Momentum and contrarian strategies Lecture 5 Momentum and contrarian strategies

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

The momentum strategies

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)

Table 1: Returns of relative strength portfolios

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

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

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

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

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, 0.0012. 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.

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

Table 3 Panel A size- and beta-based portfolios

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

Table 3 Panel B size- and beta-based portfolios

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

Table 7 Performance beyond 12 months

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

The contrarian strategies

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

Table I. Returns for decile portfolios

Table I (cont.)

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

Figure 1

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

Table IV: Regression analysis

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

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

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

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

Table VII

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

Table VIII

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

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

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

Adaptive Market Hypothesis

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.

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.

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

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

Reading materials Jegadeesh, N., Titman, S., 1993, Returns to buying winners and selling losers: implications for market efficiency. Journal of Finance 48, 65-91. 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, 379-395. Rouwenhorst, G., 1998, International momentum strategies, Journal of Finance 53, 265-95.