1. Momentum and contrarian strategies
Lecture 5
Momentum and contrarian strategies

2. You will learn…
About the momentum and contrarian investment strategies
How tests were designed to confirm the empirical findings
Adaptive Market Hypothesis
Implications for investment

3. The momentum strategies

4. Jegadeesh and Titman (1993)
Study U.S. equity markets from 1965 to 1989 using CRSP data
Form a portfolio based on returns over past J months (J=3 to 12) and hold it for K months (K=3 to 12)
Divide stocks into ten deciles with ascending returns in past J months: Buy best winning decile; sell worst losing decile.
Panel A shows winning portfolios keep winning, losing portfolios keep losing
Panel B shows similar results when portfolios are formed 1 week after the formation period (J months)

5. Table 1: Returns of relative strength portfolios

6. Sources of relative strength profits
Jegadeesh and Titman tried to decompose the observed relative strength profits into
2 components relating to systematic risk, which could exist in an efficient market; and
a third component relating to firm-specific returns, which would suggest the market is inefficient
They used a one-factor model to decompose the stock returns

7. One-factor model where rit – return on security i
µi – unconditional expected return on security i
ft – unconditional expected on a factor-mimicking portfolio
eit – firm-specific component of return on security i
bi – factor sensitivity of security i

8. 2 systematic, 1 firm-specific components
The outperformance of winning portfolio implies
It may be shown that
where in (3)
1st term relates to expected returns
2nd term relates to the potential to time the factor
Last term is the average serial covariance of the idiosyncratic components of security returns

9. Are they high-risk stocks?
If the relative strength strategies systematically pick high-risk stocks and benefit from the 1st term in (3), then winning portfolios have large betas and small market capitalizations.
Table II on the right reveals a negative answer
Table II

10. What about the serial covariance of the factor and residuals?
It can be shown that
If the source of relative strength profits is the serial covariance of factor-related returns, then the serial covariance of the (equally weighted) index returns is positive.
But (4) = –0.0028
The estimates of the serial covariance of the one-factor model residuals are on average positive,
This suggests the relative strength profits may arise from stocks under-reacting to firm-specific information
The above is further supported by evidence that stocks do not react with a lag to the factor realizations.

11. Performance within size- and beta-based subsamples
Consider J=6/K=6 strategy as example
Divide equally weighted portfolios into
Small (S1), medium (S2) and large (S3) firms
Firms with small (β1), medium (β2) and large (β3) betas
P1 is the lowest return decile; P2 the next decile…
Panel A shows the average monthly returns

12. Table 3 Panel A size- and beta-based portfolios

13. Risk-adjusted returns
The risk-adjusted returns are estimated as
rpt – rft = αp – βp (rmt – rft) + eit
where
rpt – return on the portfolio p
rmt – return on the value-weighted index
rft – interest rate on 1-month Treasury Bill
The results are shown in Panel B

14. Table 3 Panel B size- and beta-based portfolios

15. Performance beyond 12 months
J=6/K=6 zero-cost (buy-sell) portfolio is considered
Negative returns beyond month 12
Relative strength strategy does not pick stocks with high unconditional expected returns
Initially positive and then negative returns
Observed price changes in the first 12 months are not permanent

16. Table 7 Performance beyond 12 months

17. Summary of momentum effects found by Jegadeesh and Titman (1993)
Cannot be explained by…
Systematic risk
Lead-lag effects due to delayed stock price reactions to common factors
But is consistent with
Delayed price reactions to firm-specific information
Negative returns observed after 12 months
DeLong et al (1990): momentum strategies move prices away from long-run values, thereby cause price to overreact

18. The contrarian strategies

19. Lakonishok et al. (1994)
Use returns data from CRSP and accounting data from COMPUSTAT
Sample period: April 1963 – April 1990
10-decile portfolios are formed in ascending order based on
B/M, ratio of book to market
C/P, ratio of cash flow to price
E/P, ratio of earnings to price
GS, preformation 5-year average growth rate of sales
Various returns are calculated
R1 through R5: returns for years 1 through 5
CR5: cumulative 5-year returns
SAAR: size-adjusted average annual 5-year return

20. Table I. Returns for decile portfolios

21. Table I (cont.)

22. Anatomy of contrarian strategies
Glamour (value) stock: high (low) growth in the past and high (low) expected future growth
High ratios of C/P (E/P) as a proxy for a low expected growth rate
Independently sort stocks into 3 groups by GS and C/P: 1 – bottom 30%; 2 – middle 40%; 3 – top 30%
See Figure 1 for GS-C/P results.
Similar results for GS-E/P, GS-B/M

23. Figure 1

24. Regression analyses Multiple regression analyses are used
to confirmed the contrarian strategies; as well as
identify which variables are significant
Fama-MacBeth (1973) procedure is used; run 22 cross-sectional regressions in which
Dependent variable: annual return on stock i for post-formation Year +1
Independent variables: characteristics of stock i

25. Table IV: Regression analysis

26. A test of the extrapolation model
Investors tie their expectation of future growth to past growth, i.e., they extrapolate the past too far into the future
A direct test of extrapolation looks at actual future growth rates and compare them to past growth rates and to expected growth rates as implied by the multiples

27. Table V: fundamental variables, past performance, and future performance
AEG – geometric average growth rate of earnings
ACG, ASG – defined analogously for cash flows and sales, respectively

28. Are contrarian strategies riskier?
Lakonishok et al. also check if superior returns to value strategies are due to greater systematic risk by
investigating the consistency of performance of the value and glamour strategies over time
Checking whether the times when value underperforms are recessions, times of severe market declines, etc, in which the marginal utility of consumption is high
No evidence of value strategies being riskier can be found

29. Figure 2. Year-by-year returns: value minus glamour

30. Table VII

31. Table VII (cont.) Table VII W25 – 25 worst stock return months
N88 – remaining 88 negative months
P122 – 122 positive months
B25 – 25 best stock return months

32. Table VIII

33. Summary of contrarian results
Lakonishok et al (1994) established three propositions
Value stocks outperformed glamour stocks over the period April 1968 to April 1990.
Investors appear to have consistently overestimate future growth rates of glamour stocks relative to value stocks
Value strategies appear to be no riskier than glamour strategies

34. Possible issues for contrarian results
Until 1990, quantitative investment strategies are relatively recent activities
But advocacy of value strategies dates back to Graham and Dodd (1934)
Data snooping
But similar results are found for Japan, France, Germany, Switzerland and UK
Evidence suggests a systematic expectational errors on the part of investors

35. A more plausible explanation
Lakonishok et al. conjecture the contrarian results be explained by the preference of both individual and institutional investors for glamour strategies and by their avoidance of value strategies

36. Adaptive Market Hypothesis

37. Adaptive Market Hypothesis (AMH)
Based on evolutionary principles, the Adaptive Markets Hypothesis implies that the degree of market efficiency is related to environmental factors characterizing market ecology such as
the number of competitors in the market,
the magnitude of profit opportunities available, and
the adaptability of the market participants.

38. AMH (cont.)
Many of the examples that behavioralists cite as violations of rationality that are inconsistent with market efficiency –
loss aversion,
overconfidence,
overreaction,
mental accounting, and
other behavioral biases
– are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics.

39. Implications of AMH
The relation between risk and reward is unlikely to be stable over time
Contrary to the classical EMH, arbitrage opportunities do exist from time to time in the AMH
Consistent with Grossman and Stiglitz (1980)
Investment strategies may perform well in certain environments and perform poorly in other environments.
Characteristics such as value and growth may behave like “risk factors” from time to time

40. In relation to momentum, value and growth
During the U.S. technology bubble of the late 1990's, growth stocks garnered higher expected returns than value stocks, only to reverse after the bubble burst.
Momentum strategy is likely to fail during recent financial crisis

41. Reading materials
Jegadeesh, N., Titman, S., 1993, Returns to buying winners and selling losers: implications for market efficiency. Journal of Finance 48,
Lakonishok, J., Shleifer A., et al., 1994, Contrarian investment, extrapolation and risk. Journal of Finance 49, 1541 – 1578.
Lo, A., 2004, Adaptive Market Hypothesis, Journal of Portfolio Management.
Forbes, W., 2009, Behavioural Finance, Wiley: Chapter 9 & 10
Other references
De Long, B., Shleifer, A., Summers, L., Waldman, R., 1990, Positive Feedback Investment Strategies and Destabilizing Rational Speculation. Journal of Finance 45,
Rouwenhorst, G., 1998, International momentum strategies, Journal of Finance 53,