we run the following exercise we estimate
betas using a trailing five year window with data
starting in 1926-07 all the way up to 2009-09.
• As before we use the market model and we estimate
betas by running time series regressions of excess
returns for the BE/ME portfolios on market excess
returns.
40

41. The value premium: Is it risk?
41
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
193007
193112
193305
193410
193603
193708
193901
194006
194111
194304
194409
194602
194707
194812
195005
195110
195303
195408
195601
195706
195811
196004
196109
196302
196407
196512
196705
196810
197003
197108
197301
197406
197511
197704
197809
198002
198107
198212
198405
198510
198703
198808
199001
199106
199211
199404
199509
199702
199807
199912
200105
200210
200403
200508
200701
200806
Growth
Monthly time series of market betas for the value and growth portfolios, defined as the top and bottom
decile,respectively,of the book-to-market sorted portfolios for the sample 1926-07 to 2009-09.The betas
are estimated using a rolling five year window. The monthly returns are from Ken French’s data base.

42. The value premium: Is it risk?
42
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
193007
193112
193305
193410
193603
193708
193901
194006
194111
194304
194409
194602
194707
194812
195005
195110
195303
195408
195601
195706
195811
196004
196109
196302
196407
196512
196705
196810
197003
197108
197301
197406
197511
197704
197809
198002
198107
198212
198405
198510
198703
198808
199001
199106
199211
199404
199509
199702
199807
199912
200105
200210
200403
200508
200701
200806
Growth Value
Monthly time series of market betas for the value and growth portfolios, defined as the top and bottom
decile,respectively,of the book-to-market sorted portfolios for the sample 1926-07 to 2009-09.The betas
are estimated using a rolling five year window. The monthly returns are from Ken French’s data base.

43. The value premium: A behavioral view
• As we just saw, a “risk story” faces challenges in
addressing the value premium.
• One of the biggest divide in finance academic circles:
- Rational: The value premium is a compensation for risk; we
just simply don’t observe the proper measure of risk (and
the CAPM βs do not describe risk).
- Behavioral: The value premium is a reflection of systematic
underpricing of value stocks by investors.
• We study next the behavioral story.
43

44. The value premium: A behavioral view
• Lakonishok, Shleifer and Vishny (JF, 1994) argue that
the value premium arises because:
- Value portfolios are comprised of stocks that have
“underperformed” recently according to some metrics such
as returns and earnings and
- that investors extrapolate this past performance into the
future and thus expect equally dismal results.
- As a result they stay away from these distressed stocks
which fall in price relative to fundamentals, such as a book,
and thus the higher returns when performance surprises on
the positive side.
44

45. The value premium: A behavioral view
• This logic is an example of the representative
heuristic (Tversky and Kahneman, 1974), the
tendency of individuals to identify the an uncertain
event or a sample by the degree to which is similar to
the parent population.
• To quote Benjamin Graham:
“[Strong past returns] created a natural satisfaction on Wall
Street with such fine achievements, and a quite illogical and
dangerous conviction that equally marvelous results could
be expected for common stocks in the future. Few people
seem to have been bothered by the thought that the very
extent of the rise might indicate that it had been overdone.”
The Intelligent Investor, Revised Ed 1984, pp. 67-69.45

46. The value premium: A behavioral view
• LSV offer some striking evidence of their thesis.
• Take the same extreme value and growth portfolios
that we constructed above and calculate some
measure of past and future performance for these
portfolios:
- Earnings
- Sales
- Cash flows
46

47. The value premium: A behavioral view
Growth Value
AEG(-5,0) .309 -.274
AEG(0,5) .050 .436
AEG(2,5) .070 .215
ACG(-5,0) .217 -.013
ACG(0,5) .127 .070
ACG(2,5) .086 .111
ASG(-5,0) .091 .030
ASG(0,5) .062 .020
ASG(2,5) .059 .023
47
AEG(i,j) is the geometric average growth rate of earnings for the portfolio from year i to year j.
ACG(i,j) and ASG(i,j) are defined analagously for cash-flow and sales respectively.
Source: J. Lakonishok,A. Shleifer and R.Vishny. Journal of Finance, Dec. 1994,TableV.

48. The value premium: A behavioral view
Growth Value
AEG(-5,0) .309 -.274
AEG(0,5) .050 .436
AEG(2,5) .070 .215
ACG(-5,0) .217 -.013
ACG(0,5) .127 .070
ACG(2,5) .086 .111
ASG(-5,0) .091 .030
ASG(0,5) .062 .020
ASG(2,5) .059 .023
48
AEG(i,j) is the geometric average growth rate of earnings for the portfolio from year i to year j.
ACG(i,j) and ASG(i,j) are defined analagously for cash-flow and sales respectively.
Source: J. Lakonishok,A. Shleifer and R.Vishny. Journal of Finance, Dec. 1994,TableV.

49. The value premium: A behavioral view
• Thus it seems that past earnings growth fostered
pessimism on these stocks which led to low prices
and realized low returns
• These stocks thus get classified as value.
• After that, these same stocks surprise on the upside
and thus the larger return after being classified as
value.
• This behavioral story fits with a particular
psychological bias which is that of extrapolation
49

50. Momentum
• Value strategies are perhaps the most famous of
sorts, but the strategy to uncover sources of average
excess returns are always the same:
- Sort stocks along your favorite characteristic.
- Form portfolios, say decile portfolios, and track their
returns over a particular period.
- Reform the portfolios at a frequency of your choice (in the
case of book-to-market sorted portfolios the standard rule
is to resort portfolios after one year.)
- Compute average excess returns across these portfolios
and test whether whatever cross sectional dispersion in
average returns can be explained by some measure of risk.
50

51. Momentum
• Momentum portfolios are those where the sort is
driven by past returns:
- Each month, say, we place stocks in portfolios according to
their performance in the last year.
- We then compute the returns of the so formed portfolios
for the following month.
• The idea behind momentum is easy enough to explain
- Stocks that did well in the recent past are going to keep
doing well, whereas those that did poorly are going to
underperform.
51

52. Momentum
Low 2 3 4 5 6 7 8 9 High
Avge.
Returns
(%)
monthly
0.33 0.70 0.71 0.85 0.85 0.92 1.00 1.12 1.19 1.52
52
Monthly returns, in percentages, of ten portfolios sorted on realized returns over the previous year.
Thus the returns in month t, corresponds to the portfolio formed at the end of month t-1, based on
realized returns from t-1 to t-13. 1927-01 to 2009-09. Source: Ken French data base
•Notice thus the portfolio of past winners outperforms
the portfolio of past losers by more than a percentage
point a month!

53. Momentum
• Momentum has deep implications for the value
investor.
- Value investors focus on “cheap and ugly” stocks.
- If these have become such over the last year, it means that
one can expect them to remain so and to keep
underperforming in the short run.
• Finally, notice that it is difficult to conceive of a risk
based story that can account for the dispersion in
average returns across momentum sorted portfolios
as the frequency is much higher than that at which
macroeconomic news occur.
53

54. Momentum
54
-60
-40
-20
0
20
40
60
80
1927
1929
1931
1933
1935
1937
1939
1941
1943
1945
1947
1949
1951
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Mom Mkt-Rf
Annual returns for the momentum factor and market excess return; 1927-2009; Source: Ken French data base. The construction of the Momentum Factor is due to Ken
French. It is based on “six value-weight portfolios formed on size and prior (2-12) returns to construct Mom. The portfolios, which are formed monthly, are the
intersections of 2 portfolios formed on size (market equity, ME) and 3 portfolios formed on prior (2-12) return. The monthly size breakpoint is the median NYSE
market equity. The monthly prior (2-12) return breakpoints are the 30th and 70th NYSE percentiles. Mom is the average return on the two high prior return portfolios
minus the average return on the two low prior return portfolios: Mom = 1/2 (Small High + Big High) - 1/2(Small Low + Big Low).

55. Momentum
• There are many possible explanations for momentum:
- Conservatism bias: Investors underreact to new
information, effectively underweighting new data, and this
gives rise to momentum profits.
- There is also evidence that, at least in the past, mutual fund
managers tended to buy past winners and sell past losers
creating the conditions for the momentum effect to arise.
- There are explanations also based on self-attribution bias.
For instance, negative news about stocks are attributed to
“bad luck” rather than poor skills and thus mangers don’t
liquidate their position delaying the full incorporations of
news in prices.
55

56. Long term reversals
• Momentum is the observation that stocks that have
done well in the recent past (last year) do well in the
near future (generally, 3 months to a year) whereas
stocks that have done poorly keep underperforming.
• Momentum can lead to overvaluation but if this is the
case we should observe some long term reversal. Is this
the case?
- The answer is yes: When we sort stocks based on the
performance between one and five years there is substantial
evidence that bad performers tend to perform better
whereas the opposite is true for winners.
56

57. Long term reversals
• To check this we now sort stocks on a monthly basis
based on their performance between one and five
year into ten portfolios. Then we compute their
monthly returns:
57
Low 2 3 4 5 6 7 8 9 High
Avge.
Monthly
returns
(%)
1.47 1.26 1.24 1.04 1.10 0.99 1.02 1.02 0.87 0.87
Average monthly returns, in percentages, of ten portfolios sorted at the end of month t-1 based on
returns between months t-13 and month t-60. 1931-01 to 2009-09. The stocks are all stocks
In NYSE, Nasdaq and AMEX. Source: Ken French data base

58. Long term reversals
58
-50
-30
-10
10
30
50
70
90
1931
1933
1935
1937
1939
1941
1943
1945
1947
1949
1951
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Long Term Reversal Factor Mkt-Rf
Annual returns of the long term reversal factor versus the market excess return over the one year treasury bill rate.
1931 to 2008. Source: Ken French data base, which should be consulted for the construction of the factor

59. THE MARKET
59

60. Some thoughts on the market
• Value investors are reluctant to give advice regarding
the timing of Mr. Market, being aware of its
intemperate, capricious and childish ways!
• Still, one should not make the mistake of ignoring the
vagaries of the market.
• It is important to try to make sense of its gyrations.
• Next we try to cover briefly where we are and some
general lessons for the value investor regarding the
market outlook.
60

61. Some thoughts on the market
61
0
200
400
600
800
1000
1200
1400
1600
1800
2000
1870 1890 1910 1930 1950 1970 1990 2010
RealS&P500StockPriceIndex
Year
Price
Real S&P Stock Price Index and Composite Earnings. Monthly 1871-01 to 2010-01 (Jan. 13)
Source: Robert Shiller

62. Some thoughts on the market
62
0
50
100
150
200
250
300
350
400
450
0
200
400
600
800
1000
1200
1400
1600
1800
2000
1870 1890 1910 1930 1950 1970 1990 2010
RealS&PCompositeEarnings
RealS&P500StockPriceIndex
Year
Price
Earnings
Real S&P Stock Price Index and Composite Earnings. Monthly 1871-01 to 2010-01 (Jan. 13)
Source: Robert Shiller

63. Some thoughts on the market
63
0
5
10
15
20
25
30
35
40
45
1881.01
1883.05
1885.09
1888.01
1890.05
1892.09
1895.01
1897.05
1899.09
1902.01
1904.05
1906.09
1909.01
1911.05
1913.09
1916.01
1918.05
1920.09
1923.01
1925.05
1927.09
1930.01
1932.05
1934.09
1937.01
1939.05
1941.09
1944.01
1946.05
1948.09
1951.01
1953.05
1955.09
1958.01
1960.05
1962.09
1965.01
1967.05
1969.09
1972.01
1974.05
1976.09
1979.01
1981.05
1983.09
1986.01
1988.05
1990.09
1993.01
1995.05
1997.09
2000.01
2002.05
2004.09
2007.01
2009.05
Price to (10 year smoothed) earnings ratio of S&P Index – 1881-01 to 2010-01. Source: Robert Shiller

64. Some thoughts on the market
• More recently, the market has rallied dramatically
since its low in March 2009.
• This has been very painful for many investors (value
or not) who have remained skeptical about the
sources and sustainability of the recovery.
- In particular, the recovery seems to be fueled by a world
wide expansion of the monetary base.
• Before we turn to this rally let’s consider one
previous rally in times that were economically
challenging as well.
64

65. Some thoughts on the market
65
• S&P composite 12/28-04/30
• One dollar invested in the
index at the peak, in Sept.
1929, becomes only 71cents
in November 1929
• One dollar (re)invested at
the (local) bottom of
November 1929 becomes
$1.52 in April 1930.
• The plot is the price and one
has to account for dividends,
which are also reinvested.
• What happened afterwards?
20.00
22.00
24.00
26.00
28.00
30.00
32.00
1928.12
1929.01
1929.02
1929.03
1929.04
1929.05
1929.06
1929.07
1929.08
1929.09
1929.1
1929.11
1929.12
1930.01
1930.02
1930.03
1930.04
S&P composite price. Monthly. 12/28-04/30. Source: Robert Shiller
-30%
+50%

66. Some thoughts on the market
66
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
1929.01
1929.07
1930.01
1930.07
1931.01
1931.07
1932.01
1932.07
1933.01
1933.07
1934.01
1934.07
1935.01
1935.07
1936.01
1936.07
1937.01
1937.07
1938.01
1938.07
1939.01
1939.07
1940.01
1940.07
1941.01
1941.07
1942.01
1942.07
1943.01
1943.07
1944.01
1944.07
1945.01
1945.07
1946.01
1946.07
1947.01
1947.07
1948.01
1948.07
1949.01
1949.07
1950.01
1950.07
1951.01
1951.07
1952.01
1952.07
1953.01
1953.07
1954.01
1954.07
1955.01
1955.07
S&P composite price. Monthly. 1/29-12/55. Source: Robert Shiller

67. Some thoughts on the market
67
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
1929.01
1929.07
1930.01
1930.07
1931.01
1931.07
1932.01
1932.07
1933.01
1933.07
1934.01
1934.07
1935.01
1935.07
1936.01
1936.07
1937.01
1937.07
1938.01
1938.07
1939.01
1939.07
1940.01
1940.07
1941.01
1941.07
1942.01
1942.07
1943.01
1943.07
1944.01
1944.07
1945.01
1945.07
1946.01
1946.07
1947.01
1947.07
1948.01
1948.07
1949.01
1949.07
1950.01
1950.07
1951.01
1951.07
1952.01
1952.07
1953.01
1953.07
1954.01
1954.07
1955.01
1955.07
S&P composite price. Monthly. 1/29-12/55. Source: Robert Shiller
This is the episode in the previous plot

68. Some thoughts on the market
68
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
1929.01
1929.07
1930.01
1930.07
1931.01
1931.07
1932.01
1932.07
1933.01
1933.07
1934.01
1934.07
1935.01
1935.07
1936.01
1936.07
1937.01
1937.07
1938.01
1938.07
1939.01
1939.07
1940.01
1940.07
1941.01
1941.07
1942.01
1942.07
1943.01
1943.07
1944.01
1944.07
1945.01
1945.07
1946.01
1946.07
1947.01
1947.07
1948.01
1948.07
1949.01
1949.07
1950.01
1950.07
1951.01
1951.07
1952.01
1952.07
1953.01
1953.07
1954.01
1954.07
1955.01
1955.07
S&P composite price. Monthly. 1/29-12/55. Source: Robert Shiller
August 1929: 31.30 September 1954: 31.45

69. Some thoughts on the market
• What fueled the impressive
rally between November
1929 and April 1930?
• Nothing: Economic news
were dismal and in fact the
economy was in for a
massive downturn.
• No wonder the market
resumed its downfall
afterwards.
• It took 25 years to cross the
level of August 1929 again.
69
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
1930-01-01
1931-01-01
1932-01-01
1933-01-01
1934-01-01
1935-01-01
1936-01-01
1937-01-01
1938-01-01
1939-01-01
1940-01-01
1941-01-01
1942-01-01
1943-01-01
1944-01-01
1945-01-01
Real GNP growth –percent change from a year ago– 1930 to 1945. Source: St. Louis Fed

70. Some thoughts on the market
70
700
800
900
1000
1100
1200
1300
1400
1500
1600
2007.01
2007.03
2007.05
2007.07
2007.09
2007.11
2008.01
2008.03
2008.05
2008.07
2008.09
2008.11
2009.01
2009.03
2009.05
2009.07
2009.09
2009.11
2010.01
20.00
22.00
24.00
26.00
28.00
30.00
32.00
1928.12
1929.01
1929.02
1929.03
1929.04
1929.05
1929.06
1929.07
1929.08
1929.09
1929.1
1929.11
1929.12
1930.01
1930.02
1930.03
1930.04
S&P 500 index during two big market corrections: 2007-01 to 2010-01 & 1928-12 to 1930-04. Source: Robert Shiller

71. Some thoughts on the market
71
700
800
900
1000
1100
1200
1300
1400
1500
1600
2007.01
2007.03
2007.05
2007.07
2007.09
2007.11
2008.01
2008.03
2008.05
2008.07
2008.09
2008.11
2009.01
2009.03
2009.05
2009.07
2009.09
2009.11
2010.01
• The market has rallied
dramatically since the trough
in March 2009. What is
different?
- Some of it real: No Great
Depression – II
- Main difference: Policy
response
- Fiscal
- Monetary:
• Low rates and promises of
low rates
• Money base expansion

72. Some thoughts on the market
72
0
200
400
600
800
1000
1200
1400
1600
1800
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
2007-01-03
2007-01-24
2007-02-14
2007-03-07
2007-03-28
2007-04-18
2007-05-09
2007-05-30
2007-06-20
2007-07-11
2007-08-01
2007-08-22
2007-09-12
2007-10-03
2007-10-24
2007-11-14
2007-12-05
2007-12-26
2008-01-16
2008-02-06
2008-02-27
2008-03-19
2008-04-09
2008-04-30
2008-05-21
2008-06-11
2008-07-02
2008-07-23
2008-08-13
2008-09-03
2008-09-24
2008-10-15
2008-11-05
2008-11-26
2008-12-17
2009-01-07
2009-01-28
2009-02-18
2009-03-11
2009-04-01
2009-04-22
2009-05-13
2009-06-03
2009-06-24
2009-07-15
2009-08-05
2009-08-26
2009-09-16
Securities held outright S&P500
Securities (Treasuries, MBS, and Agency Debt) held by the Federal Reserve (in millions) vs. the S&P500; weekly; 2007-01-03 to 2009-09-16

73. Some thoughts on the market
• Whether the recovery justifies the prices is difficult to
say but there are reasons to question the recent rally.
• In the words of Jeremy Grantham:
- “Riding a bubble is a guilty pleasure totally denied to value managers
who typically pay a high price to the God of Investment Discipline …”
- “Risk taking has come roaring back. Value, it must be admitted, is
seldom a powerful force in the short term. The Fed’s weapons of low
rates, plenty of money and the promise of future help if necessary
seems stronger than value over a few quarters. And the forces of
herding and momentum are also helping to push prices up, with the
market apparently quite unrepentant of recent crimes and willing to be
silly again. ”
Jeremy Grantham, Just Deserts and Markets Being Silly Again, GMO Quarterly Letter, October 2009
73