The Role of Reference Points in Explaining Stock Returns Following Large Price Shocks
ABSTRACT
We analyze short-run price movements in individual stocks following returns that exceed 10% up or down in a single day.
The paper contributes to the literature by bridging the burgeoning literature on reference points together with market
efficiency tests following large price shocks. We find the stock’s proximity to its 52-week high price and 52 week-low
price just prior to the large price change helps explain the direction and magnitude of returns after controlling for a host of
other variables including size, value, momentum, liquidity and specific news events. These results are consistent with the
behavioral theory of anchoring. We also find that abnormal return-volume combinations prior to dramatic one-day price
moves can explain returns after the event. This suggests investors hold on to their private information signals even when
faced with a large one-day price move in the opposite direction.
Key Words: Market efficiency; Anchor; Overreaction; Underreaction, 52-week high, 52-week low
0
I. Introduction
According to the semi-strong version of the efficient markets hypothesis, stock prices should reflect all publicly
available information. There should be no evidence of systematic positive or negative returns beyond what would be
predicted using conventional asset pricing models. The academic literature is filled with empirical investigations that test
this proposition. One strand of this research focuses on how equities perform after large price shocks, which are typically
defined as a change of 10% or more in a single day. Amini, Gebka, Hudson, & Keasy (2013), AGHK going forward,
provide a comprehensive review of over 60 studies in this specific area. While AGHK document prior research that has
found elements of stock price predictability following large price changes, they suggest that the literature in this area
continues to grow because of the lack of agreement in the empirical findings. Our paper is motivated by the ongoing
debate in this branch of research.
We investigate short-term stock price movements following one-day price changes of at least 10%. We conjecture
that when investors are faced with a large price shock they are more likely to use reference points in their decision
making, especially when there is not any accompanying news. Our main research question is behavioral at its core since
there is no rational reason why reference points should have an impact on future returns in an efficient market. We
develop hypotheses based on the theory of anchoring first proposed by Kahneman, Slovic, and Tversky ((1982), pp. 8498), and theories on reaction to news discussed in Griffin and Tversky (1992) and applied by George and Hwang (2004).
Our results show that a stock’s proximity to its 52-week high and 52-week low just prior to the event day help explain the
direction and magnitude of future short-term returns even after controlling for size, valuation, momentum, specific news
events, and a number of other factors that have been used to explain returns.1
AGHK classify the existing explanations of return predictability following large price moves into three broad
categories: changes in risk, market microstructure issues, and behavioral biases.2 Our research contributes to the
behavioral bias portion of this literature in several ways. First, it offers a new dynamic to the existing behavioral
explanations of returns following price shocks. Prior behavioral theories have emphasized that the manner in which
information is processed contributes to underreaction or overreaction. For example, Savor (2012) uses the release of an
1
We include both reference points in our models because a stock that traded in a wide range during the past year could be far from
its 52-week low, but not close to its 52-week high.
2
A more detailed review of existing studies can be found in Section II.
1
analyst report surrounding the large price move as a proxy for information and finds convincing evidence that stocks
underreact when there is information associated with the large price change, but overreact when there is no concurrent
information. However, the majority of the price moves in his sample are not accompanied by his proxy for information.
This leads us to hypothesize that when investors are confronted with a dramatic price shock, they will rely on other
information concerning their investment (or potential investment), such as easily identifiable reference points like the 52week high and 52-week low. We test this proposition in logistic and OLS models and show that a large directional move
away from a reference point is associated with underreaction. For example, the closer a stock is trading to its 52-week
high, the higher the probability it has a negative cumulative abnormal return (CAR) in various trading day windows
following a large, one-day price drop. As another example, the closer a stock is trading to its 52-week low before a large,
one-day price gain, the larger the CAR after the event day. These results are consistent with investors anchoring to the 52week high and 52-week low reference points on the event day.
The second way this paper contributes is by bridging the growing literature on reference points together with
market efficiency tests following large price shocks. Recent behavioral studies identify the existence of anchors that have
psychological importance to investors and contribute to overreaction or underreaction. In other words, incorporating these
reference points into models helps predict returns. For example, George and Hwang (2004) observe underreaction to
additional good news when a stock is near a 52-week high. They explain that stocks close to a 52-week high have recently
had good news, which causes investors discount any additional positive information. This results in underreaction to the
positive information, which then leads to positive abnormal returns (i.e., momentum) going forward as the stock moves
towards its intrinsic value. Li and Yu (2012) combine anchoring and limited investor attention theories to explain overall
market returns as opposed to individual stocks.3 These types of studies, however, usually form portfolios of stocks, or
examine longer term-returns, or do not use our large price shock setting. In addition, many of these studies exclude the 52week low in their analysis.4 In short, we are unaware of another study that uses both reference points to explain short-term
price movements in the cross-section of individual stocks following substantial, one-day price movements.
Finally, we contribute by adding other variables in our models that seem particularly relevant in light of prior
arguments made in the literature. For example, in their model of returns following large single day returns, Larson and
3
Our setting of large single-day price shocks reduce investor inattention issues highlighted in Li and Yu (2012): a large percentage
change in a stock is likely to be noticed by its existing and potential investors.
4
Sturm (2008) is a notable exception.
2
Madura (2003) control for abnormal price movements in the days leading up to the event day because Daniel, Hirshleifer,
and Subrahmanyam (1998) suggest markets overemphasize informed trading signals. Meulbrock (1992) finds that one
form of informed trading, insider trading, is associated with trading volume spikes. Thus, we hypothesize that there will
be a differential impact on future returns following a large price shock if an abnormal share price change prior to the event
day was accompanied with abnormally heavily volume in that period compared to abnormally low volume. We include
several dummy variables to capture this effect and find that they are useful in predicting future returns.
II. Literature Review and Hypotheses Development
Literature Review
An early study by Bremer and Sweeney (1991) showed that individual stocks exhibit positive abnormal returns
for up to three days following a one day price decline of more than 10%. The implication was that the stock price had
overreacted on the day of the large drop. Their evidence was consistent with the Uncertain Information Hypothesis (UIH)
proposed by Brown, Harlow, and Tinic (1988). The UIH claims that large price changes are associated with an increase in
risk, thereby depressing current returns. In time, this risk premium reduces which inflates the stock price to some degree.
This reflation explains the positive abnormal returns after the large price drop.
Later studies brought into question the role of the UIH in explaining the reversal following large price declines.
Cox and Peterson (1994) found that returns following large down moves could be largely explained by negative
autocorrelation in security returns resulting from prices oscillating between the bid and asked spread. In other words,
stocks that had heavy declines in a day were more likely to trade on the bid price at the close of trading. The next day, if
they traded at the ask price, this could account for a large amount of the reversal, especially in thinly traded stocks with
wide spreads. The implication of this market microstructure anomaly was that a trading strategy could not be used to earn
excess returns after large price declines. Furthermore, Liang and Mullineaux (1994), Park (1995), and Corrado and Jordan
(1997) found that after a large price increase, returns tended to be abnormally negative. The UIH, however, predicts a
higher return in this setting since the increase in risk associated with the large price change would initially dampen the
impact of the underlying reason causing the large return. Liang and Mullineaux (1994) and Park (1995) both found
evidence that the “bid-ask bounce” explained above could partially explain their results. However, Choi & Jayaraman
3
(2009) do not think this “bid-ask bounce effect” is the driving force behind the apparent overreaction. They find that only
stocks that do not trade options overreact to large down moves.
As mentioned above, the research in this area also has major behavioral component. Griffin and Tversky (1992)
suggest that individuals underreact to sporadic news, but overreact to a noticeable trend of prior performance. George and
Hwang (2004) find the market underreacts to incremental good news when a stock is trading close to a 52-week high.5
Their results indicate that the short-term underreaction can be characterized as an anchoring bias that the market resolves
later on. Chan (2003) and Savor (2012) have shown that the returns after the price decline depend whether or not there
was any accompanying news on the day of the stock drop or soon after. Chan focuses on the existence of a news headline,
while Savor looks for the production of an analyst report. Pritamani & Singal (2001) and Larson & Madura (2003) study
several of the specific causes of the large price movements, such as earnings announcements, and link them to variations
in the cross-sectional abnormal returns in different ex-post time frames. The results in these studies are consistent with
behavioral biases rooted in information processing. We include dummy variables for a host of different types of specific
news events surrounding the large, single day price movements. We believe our approach is the most comprehensive to
control for this type of potential behavioral bias and also allows us to analyze which types of news investors tend to
misprice on the event date.
Our main hypotheses develop from a combination of the anchoring and news assimilation theories. The 52-week
high/low price for a security has been documented as an important psychological consideration in investment decisions.6
For example, if a stock is near its 52-week high, we assume that market participants anchor to this high because investors’
priors on the stock’s future performance are strong. We hypothesize that investors underreact by holding back the supply
of shares and/or increasing the demand for shares to a greater degree the closer the stock was to its 52-week high prior to a
large one-day decline as they expect that the stock can return to that reference point.7 As time goes on, however, the stock
price continues downward to its intrinsic value. This makes it more likely that the CAR will be negative in the ensuing
days after the large negative return on the event day.
5
As mentioned earlier, they do not look at the 52-week low.
For example, see George & Hwang (2004).
7
This line of reasoning requires a downward sloping demand curve for a firm’s shares. Asquith and Mullins (1986) and Schleifer
(1986) find support that downward sloping demand curves for firms’ shares exist.
6
4
Main Hypothesis I: The stock’s proximity to its 52-week high and / or 52-week low on the day before the event is useful
in predicting the direction and magnitude of the post-event CAR following a large single day price move. Based on the
anchoring theory above, we expect:
Directional Predictions


The closer the stock is to its 52-week high before a large negative shock, the more likely the sign of the post-event
CAR is negative.
The closer the stock is to its 52 week low before a large positive shock, the more likely the sign of the post-event
CAR is positive.
Magnitude Predictions


The closer the stock is to its 52-week high before a large negative shock, the lower (i.e., more negative) is the
post-event CAR.
The closer the stock is to its 52-week low before a large positive shock, the higher the average post-event CAR
will be.
It is important to note that we do not make predictions a priori about what happens when a stock experiences a
shock in the same direction as its nearness to the reference point. For example, we make no prediction about the postevent CAR when a stock trading near its 52-week high experiences a large positive shock. The reason is that the
prediction based on anchoring theory will depend on whether the stock price trades below or above the 52-week high on
the event day.
Abnormal volume has been examined in a wide variety of settings in the finance literature. We include a measure
of abnormal volume in conjunction with the cumulative abnormal return prior to the event day since Hong and Stein
(2007) suggest behavioral asset pricing models that stress differential beliefs should include joint measures of price and
volume. Meulbroek (1992) shows that insider trading that can lead to abnormal returns before a large event is
accompanied by changes in trading volume. We conjecture that if there is a negative abnormal CAR and abnormally high
volume just prior to a subsequent, large one-day decline, this event was somewhat expected.
Table 2 contains descriptions of various combinations just prior to the large, one-day price move. Panel A
describes these combinations in relation to large price drops and Panel B in relation to large price rises. In Table 2, Panel
A, the situation described in the last paragraph is “D”, which is our base case. We predict that there will be a different
post-event CAR compared to the base case for the other pre-event return-volume combinations. For example, if there was
abnormally high volume (i.e., pre-VOL is Positive) and the stock had a large positive pre-event CAR before the decline
(i.e., positive RUNUP), we predict a lower post-CAR since the market’s prior expectation was strongly contradicted and
5
investors will anchor to their prior convictions. In other words, in Table 2, Panel A, “A” is labeled as “strong
contradiction” because the stock had an abnormal move up on abnormally positive volume just prior to its fall.
Main Hypothesis II: The stock’s abnormal return-volume pattern before a large-one day price move is useful in predicting
the direction and magnitude of the post-event CAR following a large single day price move.
Directional Predictions


Compared to the situation where there was negative runup and strong volume prior to the large one day decline
(i.e., strong confirmation in Table 2, Panel A), other return –volume combinations are associated with a lower
likelihood of positive abnormal returns after the event day.
Compared to the situation where there was negative runup and strong volume prior to the large one day rise (i.e.,
strong contradiction in Table 2, Panel B), other return –volume combinations are associated with a lower
likelihood of positive abnormal returns after the event day.
Magnitude Predictions


Compared to the situation where there was negative runup and strong volume prior to the large one day decline
(i.e., strong confirmation in Table 2, Panel A), other return –volume combinations are associated with lower (i.e.,
more negative) abnormal returns after the event day.
Compared to the situation where there was negative runup and strong volume prior to the large one day rise (i.e.,
strong contradiction in Table 2, Panel B), other return –volume combinations are associated with lower (i.e., more
negative) abnormal returns after the event day.
III. Sample and Methodology
Sample Construction
Our study investigates single-day returns that are larger than 10% either up or down from 1/1/1995 to 12/31/2012.
We use this date range because we control for specific types of news events, such as mergers, stock splits, analyst reports,
etc., and the 1/1/1995 starting date was the best in order to capture all of the events in the databases. We obtain security
return data from the CRSP database and identify all stocks that experienced a return of greater than 10% in absolute value
terms in a single trading day. Following Bremer (1991), we only include stocks where the closing price was an actual
transaction price, as opposed to an average of the bid-ask spread. We eliminate firms with a CRSP share code of 14 and
above (which includes ADRs, unit trusts, etc.) and any stocks that do not trade on the NYSE, AMEX or NASDAQ
exchanges. We remove those stocks that closed at less than $5 to eliminate stocks more susceptible to the “bid-ask bounce
effect” identified by other researchers and mentioned in earlier in this paper.8 We also require stocks to have traded at
least 10,000 shares on the day of the large price move because to reduce the effects of the bid-ask bounce. We also require
8
For example, see Bremer (1991), Cox & Peterson (1994) and Park (1995).
6
the full set of variables to be available for my analysis, including control variables obtained from the COMPUSTAT
database, such as book value. The final sample is comprised of 70,607 events in the down sample and 130,989 in the up
sample. These are broken down by year in Table 1.9 This table shows the declines are clustered in the 1998 through 2001
years.10 Below is a list and explanation of the variables used in this paper.
CAR#P: A dummy variable taking the value of 1 if the cumulative abnormal return in the #-day window following the
large price move is positive. This is the dependent variable in our logit regressions.
CAR#: The cumulative abnormal return in the #-day window starting the day after the large price change. This is the
dependent variable in the multivariate OLS regressions.11
RUNUP: The cumulative abnormal return in the 5-day window before days before large price change. This is a control
variable included following Larson and Madura (2003).
RET0: The raw return on the day of the large price change. We include this variable to control for any impact the raw
size of the one-day return may have on the post-event day abnormal return windows.
VOL_RUNUP_A: A dummy variable taking the value of 1 if the cumulative abnormal volume (“CAV”) and the CAR in
the 5-day window prior to the large price change were both positive and 0 otherwise.
VOL_RUNUP_B: A dummy variable taking the value of 1 if the CAV was negative and the CAR was positive in the 5day window prior to the large price change and 0 otherwise.
VOL_RUNUP_C: A dummy variable taking the value of 1 if the CAV and CAR in the 5- day window prior to the large
price change were both negative and 0 otherwise.
Note: The preceding three variables are based on hypothesis 2.
52_WK_HI: 1- [(stock’s 52-week high - stock’s closing price on the day prior to the large move) / stock’s 52-week high]
 As this measure moves closer to 1, it implies that the stock was closer to its 52-week high the day prior to the
drop. A value of 1 indicates the stock closed at is 52-week high the day prior to the large move and it is the
maximum value this variable can assume.
52_WK_LO: 1- [(stock’s closing price on the day prior to the large move - stock’s 52-week low) / stock’s 52-week low]
 As this measure moves closer to 1, it implies that the stock was closer to its 52-week low the day prior to the
move. A value of 1 indicates the stock closed at its 52-week low the day prior to the large move and it is the
maximum value this variable can assume.
Note: The preceding two variables are based on hypothesis 1.
LN_MKTVAL: The natural logarithm of the company’s market valuation, calculated as price times shares outstanding
on the day prior to the large price change. This is a control variable included to control for the well-known impact of size
on returns (e.g., Fama and French (1992))
9
All tables referenced are at the end of the paper.
In the regression analysis in Section V, we control for cross-sectional dependence by using robust standard errors clustered by
date. Alternative specifications that control for year and industry effects do not materially change the results found in that section.
11
For example, CAR5 is the cumulative abnormal return over the next 5 trading days following the large price move.
10
7
MOM: The cumulative monthly abnormal return over the prior six trading months ending one month before the large
price change. This variable is included to control for the momentum effect of Jegadeesh and Titman (1993).
VIX: The percentage change in the CBOE Volatility Index on the day of the large price decline.
Q: Proxy for Tobin’s Q ratio calculated as (market value of equity + book value of debt) / book value of assets.12
LN_VOL: The natural logarithm of the number of shares traded on the day of the large price change. We include this
variable to control for the impact of liquidity on returns as in Pastor and Stambaugh (2001).
ANLYST: A dummy variable taking the value of 1 if there was an analyst report issued on either the day of the large
price decline or the day before, and 0 otherwise.
ASSET_SALE: A dummy variable taking the value of 1 if there was an announcement of an asset sale on either the day
of the large price decline or the day before, and 0 otherwise.
EARNINGS: A dummy variable taking the value of 1 if there was an earnings report issued on either the day of the large
price decline or the day before, and 0 otherwise.
SHR_ISSUE: A dummy variable taking the value of 1 if there was an seasoned equity offering on either the day of the
large price decline or the day before, and 0 otherwise.
MERGER: A dummy variable taking the value of 1 if there was a merger announcement on either the day of the large
price decline or the day before, and 0 otherwise.
REPO: A dummy variable taking the value of 1 if there was a share repurchase announcement on either the day of the
large price decline or the day before, and 0 otherwise.
SPLIT: A dummy variable taking the value of 1 if there was a stock split on either the day of the large price decline or the
day before, and 0 otherwise.
Table 3 contains summary statistics of our variables. For parsimony, we do not include a correlation matrix of the
independent variables. However, multicollinearity is not an issue. This is important because in our models we include
both the stock’s proximity to its 52-week high and its 52-week low. The correlation is significant at -.177, but it is not so
high as to preclude these measures from providing independent information. For example, a stock that has had a wide
trading range on the year could be far from its 52-week low, but not close to its 52-week high.
Methodology
We test our hypotheses concerning the likelihood that the sign of the cumulative abnormal returns is positive
following the event day by performing logistic regressions on the following equation:
CAR#Pi = α + β1(RUNUPi) + β2(RET0i) + β3(VOL_RUNUP_Ai) + β4(VOL_RUNUP_Bi) + β5(VOL_RUNUP_Ci) +
β6(52_WK_HIi) + β7(52_WK_LOi) + β8(LN_MKTVALi) + β9(MOMi) + β8(VIXi) + β11(Qi) + β12(LN_VOLi) +
12
We calculate this variable using the same method as McConnell and Sevaes (1990) and Lie (2000). We use this variable instead of
the book-to-market since some observations in our sample had negative book values.
8
β13(ANLYSTi) + β14(ASSET_SALEi) + β15(EARNINGSi)+ β16(SHR_ISSUEi) + β17(MERGERi) + β18(REPOi) +
β19(SPLITi) + εi
(1)
The CAR#Pi is a dummy variable that takes on the value of 1 if the cumulative abnormal return in the designated time
frame is positive and 0 otherwise.
We test our hypotheses concerning the magnitude of the cumulative abnormal returns following the event day in a
multivariate regression framework using OLS by estimating the following equation:
CAR#i = α + β1(RUNUPi) + β2(RET0i) + β3(VOL_RUNUP_Ai) + β4(VOL_RUNUP_Bi) + β5(VOL_RUNUP_Ci) +
β6(52_WK_HIi) + β7(52_WK_LOi) + β8(LN_MKTVALi) + β9(MOMi) + β8(VIXi) + β11(Qi) + β12(LN_VOLi) +
β13(ANLYSTi) + β14(ASSET_SALEi) + β15(EARNINGSi)+ β16(SHR_ISSUEi) + β17(MERGERi) + β18(REPOi) +
β19(SPLITi) + εi
(2)
The variable names were discussed above.
It is important to note that the independent variables that we use in modeling returns can be measured on the day
of the decline. Some recent research on underreaction and overreaction has looked to the help of information released
after the stock price decline to provide information on future security movements. For example, Savor (2012) includes in
his analysis analyst reports that are released surrounding the day of the large price movement. This allows the information
in the analyst report to cause or be in response to the stock price movement. He further documents that forming portfolios
based on these findings would generate annualized abnormal returns exceeding 30%. Since the variables used our model
can be measured ex-ante, they could improve the risk-return profile of this trading strategy by narrowing the set of
securities included in his portfolios.13
IV. Results
Tables 4 and 5 contain the results from a logistic regression analysis to see if the independent variables can offer
insight into the probability of the post-event CAR being positive. The logistic function was applied to equation (1) from
section III. The dependent variable is a dummy variable equal 1 if the post-CAR was positive and zero otherwise.
Table 4 contains the results from the large down mover sample. The main variables of interest are 52_WK_HI,
VOL_RUNUP_A, VOL_RUNUP_B, and VOL_RUNUP_C.14 We find a statistically significant negative coefficient on
13
14
This extension is not explored in this paper.
For parsimony, we relegate a discussion of the control variables to the internet appendix.
9
52_WK_HI in five of our specifications. The negative coefficient implies that the closer the stock was trading to its 52week high prior to the large down move, the lower the probability that the post-event CAR is positive. This is consistent
with investors anchoring to the 52-week high on the event day which induces underreaction. As expected, we find
negative and statistically significant coefficients on all of our volume-return indicator variables. Recall that the base case
(i.e., the category not explicitly included in the model) was a negative cumulative abnormal return on abnormally strong
volume in the five day window prior to the large down move. In this base case situation, we expected the highest
probability of a positive post-CAR compared to other pre-event volume-return patterns because the down move on heavy
volume was a strong confirmation of prior expectations. The negative coefficients on our dummy variables imply that
other volume-return patters prior to the large fall are associated with a lower probability of the stock having a positive
abnormal return after the event.
Table 5 contains the results from the large up day sample. The main variables of interest are 52_WK_LO,
VOL_RUNUP_A, VOL_RUNUP_B, and VOL_RUNUP_C. We find a statistically significant positive coefficient on
52_WK_LO in five of our specifications. The positive coefficient implies that the closer the stock was trading to its 52week low prior to the large up move, the higher the probability that the post-event CAR is positive. This is consistent with
investors anchoring to the 52-week low on the event day which induces underreaction. We find some evidence that the
volume-return indicator variables are useful in predicting returns in the hypothesized direction following a large one-day
up move. Recall that the base case was a negative cumulative abnormal return on abnormally strong volume in the five
day window prior to the large up move. In this base case situation, we expected the highest probability of a positive postCAR compared to other pre-event volume-return patterns because the up move on heavy volume was a strong
contradiction of prior expectations. The negative coefficients on about half our volume-return dummy variables imply that
other volume-return patters prior to the large rise are associated with a lower probability of the stock having a positive
abnormal return after the event.
Tables 6 and 7 contain the results from OLS regressions to see if the independent variables can offer insight into
the magnitude of the post-event CAR. The OLS model we apply is (2) from section III. The dependent variable is the
post-event day CAR in various trading day windows. Table 6 contains the results from the large down mover sample. The
main variables of interest are 52_WK_HI, VOL_RUNUP_A, VOL_RUNUP_B, and VOL_RUNUP_C. We find a
negative and statistically significant coefficient on 52_WK_HI in seven of the eight models. This result implies that the
10
closer the stock was trading to its 52-week high prior to the large decline, the lower (i.e., more negative) is the post-event
day CAR. This is consistent with our anchoring hypothesis. Our pre-event day volume-return indicator variables are
broadly negative and significant. This implies that compared to the case where the stock had moved abnormally lower on
strong volume even before the one-day price drop, other volume-return patterns are associated with lower post-event
CARs. This is consistent with our hypotheses and suggests investors anchor to their private signals when faced with large
price shocks in an unanticipated direction.
Table 7 contains the results from the large up mover sample. The main variables of interest are 52_WK_LO,
VOL_RUNUP_A, VOL_RUNUP_B, and VOL_RUNUP_C. We find mixed results on the 52-week low variable. In the 3,
5, and 20 day post-event windows, the positive and significant coefficient suggests that investors underreact on the event
day. However, the negative coefficient on the 1-day event window is opposite of out predictions. Our pre-event day
volume-return indicator variables are also mixed, but generally significant in the hypothesized direction.
Our overall results suggest that reference points and trading patterns prior to a large single-day price shock are
useful in predicting future abnormal returns even after controlling for a host of other factors known to impact returns. This
suggests an element of market inefficiency that we attribute to an anchoring bias by investors when faced with a large
unexpected price move.
V. Conclusion
This paper hypothesized that investors anchor to stock price reference points following large negative or positive
shocks of more than 10% in a single trading day. We show that the stock’s proximity to its 52-week high and 52 weeklow just prior to the large price change helps explain returns even after controlling for size, value, momentum, liquidity,
news, and other variables known to impact returns. Stocks close to their 52-week high before a large one-day drop are less
likely to have a positive abnormal return in short-term trading day windows following the event. They are also associated
with lower cumulative abnormal returns after the large price drop. In other words, stocks closer to their 52-week high tend
to underreact on the day of a large price decline. Stocks close to their 52-week low before a large one-day rise are more
likely to have a positive abnormal return in short-term trading day windows following the event. They are also associated
with higher cumulative abnormal returns after the large price rise. In other words, stocks closer to their 52-week high tend
11
to underreact on the day of a large price decline. These results are consistent with investors anchoring to well-established
reference points in the face of a large price shock.
We also document that there differential impact on future returns if an abnormal share price increase prior to the
event day was accompanied with abnormally heavily volume in that period compared to abnormally low volume. We
include several dummy variables to capture this effect and find that they are useful in predicting future returns. The paper
contributes to the literature by using reference points in market efficiency tests following large price shocks. Our results
may augment the investment strategies suggested by other authors who have found predictability in returns following
large one-day price changes.
12
Table 1
Table 1 contains frequency distributions by year of the number of stocks with decreases (“Down Events”) and increases (“Up Events”)
of at least 10% in a single trading day that qualified for our sample based on criteria outlined in Section III. The declines are clustered
in the 1998 through 2001 years. In our regression analysis, we control for clustering (not shown here) in the standard errors to take
into account the cross-sectional dependence of daily stock returns highlighted by Rogers (1993) and implemented by Savor (2012).
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Total
Number of Down Events Percent of Total Number of Up Events Percent of Total
2,245
3.18%
5,479
4.18%
3,426
4.85%
7,853
6.00%
3,526
4.99%
8,013
6.12%
6,117
8.66%
11,362
8.67%
5,335
7.56%
13,415
10.24%
12,661
17.93%
21,151
16.15%
6,484
9.18%
11,175
8.53%
4,207
5.96%
6,163
4.70%
1,851
2.62%
4,404
3.36%
2,029
2.87%
3,981
3.04%
1,713
2.43%
2,987
2.28%
1,667
2.36%
2,971
2.27%
2,138
3.03%
3,574
2.73%
8,564
12.13%
11,998
9.16%
4,190
5.93%
7,888
6.02%
1,283
1.82%
2,629
2.01%
1,840
2.61%
3,652
2.79%
1,331
1.89%
2,294
1.75%
70,607
100%
13
130,989
100%
Table 2
Panel A contains joint measures of abnormal volume and run-up prior to the large price drop. For example, “A” is labeled
as “strong contradiction” because the stock had an abnormal move up on abnormally positive volume just prior to its fall.
RUNUP is Positive
RUNUP is Negative
Pre-VOL is Positive
Pre-VOL is Negative
Strong Contradiction
Weak Contradiction
“A”
“B”
Strong Confirmation
Weak Confirmation
“D”*
“C”
*“D” is not explicitly in the multivariate models tested later in order to avoid the dummy variable trap.
Panel B contains joint measures of abnormal volume and run-up prior to the large price rise. For example, “A” is labeled
as “strong confirmation” because the stock had an abnormal move up on abnormally positive volume just prior to its rise.
RUNUP is Positive
RUNUP is Negative
Pre-VOL is Positive
Pre-VOL is Negative
Strong Confirmation
Weak Confirmation
“A”
“B”
Strong Contradiction
Weak Contradiction
“D”*
“C”
*“D” is not explicitly in the multivariate models tested later in order to avoid the dummy variable trap.
14
Table 3
Panel A contains summary statistics for the sample of stocks that fell 10% or more in one day between 1995 and 2012 that
fit our criteria and data collection standards.
Variable
Mean
Std. Dev.
Min
Max
CAR20
-0.0058
0.2323
-2.4823
2.9248
CAR5
0.0115
0.1340
-1.4408
2.7227
CAR3
0.0107
0.1121
-1.3387
2.2239
CAR1
0.0082
0.0760
-0.8465
1.2482
RUNUP
0.0112
0.1909
-1.0362
6.8205
RET0
-0.1444
0.0560
-0.7917
-0.1000
VOL_RUNUP_A
0.2848
0.4513
0.0000
1.0000
VOL_RUNUP_B
0.1867
0.3897
0.0000
1.0000
VOL_RUNUP_C
0.2198
0.4141
0.0000
1.0000
NEAR_YR_HI
0.6066
0.2460
0.0167
1.0000
NEAR_YR_LO
-0.4849
4.9260
-366.9441
1.0000
LN_MKTVAL
19.6894
1.5265
12.0694
27.0458
MOM
0.0783
0.8918
-41.3888
13.5976
VIX
27.5671
12.8650
9.8900
80.8600
Q
3.8402
7.8267
0.0708
397.4457
LN_VOL
13.0680
1.9571
9.2103
20.5032
ANLYST
0.1515
0.3585
0.0000
1.0000
ASSET_SALE
0.0037
0.0603
0.0000
1.0000
EARNINGS
0.1749
0.3799
0.0000
1.0000
SHR_ISSUE
0.0029
0.0539
0.0000
1.0000
MERGER
0.0050
0.0705
0.0000
1.0000
REPO
0.0023
0.0478
0.0000
1.0000
SPLIT
0.0016
0.0398
0.0000
1.0000
15
Panel B contains summary statistics for the sample of stocks that rose 10% or more in one day between 1995 and 2012
that fit our criteria and data collection standards.
Variable
Mean
Std. Dev.
Min
Max
CAR20
-0.0354
0.2484
-4.8637
11.4770
CAR5
-0.0156
0.1288
-1.3781
3.4923
CAR3
-0.0118
0.1057
-1.1147
2.9285
CAR1
-0.0034
0.0724
-0.9811
2.5981
RUNUP
0.0005
0.1648
-2.3206
6.6302
RET0
0.1574
0.0991
0.1000
6.2593
VOL_RUNUP_A
0.2961
0.4565
0.0000
1.0000
VOL_RUNUP_B
0.1834
0.3870
0.0000
1.0000
VOL_RUNUP_C
0.2282
0.4196
0.0000
1.0000
NEAR_YR_HI
0.6029
0.2552
0.0072
1.0000
NEAR_YR_LO
-0.2913
4.1886
-387.0000
1.0000
LN_MKTVAL
19.2879
1.4968
12.3218
26.7282
MOM
0.0509
0.8368
-41.3888
29.4922
VIX
24.6636
10.5463
9.8900
80.8600
Q
4.8180
41.2521
0.0525
8006.4960
LN_VOL
12.7766
1.7787
9.2103
20.7503
ANLYST
0.0899
0.2860
0.0000
1.0000
ASSET_SALE
0.0050
0.0704
0.0000
1.0000
EARNINGS
0.1163
0.3206
0.0000
1.0000
SHR_ISSUE
0.0017
0.0406
0.0000
1.0000
MERGER
0.0219
0.1463
0.0000
1.0000
REPO
0.0034
0.0584
0.0000
1.0000
SPLIT
0.0016
0.0400
0.0000
1.0000
16
Table 4
Table 4 contains the results from estimating the model below using logit on the “Down Event” (i.e., a raw return of at most -10% in a
single trading day). The dependent variable, CAR#P takes on the value of 1 if the cumulative abnormal return obtained in the # day
post-event window is positive and 0 otherwise. The independent variables are defined in Section III.
CAR#Pi = α + β1(RUNUPi) + β2(RET0i) + β3(VOL_RUNUP_Ai) + β4(VOL_RUNUP_Bi) + β5(VOL_RUNUP_Ci) + β6(52_WK_HIi) + β7(52_WK_LOi) +
β8(LN_MKTVALi) + β9(MOMi) + β8(VIXi) + β11(Qi) + β12(LN_VOLi) + β13(ANLYSTi) + β14(ASSET_SALEi) + β15(EARNINGSi)+ β16(SHR_ISSUEi) +
β17(MERGERi) + β18(REPOi) + β19(SPLITi) + εi
CAR20P
RUNUP
-0.0388
(-0.57)
RET0
-0.580***
(-3.23)
VOL_RUNUP_A -0.0536*
(-1.90)
VOL_RUNUP_B -0.0512*
(-1.72)
VOL_RUNUP_C -0.0527**
(-2.14)
52_WK_HI
-0.399***
(-7.50)
52_WK_LO
0.0236***
(5.91)
LN_MKTVAL
0.0665***
(4.74)
MOM
-0.0757***
(-5.24)
VIX
0.382*
(1.71)
Q
-0.0149***
(-6.32)
LN_VOL
-0.0933***
(-9.48)
ANLYST
CAR20P
CAR5P
CAR5P
CAR3P
CAR3P
CAR1P
CAR1P
-0.366***
(-4.81)
-0.636***
(-3.37)
-0.114***
(-3.92)
-0.198***
(-6.38)
-0.125***
(-4.99)
-0.274***
(-4.97)
0.0121***
(4.61)
0.0937***
(7.38)
-0.0291**
(-2.38)
0.863***
(3.25)
-0.00685***
(-4.15)
-0.141***
(-15.04)
-0.310***
(-4.33)
-0.716***
(-3.90)
-0.147***
(-4.93)
-0.208***
(-6.41)
-0.147***
(-5.70)
-0.182***
(-3.17)
0.0106***
(4.07)
0.0857***
(6.85)
-0.0307**
(-2.36)
0.628**
(2.04)
-0.00401***
(-2.70)
-0.143***
(-16.13)
0.503***
(2.91)
-0.380***
(-5.23)
-0.961***
(-5.14)
-0.126***
(-4.17)
-0.151***
(-4.64)
-0.118***
(-4.54)
-0.0469
(-0.83)
0.0124***
(4.48)
0.0766***
(6.19)
-0.0429***
(-3.21)
0.393
(1.29)
-0.00527***
(-3.40)
-0.111***
(-12.75)
-0.156***
(-5.91)
0.0898
(0.72)
-0.368***
(-15.05)
-0.386***
(-2.85)
-0.133
(-1.20)
0.320*
(1.94)
0.144
(0.75)
0.232
(1.29)
-0.328***
(-4.89)
-0.781***
(-4.42)
-0.0730***
(-2.70)
-0.121***
(-4.06)
-0.0969***
(-4.01)
-0.0714
(-1.44)
0.00912***
(3.95)
0.101***
(8.69)
-0.0401***
(-3.13)
1.009***
(3.51)
-0.00303**
(-2.35)
-0.141***
(-17.14)
0.376**
(2.19)
-0.427***
(-5.51)
-0.823***
(-4.26)
-0.0952***
(-3.24)
-0.149***
(-4.77)
-0.100***
(-3.98)
-0.158***
(-2.93)
0.0137***
(4.95)
0.0853***
(6.80)
-0.0388***
(-3.11)
0.666**
(2.50)
-0.00804***
(-4.67)
-0.115***
(-12.39)
-0.113***
(-4.26)
0.267**
(2.09)
-0.328***
(-13.48)
-0.496***
(-3.42)
0.0265
(0.24)
0.330**
(2.00)
0.297
(1.52)
0.163
(0.92)
0.0315
(0.18)
-0.397***
(-5.83)
-1.058***
(-5.98)
-0.0527*
(-1.94)
-0.0667**
(-2.24)
-0.0689***
(-2.84)
0.0575
(1.18)
0.0109***
(4.42)
0.0941***
(8.22)
-0.0529***
(-4.00)
0.779***
(2.78)
-0.00431***
(-3.18)
-0.110***
(-13.13)
-0.194***
(-7.66)
0.0995
(0.79)
-0.333***
(-14.16)
-0.145
(-1.05)
-0.157
(-1.43)
0.267
(1.63)
0.314*
(1.68)
-0.27
(-1.52)
70622
70622
70622
70622
70622
70622
Intercept
0.182
(0.97)
-0.0577
(-0.85)
-0.625***
(-3.43)
-0.0461
(-1.63)
-0.0325
(-1.09)
-0.0437*
(-1.76)
-0.355***
(-6.71)
0.0243***
(5.94)
0.0624***
(4.50)
-0.0786***
(-5.42)
0.311
(1.39)
-0.0153***
(-6.43)
-0.0840***
(-8.62)
-0.0118
(-0.45)
0.0381
(0.31)
-0.139***
(-5.55)
-0.238
(-1.59)
-0.0051
(-0.05)
0.261
(1.64)
-0.492**
(-2.41)
0.132
(0.69)
N
70622
70622
ASSET_SALE
EARNINGS
SHR_ISSUE
MERGER
REPO
SPLIT
***, **, * represent statistical significance at the 1%, 5%, and 10% levels, respectively.
17
Table 5
Table 5 contains the results from estimating the model below using logit on the “Up Event” (i.e., a raw return of at least +10% in a
single trading day). The dependent variable, CAR#P takes on the value of 1 if the cumulative abnormal return obtained in the # day
post-event window is positive and 0 otherwise. The independent variables are defined in Section III.
CAR#Pi = α + β1(RUNUPi) + β2(RET0i) + β3(VOL_RUNUP_Ai) + β4(VOL_RUNUP_Bi) + β5(VOL_RUNUP_Ci) + β6(52_WK_HIi) + β7(52_WK_LOi) +
β8(LN_MKTVALi) + β9(MOMi) + β8(VIXi) + β11(Qi) + β12(LN_VOLi) + β13(ANLYSTi) + β14(ASSET_SALEi) + β15(EARNINGSi)+ β16(SHR_ISSUEi) +
β17(MERGERi) + β18(REPOi) + β19(SPLITi) + εi
CAR20P
RUNUP
CAR20P
-0.0887
(-1.46)
RET0
-0.405***
(-4.98)
VOL_RUNUP_A 0.00949
(0.46)
VOL_RUNUP_B
0.0306
(1.29)
VOL_RUNUP_C -0.0682***
(-3.72)
52_WK_HI
0.0780*
(1.95)
52_WK_LO
0.0143***
(4.80)
LN_MKTVAL
-0.0379***
(-3.09)
MOM
-0.0667***
(-5.37)
VIX
0.129
(0.63)
Q
-0.0153***
(-7.26)
LN_VOL
0.0735***
(9.90)
ANLYST
ASSET_SALE
EARNINGS
SHR_ISSUE
MERGER
REPO
SPLIT
Intercept
N
-0.0555
(-0.93)
-0.472***
(-5.47)
0.0153
(0.74)
0.0136
(0.57)
-0.0821***
(-4.47)
-0.00198
(-0.05)
0.0126***
(4.47)
-0.0428***
(-3.44)
-0.0600***
(-4.92)
0.0688
(0.33)
-0.0134***
(-6.64)
0.0586***
(8.02)
0.151***
(6.82)
0.122
(1.48)
0.272***
(12.40)
0.266*
(1.76)
0.120***
(2.87)
0.188**
(2.07)
-0.264*
(-1.78)
-0.435***
-0.146
(-2.64)
(-0.85)
131001
131001
CAR5P
CAR5P
CAR3P
CAR3P
CAR1P
-0.455***
(-6.57)
-0.658***
(-7.52)
-0.0267
(-1.27)
-0.0273
(-1.04)
-0.0349*
(-1.91)
-0.00226
(-0.06)
0.00801***
(3.71)
-0.0662***
(-6.53)
-0.0372***
(-3.56)
0.136
(0.65)
-0.00569***
(-4.20)
0.0949***
(13.86)
-0.491***
(-7.29)
-0.772***
(-8.16)
-0.0221
(-1.03)
-0.0528**
(-2.12)
-0.0433**
(-2.39)
-0.0315
(-0.85)
0.00346**
(2.08)
-0.0786***
(-7.72)
-0.0397***
(-4.10)
0.293
(1.50)
-0.00310**
(-2.52)
0.0926***
(12.81)
0.156
(1.10)
-0.453***
(-6.78)
-0.903***
(-8.88)
-0.0181
(-0.85)
-0.0738***
(-2.97)
-0.0592***
(-3.26)
-0.124***
(-3.32)
0.00205
(1.30)
-0.0832***
(-8.14)
-0.0316***
(-3.33)
0.224
(1.17)
-0.00173
(-1.46)
0.0770***
(10.74)
0.136***
(6.19)
0.0825
(1.01)
0.281***
(13.12)
0.0202
(0.13)
0.264***
(6.13)
0.127
(1.37)
-0.018
(-0.13)
0.464***
(3.17)
-0.485***
(-7.01)
-0.889***
(-9.95)
0.00749
(0.35)
-0.0725***
(-2.78)
-0.0883***
(-5.00)
-0.0806**
(-2.14)
-0.000354
(-0.25)
-0.108***
(-11.45)
-0.0168*
(-1.69)
0.480**
(2.45)
-0.000147
(-0.14)
0.0971***
(14.21)
-0.13
(-0.92)
-0.421***
(-6.12)
-0.759***
(-8.12)
-0.0223
(-1.06)
-0.0465*
(-1.76)
-0.0499***
(-2.73)
-0.0872**
(-2.21)
0.00650***
(3.21)
-0.0704***
(-6.92)
-0.0302***
(-2.94)
0.0733
(0.35)
-0.00431***
(-3.34)
0.0804***
(11.78)
0.118***
(5.71)
0.141*
(1.73)
0.277***
(13.00)
0.132
(0.89)
0.201***
(4.76)
0.0916
(0.98)
-0.13
(-0.90)
0.149
(1.02)
131001
131001
131001
131001
131001
***, **, * represent statistical significance at the 1%, 5%, and 10% levels, respectively.
18
CAR1P
-0.447***
(-6.51)
-1.031***
(-10.66)
0.0113
(0.52)
-0.0939***
(-3.62)
-0.105***
(-5.95)
-0.175***
(-4.66)
-0.00163
(-1.12)
-0.112***
(-11.92)
-0.00865
(-0.88)
0.411**
(2.12)
0.00107
(1.03)
0.0824***
(12.15)
0.113***
(5.02)
0.198**
(2.44)
0.290***
(13.67)
-0.0753
(-0.51)
0.289***
(6.71)
0.0686
(0.74)
0.250*
(1.81)
0.764*** 1.066***
(6.06)
(8.04)
131001
Table 6
Table 6 contains the results from estimating the model below using OLS on the “Down Event” (i.e., a raw return of -10% or less in a
single trading day). The dependent variable, CAR#, represents the cumulative abnormal return obtained in the # day post-event
window using the equal-weighted market model. The independent variables are defined in Section III.
CAR#i = α + β1(RUNUPi) + β2(RET0i) + β3(VOL_RUNUP_Ai) + β4(VOL_RUNUP_Bi) + β5(VOL_RUNUP_Ci) + β6(52_WK_HIi) + β7(52_WK_LOi) +
β8(LN_MKTVALi) + β9(MOMi) + β8(VIXi) + β11(Qi) + β12(LN_VOLi) + β13(ANLYSTi) + β14(ASSET_SALEi) + β15(EARNINGSi)+ β16(SHR_ISSUEi) +
β17(MERGERi) + β18(REPOi) + β19(SPLITi) + εi
RUNUP
CAR20
CAR20
CAR5
0.00181
(0.18)
-0.0958***
(-3.63)
-0.00344
(-1.03)
-0.00914***
(-2.77)
-0.00826***
(-2.59)
-0.0302***
(-3.64)
0.00274***
(5.94)
0.00584***
(2.93)
-0.0149***
(-5.73)
0.0264
(0.97)
-0.00208***
(-7.94)
-0.00951***
(-7.56)
-0.0211***
(-3.28)
-0.0822***
(-4.42)
-0.00670***
(-3.26)
-0.0132***
(-6.30)
-0.00943***
(-5.25)
-0.0219***
(-4.56)
0.000712***
(3.83)
0.00469***
(4.61)
-0.00187
(-1.42)
0.0455***
(2.66)
-0.000451***
(-3.71)
-0.00800***
(-11.28)
70607
1.35%
Intercept
0.0221
(0.84)
0.0000254
(0.00)
-0.100***
(-3.71)
-0.00286
(-0.85)
-0.00761**
(-2.29)
-0.00750**
(-2.33)
-0.0267***
(-3.29)
0.00276***
(5.94)
0.00553***
(2.81)
-0.0152***
(-5.83)
0.0205
(0.76)
-0.00210***
(-8.01)
-0.00872***
(-7.03)
-0.00269
(-0.96)
0.00379
(0.35)
-0.0110***
(-4.17)
-0.0235*
(-1.86)
0.0148
(1.19)
0.0533***
(4.03)
-0.0196
(-0.78)
0.0171
(0.64)
N
R-square
70607
2.17%
70607
2.21%
RET0
VOL_RUNUP_A
VOL_RUNUP_B
VOL_RUNUP_C
52_WK_HI
52_WK_LO
LN_MKTVAL
MOM
VIX
Q
LN_VOL
ANLYST
ASSET_SALE
EARNINGS
SHR_ISSUE
MERGER
REPO
SPLIT
0.0329**
(2.37)
CAR5
CAR3
CAR3
CAR1
-0.0247***
-0.0202***
-0.0235***
-0.0161***
(-3.81)
(-2.97)
(-3.44)
(-3.35)
-0.0965***
-0.0800***
-0.0928***
-0.0671***
(-5.07)
(-4.81)
(-5.47)
(-5.79)
-0.00568*** -0.00582*** -0.00486**
-0.00222*
(-2.74)
(-2.97)
(-2.47)
(-1.65)
-0.0104***
-0.0121*** -0.00949*** -0.00432***
(-4.91)
(-6.23)
(-4.86)
(-3.14)
-0.00795*** -0.00898*** -0.00759*** -0.00401***
(-4.39)
(-5.86)
(-4.93)
(-4.28)
-0.0153***
-0.0153***
-0.00913**
-0.00454**
(-3.26)
(-3.66)
(-2.25)
(-1.99)
0.000779*** 0.000567*** 0.000628*** 0.000305***
(4.11)
(3.99)
(4.32)
(3.25)
0.00427***
0.00345*** 0.00305***
0.00349***
(4.27)
(4.12)
(3.70)
(6.84)
-0.00253*
-0.000759
-0.00136
-0.00113*
(-1.90)
(-0.70)
(-1.26)
(-1.70)
0.0332**
0.0331*
0.0218
0.0343***
(1.97)
(1.85)
(1.24)
(2.62)
-0.000518*** -0.000262** -0.000323*** -0.000113*
(-4.22)
(-2.47)
(-3.03)
(-1.92)
-0.00634*** -0.00659*** -0.00506*** -0.00581***
(-9.16)
(-11.89)
(-9.34)
(-15.11)
-0.0105***
-0.00927***
(-6.28)
(-6.48)
0.00862
0.00464
(1.35)
(0.85)
-0.0177***
-0.0169***
(-12.00)
(-13.63)
-0.0168**
-0.0150***
(-2.33)
(-2.58)
0.00872
0.00819
(1.30)
(1.36)
0.0235**
0.0272***
(2.40)
(3.19)
0.0207
0.0127
(1.18)
(0.96)
0.0174
0.0332***
0.0193
0.0106
(1.23)
(2.78)
(1.57)
(1.41)
70607
1.68%
70607
1.26%
***, **, * represent statistical significance at the 1%, 5%, and 10% levels, respectively.
19
70607
1.67%
70607
1.51%
CAR1
-0.0182***
(-3.77)
-0.0757***
(-6.44)
-0.00161
(-1.20)
-0.00263*
(-1.91)
-0.00312***
(-3.34)
-0.000593
(-0.27)
0.000344***
(3.61)
0.00325***
(6.51)
-0.00152**
(-2.29)
0.0270**
(2.11)
-0.000153**
(-2.57)
-0.00482***
(-12.82)
-0.00632***
(-6.72)
0.00516
(1.34)
-0.0106***
(-12.47)
-0.00718
(-1.55)
0.00403
(0.80)
0.0146**
(2.28)
0.00594
(0.68)
0.00139
(0.18)
70607
1.86%
Table 7
Table 7 contains the results from estimating the model below using OLS on the “Up Event” (i.e., a raw return of at10% or more in a
single trading day). The dependent variable, CAR#, represents the cumulative abnormal return obtained in the # day post-event
window using the equal-weighted market model. The independent variables are defined in Section III.
CAR#i = α + β1(RUNUPi) + β2(RET0i) + β3(VOL_RUNUP_Ai) + β4(VOL_RUNUP_Bi) + β5(VOL_RUNUP_Ci) + β6(52_WK_HIi) + β7(52_WK_LOi) +
β8(LN_MKTVALi) + β9(MOMi) + β8(VIXi) + β11(Qi) + β12(LN_VOLi) + β13(ANLYSTi) + β14(ASSET_SALEi) + β15(EARNINGSi)+ β16(SHR_ISSUEi) +
β17(MERGERi) + β18(REPOi) + β19(SPLITi) + εi
CAR20
RUNUP
-0.0220**
(-2.27)
RET0
-0.0835***
(-5.97)
VOL_RUNUP_A 0.00460*
(1.85)
VOL_RUNUP_B 0.00574**
(2.07)
VOL_RUNUP_C -0.00420**
(-2.00)
52_WK_HI
0.0570***
(9.39)
52_WK_LO
0.00284***
(7.17)
LN_MKTVAL
-0.00736***
(-4.89)
MOM
-0.0147***
(-6.92)
VIX
-0.00494
(-0.20)
Q
-0.00230***
(-10.45)
LN_VOL
0.00763***
(8.76)
ANLYST
CAR20
CAR5
CAR5
CAR3
CAR3
CAR1
-0.0489***
(-7.28)
-0.0773***
(-7.26)
0.00147
(0.96)
-0.000581
(-0.31)
-0.00182
(-1.58)
0.0216***
(6.74)
0.000736***
(5.89)
-0.00673***
(-9.76)
-0.00473***
(-4.43)
0.00132
(0.09)
-0.000492***
(-5.37)
0.00630***
(13.37)
-0.0483***
(-8.40)
-0.0734***
(-7.17)
0.00223*
(1.72)
-0.00112
(-0.76)
-0.00185**
(-2.05)
0.0122***
(5.08)
0.000175*
(1.78)
-0.00562***
(-9.52)
-0.00373***
(-5.10)
0.00566
(0.46)
-0.000209***
(-2.88)
0.00491***
(11.33)
0.0390***
(5.02)
-0.0466***
(-8.19)
-0.0788***
(-7.37)
0.00235*
(1.82)
-0.00198
(-1.34)
-0.00248***
(-2.76)
0.00835***
(3.44)
0.000118
(1.20)
-0.00575***
(-9.77)
-0.00337***
(-4.63)
0.00276
(0.23)
-0.000155**
(-2.14)
0.00420***
(9.76)
0.00621***
(6.31)
0.000929
(0.28)
0.0102***
(9.93)
-0.000232
(-0.05)
0.0159***
(8.08)
0.00855**
(2.55)
-0.00446
(-0.59)
0.0514***
(6.41)
-0.0256***
(-6.21)
-0.0473***
(-5.26)
0.00199**
(2.25)
-0.00332***
(-2.82)
-0.00328***
(-5.51)
0.00275
(1.61)
-0.000146**
(-1.97)
-0.00510***
(-11.92)
-0.00121*
(-1.88)
0.00337
(0.35)
0.0000294
(0.63)
0.00405***
(13.93)
0.0352***
(3.80)
-0.0470***
(-7.06)
-0.0829***
(-7.47)
0.00166
(1.09)
-0.0016
(-0.86)
-0.00259**
(-2.27)
0.0170***
(5.30)
0.000670***
(5.43)
-0.00690***
(-10.03)
-0.00431***
(-4.07)
-0.00206
(-0.13)
-0.000427***
(-4.72)
0.00548***
(11.58)
0.00715***
(5.91)
0.000957
(0.25)
0.0132***
(10.94)
0.000191
(0.03)
0.0157***
(7.20)
0.0114***
(2.73)
-0.00685
(-0.79)
0.0499***
(5.21)
130989
1.16%
130989
1.35%
130989
1.15%
130989
1.34%
130982
1.05%
Intercept
-0.00156
(-0.08)
-0.0183*
(-1.91)
-0.0912***
(-6.27)
0.00509**
(2.05)
0.00387
(1.40)
-0.00564***
(-2.69)
0.0489***
(8.03)
0.00273***
(7.02)
-0.00780***
(-5.13)
-0.0140***
(-6.64)
-0.011
(-0.44)
-0.00216***
(-10.00)
0.00596***
(6.87)
0.0187***
(8.42)
-0.00164
(-0.22)
0.0258***
(11.89)
0.0164
(1.36)
0.0188***
(5.24)
0.0189***
(2.90)
-0.0399***
(-2.58)
0.0288
(1.37)
N
R-square
130989
1.77%
130989
2.00%
ASSET_SALE
EARNINGS
SHR_ISSUE
MERGER
REPO
SPLIT
***, **, * represent statistical significance at the 1%, 5%, and 10% levels, respectively.
20
CAR1
-0.0247***
(-6.04)
-0.0501***
(-5.35)
0.00206**
(2.34)
-0.00381***
(-3.22)
-0.00365***
(-6.17)
0.000556
(0.32)
-0.000178**
(-2.34)
-0.00519***
(-12.18)
-0.00101
(-1.56)
0.00173
(0.18)
0.0000588
(1.24)
0.00367***
(12.92)
-0.00632***
(-6.72)
0.00504**
(2.13)
0.00615***
(7.79)
-0.00774*
(-1.84)
0.00797***
(4.62)
0.00447*
(1.78)
0.00376
(0.82)
0.0492*** 0.0563***
(8.85)
(9.53)
130982
1.18%
References
Amini, Shima, Bartosz Gebka, Robert Hudson, and Kevin Keasey. 2013. "A Review of the International Literature on the
Short Term Predictability of Stock Prices Conditional on Large Prior Price Changes: Microstructure, Behavioral and
Risk Related Explanations." International Review of Financial Analysis 26 (0): 1-17.
Asquith, Paul and David W. Mullins Jr. 1986. "Equity Issues and Offering Dilution." Journal of Financial Economics 15
(1–2): 61-89.
Bremer, Marc and Richard J. Sweeney. 1991. "The Reversal of Large Stock-Price Decreases." The Journal of Finance 46
(2): 747-754.
Brown, Keith C., W. V. Harlow, and Seha M. Tinic. 1988. "Risk Aversion, Uncertain Information, and Market
Efficiency." Journal of Financial Economics 22 (2): 355-385.
Burghof, Hans-peter and Felix Prothmann. 2011. "The 52-Week High Strategy and Information Uncertainty." Financial
Markets and Portfolio Management 25 (4): 345-378.
Carhart, Mark M. 1997. "On Persistence in Mutual Fund Performance." The Journal of Finance 52 (1): 57-82.
Chan, Wesley S. 2003. "Stock Price Reaction to News and no-News: Drift and Reversal After Headlines." Journal of
Financial Economics 70 (2): 223-260.
Cox, Don R. and David R. Peterson. 1994. "Stock Returns Following Large One-Day Declines: Evidence on Short-Term
Reversals and Longer-Term Performance." Journal of Finance 49 (1): 255-267.
Fama, Eugene F. and Kenneth R. French. 1993. "Common Risk Factors in the Returns on Stocks and Bonds." Journal of
Financial Economics 33 (1): 3-56.
George, Thomas J. and Chuan-Yang Hwang. 2004. "The 52-Week High and Momentum Investing." The Journal of
Finance 59 (5): 2145-2176.
Griffin, Dale and Amos Tversky. 1992. "The Weighing of Evidence and the Determinants of Confidence." Cognitive
Psychology 24 (3): 411-435.
Harrison Hong and Jeremy C. Stein. 2007. "Disagreement and the Stock Market." The Journal of Economic Perspectives
21 (2): 109-128.
Jegadeesh, Narasimhan and Sheridan Titman. 1993. "Returns to Buying Winners and Selling Losers: Implications for
Stock Market Efficiency." The Journal of Finance 48 (1): 65-91.
Kahneman, D., & Tversky, A. (1982). Judgments of and by representativeness. In D. Kahneman, P. Slovic & A. Tversky
(Eds.), Judgment under uncertainty: Heuristics and biases(pp. 84-98). New York: Cambridge University Press.
Larson, Stephen J. and Jeff Madura. 2003. "What Drives Stock Price Behavior Following Extreme One-Day Returns."
Journal of Financial Research 26 (1): 113-127.
Li, Jun and Jianfeng Yu. 2012. "Investor Attention, Psychological Anchors, and Stock Return Predictability." Journal of
Financial Economics 104 (2): 401-419.
Park, Jinwoo. 1995. "A Market Microstructure Explanation for Predictable Variations in Stock Returns Following Large
Price Changes." The Journal of Financial and Quantitative Analysis 30 (2): 241-256.
21
Pritamani, Mahesh and Vijay Singal. 2001. "Return Predictability Following Large Price Changes and Information
Releases." Journal of Banking & Finance 25 (4): 631-656.
Rogers, W.H. 1993 “Regression Standard Errors in Clustered Samples.” Stata Technical Bulletin 13, 88-94.
Savor, Pavel G. 2012. "Stock Returns After Major Price Shocks: The Impact of Information." Journal of Financial
Economics 106 (3): 635-659.
Shleifer, Andrei. 1986. "Do Demand Curves for Stocks Slope Down?" The Journal of Finance 41 (3, Papers and
Proceedings of the Forty-Fourth Annual Meeting of the America Finance Association, New York, New York,
December 28-30, 1985): 579-590.
Sturm, Ray R. 2008. "The 52-Week High Strategy: Momentum and Overreaction in Large Firm Stocks." , Summer;
2013/4, 55+.
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