1. Investor Limited Attention and Asset Prices
Zhi Da University of Notre Dame
西南财大-金融研究所
SWUFE - IFS June 2012

2. Traditional Asset Pricing Models
assume that information is incorporated into prices with lightning speed
In traditional asset pricing theory, agents are super-computers: they process all information immediately and in its entirety. In reality, agents are not super computers – they are humans who have limited attention.
I like a quote by Nobel Laureate Herbert Simon:
Understandably there have been many theoretical papers that use this fact to think about how this limited attention might affect asset prices.
The difficulty is on the empirical side:

3. Investors Attention and Finance
Attention is a scarce cognitive resource (Kahneman, 1973)
Limited-attention has important theoretical implications for the trading and pricing of financial securities
“What information consumes is rather obvious: it consumes the attention of its recipients. Hence, a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”
--- Herbert Simon
Nobel Laureate in Economics
In traditional asset pricing theory, agents are super-computers: they process all information immediately and in its entirety. In reality, agents are not super computers – they are humans who have limited attention.
I like a quote by Nobel Laureate Herbert Simon:
Understandably there have been many theoretical papers that use this fact to think about how this limited attention might affect asset prices.
The difficulty is on the empirical side:

4. Incomplete Literature Review
Theoretical
Empirical
Measurement

5. Theoretical Analysis

6. Merton (1987, JF)
Attention is costly and investors will not have perfect information on all stocks at all time
Investors include a stock to her portfolio only if she knows the stock
Thus they hold “suboptimal” portfolios

7. Merton (1987, JF) Expected return is higher when Beta is higher
Idiosyncratic volatility is higher
Firm is larger
Shareholder base is smaller

8. Hirshleifer and Teoh (2003, JAE)
There are both attentive and inattentive investors in the economy
They are both mean-variance optimizers
They each set their optimal demand function
Aggregate demand equals aggregate supply (0)  equilibrium asset prices

9. Hirshleifer and Teoh (2003, JAE)
Information structure and inattention
Inattentive investors assume public information signals to be drawn from simpler distributions
Inattentive investors have simpler rules of thumb for valuation parameters

10. Peng and Xiong (2006, JFE)
A representative investor solves two optimization problems each period:
Optimally allocate her limited attention to (1) market factor; (2) sector factors; (3) firm-specific factors
Based on the processed information, she then solves the standard consumption Bellman equation

11. Peng and Xiong (2006, JFE)
Limited attention results in categorical learning
Investors allocate more attention to market- and sector-level factors than to firm-specific factors
In severely constrained cases, the investor allocates all attention to market- and sector-level information and ignores all the firm-specific data
Limited attention could acerbate the impact of behavioral biases on asset prices

12. Empirical Evidence

13. Anecdotes Rashes (2001, JF) Huberman and Regev (2001, JF)
The publication of an article in the New York Times about a new cancer-curing drug from EntreMed attracted great public attention and generated a daily return of more than 300% in its stocks, even though the same story had already been published more than five months earlier in Nature and other newspapers.
Rashes (2001, JF)
Excessive co-movement in MCI–MCIC pair

14. Retail attention Barber and Odean (2008)
“… individual investors are more likely to buy rather than sell those stocks that catch their attention. … this is so because attention affects buying—where investors search across thousands of stocks—more than selling—where investors generally choose only from the few stocks that they own. While each investor does not buy every single stock that grabs his attention, individual investors are more likely to buy attention-grabbing stocks than to sell them. (pg 786)”
Increased retail attention  positive price pressure
Preferences determine choices after attention has determined the choice set

15. Time-varying attention constraint
Corwin and Coughenour (2008, JF)
NYSE specialists allocate effort toward their most active stocks during periods of increased activity, resulting in less frequent price improvement and increased transaction costs for their remaining assigned stocks
Hirshleifer, Lim, and Teoh (2009, JF)
Investor distraction hypothesis: More post-earnings announcement drift following days with more announcements
DellaVigna and Pollet (2009, JF)
More post-earnings announcement drift following Friday announcement

16. Limited attention to long-term information
DellaVigna and Pollet (2007, AER)
Demographic shocks today lead to predictable shifts in consumption and industry profitability in 5 to 10 years
Investors have limited attention to information beyond 5 years, resulting in return predictability
Da and Warachka (2009, JFE)
Equity analysts issue both short-term and long-term earnings forecasts
Market participants pay more attention to the short-term forecasts than to the short-term forecasts
Disparity in the forecasts predicts future return

17. Limited attention to economic links
Cohen and Frazzini (2008)
Supplier’s stock price reacts to shocks to its customer with a delay

18. Cohen and Frazzini (2008)

19. Measurement

20. Measuring investor attention
Measuring attention empirically is tricky
Trading volume: Gervais, Kaniel, and Mingelgrin (2001); Barber and Odean (2008); Hou, Peng, and Xiong (2008)
Extreme returns: Barber and Odean (2008)
Up/down markets: Hou, Peng, and Xiong (2008)
Firms’ advertising expense: Grullon, Kanatas, and Weston  (2004) and Chemmanur and Yan (2009)
The existence of news: Barber and Odean (2008)
Repeated news stories: Tetlock (2008)
Turnover and returns are noisy and “catch-all” proxies and news coverage and advertising expense capture the supply of attention or passive attention

21. Da, Engelberg, and Gao (2011, JF)
Google’s Search Volume Index (SVI)
We use a website called Google Trends which when you give a term reports a historical time series of search volume for that term back until 2004.
Take, for example, the word diet…
Point out: (1) what each point is, (2) that spikes correspond to some news events and not others and (3) attention towards dieting falls and then spikes consistent with our intuition

22. Da, Engelberg, and Gao (2011, JF)
Consider “AAPL” and “MSFT” (shown below)
We focus on innovations in SVI of each search term
Because we want to make cross-sectional comparisons, we need to choose a reference term and so we choose MSFT. Therefore, an SVI observation in our data will always be scaled by the same constant: the time-series average of MSFT search volume.
Example of what our data look like (AAPL vs. MSFT)
Point out: A few things we can say about this example: (1) fill in some details how we use MSFT as a reference point to back out the SVI we used; (2) MSFT has twice the news articles, three times the volume, twice the mkt cap but far less search – search is capturing something different; (3) Spike in AAPL SVI is due to the MacWorld Conference held in the beginning of the year.

23. Da, Engelberg, and Gao (2011)
We show that our attention measure is capturing retail attention
Intuitively it should be individual, retail investors
Given we are dealing with retail attention, we consider the Barber and Odean (2008) theory that shocks to retail attention create positive price pressure
An increase in retail attention predicts higher short-term return, especially among smaller stocks and stocks traded more by retail investors
An increase in retail attention predicts higher first-day IPO return and subsequent reversal (IPO long-run underperformance)

24. SVI and CNBC’s Mad Money
Jim Cramer makes recommendation
Engelberg, Sasseville and Williams (2008): it is mainly individual investors whose attention the show is capturing

25. ASVI and Price Pressure
Week 1 (1)
Week 2
(2)
Week 3
(3)
Week 4
(4)
Week 5-52
(5)
ASVI
18.742***
14.904**
3.850
-1.608
(7.000)
(7.561)
(6.284)
(6.903)
(17.162)
Log Market Cap * ASVI
*** **
-4.710
4.290
16.834
(6.508)
(6.768)
(6.516)
(6.398)
(88.624)
Log Market Cap
2.653
3.858
3.144
3.575
(3.023)
(3.160)
(3.063)
(3.186)
(67.405)
Percent Dash-5 Volume * ASVI
3.552**
1.904
1.687
-2.744
16.258
(1.639)
(1.522)
(1.612)
(1.717)
(23.822)
Percent Dash-5 Volume
1.607
1.351
1.486
0.364
***
(1.644)
(1.652)
(1.659)
(1.711)
(31.765)
APSVI
-2.532***
-1.379
-0.701
-0.704
2.286
(0.930)
(0.990)
(0.808)
(0.639)
(9.909)
Absolute Abnormal Return
1.314
-2.389
-1.128
-0.463
-1.510
(1.879)
(1.979)
(1.563)
(1.405)
(28.505)
Advertising Expense / Sales
-4.012*
-4.686**
-3.959*
-4.153*
***
(2.237)
(2.228)
(2.172)
(2.234)
(52.414)
Log(1 + # of analysts)
-3.747**
-4.547***
-3.961**
-4.120**
***
(1.548)
(1.741)
(1.769)
(29.683)
Log(Chunky News Last Year)
-5.157
-5.549*
-4.349
-5.409
(3.370)
(3.272)
(3.292)
(3.558)
(80.730)
Chunky News Dummy
3.610*
1.378
-3.825
-0.058
32.466
(2.025)
(2.424)
(2.483)
(1.910)
(28.441)
Abnormal Turnover
2.398**
2.309**
2.022
0.316
10.531
(1.204)
(1.144)
(1.404)
(1.098)
(10.109)
Observations per week
1499
1498
1497
1496
1414
R-Squared
0.0142
0.0119
0.0112
0.0111
0.0170

26. First-day IPO Return

27. Post-IPO Return [5w, 52w], High vs. Low ASVI

28. Conclusion Investors are likely to have limited attention
Limited investor attention affects prices both theoretically and empirically
Measuring attention is a challenging empirical task