Journal of Behavioral and Experimental Finance 29 (2021) 100451
Contents lists available at ScienceDirect
Journal of Behavioral and Experimental Finance
journal homepage: www.elsevier.com/locate/jbef
Does investor attention matter for market anomalies?✩
∗
Hung T. Nguyen a , , Mia Hang Pham b
a
b
School of Economics and Finance, Massey University, New Zealand
Massey Business School, Massey University, New Zealand
article
info
Article history:
Received 16 May 2020
Accepted 17 December 2020
Available online 29 December 2020
JEL classification:
D91
G11
G12
G41
a b s t r a c t
This paper examines the relation between investor attention and stock market anomalies in the US
stock market. We find anomalies are stronger following high rather than low attention periods. Returns
on the long–short strategy based on a composite mispricing score during high attention months are
2.25 times higher than those during low attention periods. The results are consistent with the notion
that high levels of attention can exacerbate investor overreaction to irrelevant information. Mispricing
is then corrected, leading to increased anomaly returns following high attention periods.
© 2020 Elsevier B.V. All rights reserved.
Keywords:
Investor attention
Market anomalies
Mispricing
1. Introduction
Attention is a limited cognitive resource (Kahneman, 1973),
and it serves as a major factor in determining security selection
and investment decisions (Peng and Xiong, 2006; Barber and
Odean, 2008). While there has been a sharp focus on the impact of attention on market reactions to accounting information,
corporate behavior and outcomes, managerial incentives, market
liquidity, and investment performance,1 less is known about the
impact of attention on the efficiency of financial markets. This
paper examines whether, and to what extent, investor attention
influences financial market anomalies.
Prior literature provides mixed predictions regarding the impact of investor attention on stock market anomalies. On the one
hand, classical asset pricing models imply that the attention–
anomaly relation would be irrelevant, as investors are assumed
to be fully rational and the attention effect, if any, would be
eliminated by rational decisions attempting to exploit mispricinginduced arbitrage opportunities (e.g., Sharpe, 1964). On the other
✩ Acknowledgments: We appreciate helpful comments from Philip Gray and
Cameron Truong. Harvey Nguyen acknowledges financial supports from the
Accounting and Finance Association of Australia and New Zealand (AFAANZ)
Research Grant and Massey University Strategic Research Excellent Fund. All
errors are our own.
∗ Correspondence to: School of Economics and Finance, Massey
University, North Shore, Auckland 0632, New Zealand.
E-mail addresses: h.nguyen3@massey.ac.nz (H.T. Nguyen),
m.h.pham@massey.ac.nz (M.H. Pham).
1 See Barber and Odean (2013) and Gabaix (2019) for a review of the
literature.
https://doi.org/10.1016/j.jbef.2020.100451
2214-6350/© 2020 Elsevier B.V. All rights reserved.
hand, a growing strand of behavioral finance literature suggests that traditional rational frameworks should incorporate
psychologically-related factors to improve our understanding of
asset pricing (e.g., Hirshleifer, 2001; Barberis and Thaler, 2003;
Branch, 2014). Given human beings have bounded rationality and
limited cognitive resources to process information (Barber and
Odean, 2008), it is possible that investor attention matters for the
efficiency of financial markets.
The relation between investor attention and market anomalies
is intuitive for two reasons. First, given a limited amount of attention to devote to investment decisions, attention-constrained
investors may miss important information, leading to delayed information processing and underreaction to relevant information
(Peng and Xiong, 2006; Hirshleifer et al., 2009). Second, high levels of attention can exacerbate investor overreaction to irrelevant
information or their private information, thus generating more
pronounced anomaly returns (Hou et al., 2009; Barber and Odean,
2013).
The United States is a natural setting for our study given a
large body of documented anomalies in economic and finance
literature (e.g., McLean and Pontiff, 2016; Hou et al., 2020). We
study a set of 11 prominent anomalies in the US stock market
and a composite mispricing measure constructed based on these
anomalies. We find that long–short returns of anomalies are
stronger following high rather than low attention periods. Specifically, nine of the anomalies produce positive and significant
long–short returns following high attention periods, whereas,
only two of the anomalies generate significant long–short returns
following low attention months. Furthermore, long–short returns
H.T. Nguyen and M.H. Pham
Journal of Behavioral and Experimental Finance 29 (2021) 100451
Table 1
Investor attention and anomaly returns.
Anomaly
Mispricing score
Net stock issues
Composite equity issues
Accruals
Net operating assets
Asset growth
Investment-to-assets
Distress
O-score
Momentum
Gross profitability premium
Return on assets
Whole sample
High attention periods
Low attention periods
Long
(1)
Short
(2)
Long–Short
(3)
Long
(4)
Short
(5)
Long–Short
(6)
Long
(7)
Short
(8)
Long–Short
(9)
0.651
(2.16)
1.152
(5.66)
1.200
(5.89)
1.042
(3.50)
1.220
(4.53)
1.172
(4.54)
1.171
(4.92)
1.121
(4.84)
1.015
(4.02)
1.348
(4.39)
1.238
(5.63)
1.159
(4.94)
−0.240
(−0.61)
0.891***
(4.00)
0.553***
(3.57)
0.478***
(2.62)
0.284*
(1.90)
0.704***
(4.60)
0.416**
(2.27)
0.431***
(2.78)
0.674**
(2.09)
0.190
(1.07)
0.992***
(2.92)
0.282
(1.45)
0.609***
(2.71)
0.473
−0.836
0.784
0.203
1.400
0.620
0.905
0.578
1.363
0.708
1.037
0.736
1.087
0.740
0.998
0.776
1.468
0.527
0.974
0.505
1.333
0.750
1.010
0.761
1.312
0.747
1.031
0.733
1.414
0.431
0.829
0.462
1.248
0.825
0.783
0.825
1.730
0.139
0.968
0.573
1.414
1.191
1.063
0.723
1.390
0.499
1.309***
(4.24)
0.780***
(3.34)
0.655**
(2.10)
0.348
(1.58)
0.941***
(3.82)
0.584*
(1.95)
0.565**
(2.41)
0.983**
(2.33)
0.424*
(1.74)
1.591***
(3.49)
0.223
(0.84)
0.891***
(2.64)
0.929
0.601
0.581**
(2.05)
0.327*
(1.83)
0.301
(1.60)
0.222
(1.22)
0.469***
(2.85)
0.249
(1.24)
0.298
(1.55)
0.366
(0.81)
−0.042
(−0.17)
0.395
(0.88)
0.341
(1.40)
0.328
(1.01)
0.599
(2.09)
0.722
(2.51)
0.758
(2.75)
0.516
(1.83)
0.756
(2.54)
0.740
(2.57)
0.447
(0.99)
0.825
(2.57)
0.356
(0.91)
0.956
(3.07)
0.550
(1.53)
The table reports the value-weighted excess returns for 11 anomalies and mispricing score based on the combination of all anomalies following high and low
attention months. Decile portfolios are formed every month for the January 1980 to December 2016 period by sorting stocks based on each of the anomaly variables
over the past one month. Portfolio 1 (10) is the portfolio of stocks associated with the highest (lowest) average returns over the past month, as reported in the
literature. Long, short, long–short returns are reported in percentage. High (low) attention periods are defined based on the sample median of the aggregate investor
attention index from Chen et al. (2019). The sample period is from January 1980 to December 2016. Newey and West (1987) adjusted t-statistics are in parentheses.
*, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
of the composite mispricing score are more pronounced following
high rather than low attention periods. The results are consistent with the conjecture that too much attention allocated to
irrelevant information triggers investor overreaction to information. Once the mispricing is corrected, more anomaly returns are
realized following high attention periods.
Our paper adds two contributions to the literature. First, this
study extends to a growing literature that shows the impacts of
investor attention on various cognitive-demanding activities such
as stock picking or investment decisions. We find that levels of
investor attention are associated with the degree of mispricing.
Second, our paper contributes to a recent strand of studies documenting drivers of capital market anomalies (e.g., Stambaugh
et al., 2012; McLean and Pontiff, 2016).
The paper proceeds as follows. Section 2 describes our data
and sample. Section 3 discusses the main findings. Section 4
provides further analyses. Section 5 concludes the paper.
individual attentional proxies commonly employed in the literature.3 Our sample covers the period from January 1980 (when
attention measure becomes available) to December 2016.
3. Main findings
3.1. Univariate portfolio analysis
We provide a detailed description of each anomaly in Appendix. For each of the 11 anomalies and the composite mispricing score, all stocks are sorted into decile portfolios every month,
where decile 10 (decile 1) is assigned to stocks associated with
the lowest (highest) average return, as discussed in the literature.
We examine the return differences between the top and bottom
deciles of each anomaly and report the results in Columns (1) to
(3) in Table 1.
We study anomaly returns following high and low investor
attention periods. A high (low) investor attention month is one
in which the value of the aggregate investor attention is above
(below) the median value for the sample periods. Columns (4) to
(9) in Table 1 report long, short, and long–short value-weighted
returns for each anomaly and a composite mispricing score following high and low attention periods.
The results presented in Table 1 suggest that long–short returns of anomalies are stronger following high rather than low
attention periods. Specifically, nine of the 11 anomalies produce
positive and statistically significant long–short returns following
high attention periods, whereas, only two of the 11 anomalies
2. Data and methodology
We study a comprehensive set of 11 market anomalies as in
Stambaugh et al. (2012, 2014, 2015). For each of the anomalies, we obtain value-weighted portfolio returns for the top and
bottom deciles of the anomaly’s sorting variable returns from
Stambaugh and Yuan (2017).2 The composite mispricing measure
of a stock is constructed by combining its rankings on the 11
anomalies’ variables. We source market excess returns and risk
factor returns are from Kenneth French’s data library. We obtain
an aggregate investor attention measure from Chen et al. (2019)
who construct the market-wide attention measure based on 12
3 The twelve attention measures include abnormal trading volume, extreme
returns, analyst coverage, past returns, advertising expenses, fund inflow, media
coverage, Google search volume, nearness to the Dow 52-week high and
historical high, and EDGAR search (Chen et al., 2019). We thank Goufu Zhou
for making the data publicly available on his website: http://apps.olin.wustl.
edu/faculty/zhou/.
2 We thank Robert Stambaugh for making the data available at http://finance.
wharton.upenn.edu/~stambaug/.
2
H.T. Nguyen and M.H. Pham
Journal of Behavioral and Experimental Finance 29 (2021) 100451
Table 2
Regression analysis.
Anomaly
Intercept
Mispricing score
Net stock issues
Composite equity issues
Accruals
Net operating assets
Asset growth
Investment-to-assets
Distress
O-score
Momentum
Gross profitability premium
Return on assets
R2 (%)
ATTENTION
coeff.
t-stat
coeff.
t-stat
0.892***
0.553***
0.478***
0.284*
0.705***
0.416**
0.431***
0.674
0.190
0.992***
0.281
0.609***
(4.15)
(3.73)
(2.75)
(1.90)
(4.84)
(2.32)
(2.79)
(0.67)
(1.10)
(2.97)
(1.46)
(2.89)
0.576**
0.404**
0.490**
0.025
0.404**
0.314
0.305**
0.720**
0.354**
0.521
0.192
0.605***
(2.47)
(2.27)
(1.99)
(0.17)
(2.32)
(1.31)
(2.11)
(2.08)
(1.96)
(1.58)
(1.10)
(2.61)
1.53
1.81
1.87
0.01
1.65
0.64
0.82
1.00
0.80
0.37
0.03
1.86
The table reports the estimates of the following model specification: ERetP10−P1, t +1 = α + ATTENTIONt + Et +1 , where
ERetP10−P1, t +1 is the long–short value-weighted excess return of each anomaly and the composite mispricing score based
on 11 anomalies, and ATTENTIONt is lagged monthly investor attention from Chen et al. (2019). Newey and West (1987)
adjusted t-statistics are in parentheses. The sample covers the period from 1980 to 2016. *, **, and *** denote significance
at the 10%, 5%, and 1% levels, respectively.
Table 3
Control for alternative explanations.
(1)
(2)
(3)
(4)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
Constant
−0.272
(−0.02)
ATTENTION
0.556**
(2.27)
0.248
(0.09)
0.947
(3.08)
0.586**
(2.46)
1.392
(0.07)
0.575**
(2.39)
0.640
(0.67)
0.560**
(2.30)
0.831
(4.31)
0.547**
(2.29)
0.810
(3.52)
0.544**
(2.31)
0.899
(4.13)
0.582**
(2.49)
0.793
(3.05)
0.777**
(2.32)
1.893
(2.17)
0.541**
(2.29)
0.509
(0.22)
0.573**
(2.56)
222.402
(0.55)
0.888***
(2.61)
−16.486
(−0.38)
51.742
(0.51)
−31.150
(−0.30)
0.158
(0.97)
0.590
(0.40)
21.521
(0.72)
0.744
(0.46)
−0.261
(−0.63)
−41.942
(−1.27)
4.120
(0.68)
2.10
GDP per capita
Inflation
−21.425
(−0.27)
Employment
−0.098
(−0.02)
Industrial production
0.003
(0.25)
Business cycle
0.331
(0.32)
Hourly compensation
26.124
(1.03)
T-bill
0.289
(0.92)
Market sentiment
0.171
(0.46)
Market volatility
−21.518
(−1.07)
Macroeconomic uncertainty
R2 (%)
1.18
1.33
1.31
1.19
1.24
1.45
1.41
3.04
1.64
0.575
(0.16)
1.33
The table reports the estimates of the monthly regression of the value-weighted returns for a composite mispricing score based on the combination of all anomalies
on lagged attention index after controlling for various market-wide variables. The model specification is as follows: ERetP10−P1, t +1 = α + ATTENTIONt + Macrot + Et +1 ,
where ERetP10−P1, t +1 is the long–short value-weighted excess return of the composite mispricing score based on 11 anomalies, ATTENTIONt is lagged monthly
attention index from Chen et al. (2019), and Macrot refers to macroeconomic variables. Investor Sentiment is principal components of three sentiment indices
including Baker and Wurgler (2006)’s sentiment index, University of Michigan’s consumer sentiment index, and the Chicago Board Options Exchange’s VIX index.
We source macroeconomic variables from the Federal Reserve Bank of St. Louis and the US Bureau of Economic Analysis. GDP per capita is the natural logarithm of
GDP per capita; Inflation is personal consumption expenditures; Employment is the growth rate of employment level; Industrial Production is industrial production
index; Business Cycle is from NBER; Hourly Compensation is nonfarm business sector: real compensation per hour; T-bill is the yield on the 3-month Treasury bill;
Market volatility is measured using an EGARCH (1,1) model; Macroeconomic Uncertainty is the economic uncertainty index of Jurado et al. (2015). Newey and West
(1987) adjusted t-statistics are in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
generate significant long–short returns following low attention
months. Furthermore, long–short returns of the composite mispricing score constructed based on all anomalies during high
attention months are 2.25 times higher than those during low
attention periods (1.309% versus 0.581%).
lagged 1-month attention and report results for these tests in
Table 2.4
The results in Table 2 suggest that long–short value-weighted
returns of seven of the 11 anomalies are statistically and positively related to previous month investor attention. More importantly, the coefficient on the attention measure is positive and
statistically significant (coefficient of 0.576 and t-stat of 2.47)
when the aggregate mispricing score is used as a dependent variable. The attention–mispricing effect is economically significant,
with one standard deviation increase in investor attention being
3.2. Regression analysis
We further examine the relation between investor attention
and the long–short returns of anomalies and mispricing scores
using a regression framework. We regress long–short valueweighted returns of each anomaly and the mispricing score on
4 Attention index is scaled to have zero mean and unit standard deviation.
3
H.T. Nguyen and M.H. Pham
Journal of Behavioral and Experimental Finance 29 (2021) 100451
Table 4
Sensitivity analysis.
Intercept
R2 (%)
ATTENTION
coeff.
t-stat
coeff.
t-stat
0.492***
(2.80)
2.11
(1.96)
4.97
(2.24)
1.18
Panel A: Equal-weighted portfolio returns
1.378***
(8.60)
Panel B: Alphas with respect to the Fama and French (1993)’s three-factor model
1.383***
(5.70)
0.410**
Panel C: Subsample analysis, excluding financial turmoil (2007–2008)
0.800***
(3.67)
0.514**
The table reports results for sensitivity analysis for the monthly regression of the value-weighted returns for a composite mispricing
score based on the combination of all anomalies on lagged investor attention index as in Table 2. In Panel A, we use long–short
equal-weighted returns of mispricing scores as the dependent variable. In Panel B, we use alphas with respect to the Fama and
French (1993)’s three-factor model instead of excess returns as the dependent variable. In Panel C, we rerun Table 2’s regression
after excluding the financial turmoil (2007–2008). Newey and West (1987) adjusted t-statistics are in parentheses. The sample covers
the period from 1980 to 2016. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Table A.1
List of market anomalies in Stambaugh and Yuan (2017).
No.
Anomaly
Description
0
Composite
mispricing score
The composite mispricing measure of a stock is constructed by combining its
rankings on the 11 anomalies’ variables computed at the end of each month.
The mispricing measure ranges between 0 and 100 with higher (lower) value
indicates more overpriced (underpriced).
1
Net stock issues
Loughran and Ritter (1995) find a negative relation between net stock issues
and subsequent stock returns.
2
Composite equity
issues
Daniel and Titman (2006) show stock returns are negatively related to
composite equity issues.
3
Accruals
Sloan (1996) shows that firms with high accruals earn lower average returns
compared to firms with low accruals.
4
Net operating
assets
Hirshleifer et al. (2004) find net operating assets are negatively associated
with long-run stock returns.
5
Asset growth
Cooper et al. (2008) find companies that grow their total assets more earn
lower subsequent returns.
6
Investment to
assets
Titman et al. (2004) show that higher past investment predicts lower future
returns.
7
Distress
Campbell et al. (2008) show that firms with high failure probability have
lower rather than higher subsequent returns.
8
O-score
Ohlson (1980) finds firms with high bankruptcy risk earn lower than average
returns.
9
Momentum
Jegadeesh and Titman (1993) find that high (low) past recent returns forecast
high (low) future returns.
10
Gross profitability
premium
Novy-Marx (2013) finds gross profitability positively predicts returns.
11
Return on assets
Fama and French (2006) find that more profitable firms have higher expected
returns than less profitable firms.
associated with $0.0057 of additional long–short monthly profit
on a strategy with $1 in each leg of the spread.
stock returns (Baker and Wurgler, 2006; Bali et al., 2017). Our
results reported in Table 3 are consistent with previous findings.
Furthermore, we consider a set of sensitivity analysis and
find our results, reported in Table 4, hold when we (i) consider
equal-weighted returns, (ii) use alphas with respect to Fama and
French’s (1993) three-factor model, or (iii) exclude the period of
financial turmoil in 2007–2008.
4. Alternative explanations and sensitivity analysis
We control for a wide set of macroeconomic variables that
can influence both investor attention and mispricing in the stock
market to ensure that they are not the drivers of our attention–
anomaly results. We focus on the aggregate mispricing measure
as the mispricing score diversifies away noise related to individual anomalies and hence is a robust measure of mispricing
(Stambaugh et al., 2015). Specifically, we control for market sentiment, market volatility, and other macroeconomic variables,
including GDP per capita, inflation, employment, business cycle,
industrial production, compensation, and macroeconomic uncertainty, as these variables can be priced in the cross-section of
5. Conclusion
This paper finds that anomalies are generally stronger following high rather than low attention periods. long–short returns of
a composite mispricing score constructed based on all anomalies
during high attention months are more pronounced than during
low attention periods. Our study highlights the roles of investor
attention, an important cognitive resource, in understanding the
efficiency of capital markets.
4
H.T. Nguyen and M.H. Pham
Journal of Behavioral and Experimental Finance 29 (2021) 100451
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Appendix
See Table A.1.
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