1. The Momentum Effect
Starting point – Jegadeesh and Titman (1993):
“strategies which buy stocks that have performed well in the past and sell stocks that
have performed poorly in the past generate significant positive returns over 3- to 12-
month holding periods”
Data: US stocks, 1965 – 1989.
Selection period: 3 – 12 months
Holding period: 3 – 12 months
2. The Momentum Effect
Jegadeesh and Titman (1993):
Strategy: form deciles portfolios based on performance over the previous 9 or 12 months. Buy the
top decile (best performing 10%), short the lowest decile (worst performing 10%) for the subsequent
3-6-9-12 months. The return numbers are average monthly returns. T-statistics in parentheses.
3. The Momentum Effect
Jegadeesh and Titman (1993):
Crucial question: Does the effect hold out of sample?
No, for 1927-1940, roughly half as much as profitable in 1941-
1964.
Jegadeesh, Titman follow-on study (2001) – Similar returns found
for 1990 – 1998.
4. The Momentum Effect
Jegadeesh and Titman (1993):
Can it be explained by exposure to risk factors?
Answer: not by beta or the Fama-French risk factors (book to
market ratio and firm size).
5. The Momentum Effect
Rouwenhorst (1998): Similar effect found in 12 European markets
Strategy: form decile portfolios based on performance over the previous 3,6,9 or 12 months. Buy
the top decile (best performing 10%), short the lowest decile (worst performing 10%) for the
subsequent 3-6-9-12 months. Sample period: 1978 – 1995. Countries: UK, Germany, France, Italy,
Spain, Sweden, Norway, Denmark, Switzerland, Belgium, Netherlands, Austria.
6. The Momentum Effect
Momentum effect for other markets:
Interestingly, no one has been able to find a momentum effect for Japan so far.
There is evidence that there is a momentum effect for emerging markets in Asia
(Chui, Titman and Wei (2000))
7. The Momentum Effect
Potential rational explanation: Conrad and Kaul (1998)
• stock prices follow random walks with drifts, i.e. on average they appreciate year over
year.
• drift rates vary across stocks, all else equal, riskier stocks should have higher drift rates,
the differences in drift rates across stocks could explain momentum profits
• this hypothesis therefore predicts that the stocks on the long side of the momentum
portfolio should continue to outperform stocks on the short side by the same magnitude
in any post-ranking period.
• they find that differences in drift rates across portfolios explain a non-trivial portion of
momentum profits.
8. The Momentum Effect
Potential rational explanation: Tax loss selling (Grinblatt and Moskowitz (1999))
• Stocks with poor performance during the year may later be subject
to selling by investors keen to realize losses that can offset capital gains elsewhere.
• This selling pressure means that prior losers continue to lose, enhancing the
momentum effect. At the turn of the year, though, the selling pressure eases off,
allowing prior losers to rebound and weakening the momentum effect.
• Grinblatt and Moskowitz (1999) finds that on net, tax-loss selling may explain part of
the momentum effect, but by no means all of it.
9. The Momentum Effect
The momentum effect in January (from Jegadeesh and Titman (1993)):
Momentum is reversed in January!
10. The Momentum Effect
Behavioural Explanation: Underreaction due to Conservatism Bias
• Conservatism bias: Investors are slow to update their beliefs, i.e., they underweight
sample information which contributes to investor under-reaction to news
• “A combination of overconfidence, together with anchoring-and-adjustment leads
investors and analysts to adapt insufficiently to the arrival of new information. The result
is conservatism.” Shefrin
• Conservatism bias implies investor underreaction to new information
• Conservatism bias can generate
• Short-term momentum in stock returns
• The post-earnings announcement drift, i.e., the tendency of stock prices to
drift in the direction of earnings news for three-to-twelve months following an
earnings announcement also entails investor under-reaction
11. 11
• Behavioral Explanation: Overreaction due to overconfidence and biased self
attribution
– Investor overconfidence
• Overconfident investors place too much faith in their ability to
process information
• Investors overreact to their private information about the company’s
prospects
– Biased self-attribution
• Overreact to public information that confirms an investor’s private
information
• Underreact to public signals that disconfirm an investor’s private
information
– Contradictory evidence is viewed as due to chance
– Generate underreaction to public signals
The Momentum Effect
12. Investor overconfidence and biased self-attribution
• In the short run, overconfidence and biased self-attribution together
result in a continuing overreaction that induces momentum.
• Subsequent earnings outcomes eventually reveal the investor
overconfidence, however, resulting in predictable price reversals over
long horizons.
• Since biased self-attribution causes investors to down play the
importance of some publicly disseminated information, information
releases like earnings announcements generate incomplete price
adjustments.
The Momentum Effect
13. • Daniel, Hirshleifer, and Subrahmanyam (1998) argue that informed traders suffer
from a self-attribution bias.
• In their model, investors observe positive signals about a set of stocks, some of
which perform well after the signal is received.
• Because of their cognitive biases, the informed traders attribute the performance of
ex post winners to their stock selection skills and that of the ex post losers to bad luck.
As a result, these investors become over-confident about their ability to pick winners
and thereby overestimate the precision of their signals for these stocks.
• DHS argue that investors are more likely to be overconfident about private
information they have worked hard to generate than about public information.
• Based on their increased confidence in their signals, they push up the prices of the
winners above their fundamental values. The delayed overreaction in this model leads
to momentum profits that are eventually reversed as prices revert to their
fundamentals.
The Momentum Effect
14. Overreaction due to Representativeness Bias: A tendency to overemphasize the
most recent and the most salient may cause overreaction, creating excessive
volatility (continuing trends, then reversals).
Barberis, Shleifer, and Vishny (1998):
• The representative heuristic may lead investors to mistakenly conclude that firms
realizing extraordinary earnings growths will continue to experience similar
extraordinary growth in the future.
• Although the conservatism bias in isolation leads to underreaction, this
behavioural tendency in conjunction with the representative heuristic can
lead to long horizon negative returns for stocks with consistently high returns
in the past.
The Momentum Effect
15. Barberis, Shleifer, and Vishny (1998):
The Momentum Effect
“When a company announces surprisingly good earnings, conservatism
means that investors react insufficiently, pushing the price up too little.
Since the price is too low, subsequent returns will be higher on average,
thereby generating both post-earnings announcement drift and
momentum.
After a series of good earnings announcements, though, representativeness
causes people to overreact and push the price up too high.
The reason is that after many periods of good earnings, the law of small
numbers leads investors to believe that this is a firm with particularly high
earnings growth, and hence to forecast high earnings in the future. After
all, the firm cannot be “average”. If it were, then according the to law of
small numbers, its earnings should appear average, even in short samples.
Since the price is now too high, subsequent returns are too low
on average, thereby generating long-term reversals.”
16. On the representativeness heuristic:
“For example, people often predict future uncertain events by
taking a short history of data and asking what broader picture this
history is representative of. In focusing on such
representativeness, they often do not pay enough attention to
the possibility that the recent history is generated by chance
rather than by the ‘model’ they are constructing. Such heuristics
are useful in many life situations—they help people to identify
patterns in the data as well as to save on computation—but they
may lead investors seriously astray. For example, investors may
extrapolate short past histories of rapid earnings growth of some
companies too far into the future and therefore overprice these
glamorous companies without a recognition that, statistically
speaking, trees do not grow to the sky."
Shleifer (2000)
The Momentum Effect
17. The Momentum Effect
Hong and Stein (1999): Investors are “boundedly rational”
• Two groups of investors who trade based on different sets of information.
• The informed investors or the "news watchers" in their model obtain signals about
future cash flows but ignore information in the past history of prices.
• The other investors in their model trade based on a limited history of prices and, in
addition, do not observe fundamental information. (technical traders)
• The information obtained by the informed investors is transmitted with a delay and
hence is only partially incorporated in the prices when first revealed to the market.
• This part of the model contributes to underreaction, resulting in momentum profits.
• The technical traders extrapolate based on past prices and tend to push prices of past
winners above their fundamental values. Return reversals occur when prices eventually
revert to their fundamentals. Both groups of investors act rationally in updating their
expectations conditional on their information sets, but return predictability obtains due to
the fact that each group uses only partial information in updating its expectation.
18. The Momentum Effect
Hong and Stein (1999):
Bounded rationality:
Investors have:
• limited information
• limited cognitive capacity to process the information
• finite amount of time to make a decision
22. The Momentum Effect
The Disposition Effect and Momentum:
Disposition Effect (Shefrin and Statman, 1985): The tendency of investors to hold
onto their losing stocks to a greater extent than they hold onto their winners.
Grinblatt and Han (2005): If there are some disposition investors in a market, stocks
that have been privy to good news in the past would have excess selling pressure
relative to stocks that have been privy to adverse information. Hence, equilibrium
price would be reached only slowly, leading to momentum profits.
23. The Momentum Effect
Grinblatt and Han (2005):
• Using simple past returns in forming momentum portfolios is inferior to sorting stocks
on their capital gains overhang.
• Capital gains overhang = the difference between current market price and the
average cost basis for each stock among its shareholders.
• Use time series of past return and past turnover to proxy for capital gains overhang.
• Stocks with high capital gains overhang subsequently overperform significantly stocks
for which investors suffer large paper losses.
• The simple intermediate horizon past return loses its predictive power for future
return once the capital gain is controlled for.
