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2019-2976-FIN

It Depends on When You Search!

Internet search has been shown to have strong predictive power for stock prices, company

sales, and the spread of influenza. Following studies that propose search frequency as a

more direct and timely measure of investor attention, we explore the heterogeneity in the

search data and address several questions in the current literature. Using the daily search

volume index (SVI) from Google Trends for the S&P 500 stocks, we distinguish Internet

searches conducted during weekends from those conducted during weekdays and show

that weekend search can better capture retail investors’ attention. First, we find that

reduced market response to Friday earnings announcements happens only when the

weekend search (a proxy for attention) is low, contributing to the ongoing debate on the

cause of this phenomenon. Second, we find that weekend search can help to predict

returns for large-cap stocks, whereas the current literature shows that weekly search can

predict returns only for smaller firms. Furthermore, we find that the weekend search

measure from the Chinese Baidu Index can predict CSI 300 stock returns. These results

are robust when we include holidays or reorder the days in a week. Finally, we explore

periods with high investor sentiment and periods with high market volatility. We find that

search during “busy” weekends is less effective as a measure of investor attention in stock

return predictability.

Keywords: Google Trends, Internet Search, Retail Investor, Stock Return, S&P

500, Weekends

Introduction

The search engine is the most commonly used product on the Internet for

individuals to directly acquire information. .The search volume generated by users

is also widely used as a valid and timely proxy for predicting sales (Geva et al.

2017), the spread of influenza (Ginsberg et al. 2009, Lazer et al. 2014), and stock

prices (Da et al. 2011) in various fields of academic research. These studies argue

that search is a revealed attention measure: if you search for something with

Google, you are undoubtedly paying attention to it. The successful utilization of

search volume in financial markets greatly benefits both industry and academia.

Search volume, unlike social media, which usually contains content (text),

ratings (scores), and posters’ information (demographics), usually includes just the

search frequency (volume indices) and a time label (time generated) (Brynjolfsson

et al. 2016). Previous studies mainly focus on weekly or monthly changes in the

indices and have not explored their heterogeneity. In particular, the time label

associated with search volume remains unexplored. It is unclear whether searches

conducted on different days of a week, such as weekdays and weekends, contain

equal amounts of information. If the answer is that the amounts of information are

2019-2976-FIN

not equal, the question is, can we improve the predictive power of search volume

by taking advantage of the time label to refine the measure?

In this paper, we argue that Internet searches under different circumstances do

not reflect equal amounts of attention. This is especially true when there is an

overabundance of information, which can lead to constrained attention paid to a

particular event (Liu and Ye 2016, Hirshleifer et al. 2009). As is often stated in the

literature on attention, a wealth of information creates a poverty of attention. We

distinguish the aggregate Internet searches conducted during weekends from those

conducted during weekdays because weekdays typically have an overload of

information in comparison to weekends. The stock markets are closed over the

weekends. During trading sessions on weekdays, investors have a limited amount

of time to digest information when prices are moving in real time. Intra-week

information seasonality is also an issue since announcements and news releases

mostly occur during weekdays. Moreover, investors’ cognitive ability and

disposition usually improve during weekends, since they have more private time

and space and are away from the pressure and distraction of the workdays, all of

which lessen the constraint on the attention allocation (Ryan et al. 2010, Geurts et

al. 2003).

To construct search indices on weekdays and weekends, we first download the

search volume from Google Trends on a daily frequency from 2004 to 2012. For

each company in the S&P 500 index, we aggregate the daily data from each week

into weekday searches and weekend searches.

This process creates two weekly

series for each stock. Following Da et al. (2011), we compute the abnormal SVI

(ASVI) by subtracting the lagged log (SVI) from the current week log (SVI).

ASVI takes into account the week-to-week variation in the total SVI. We compute

the weekday ASVI, denoted by ASVI

1-5,

and the weekend ASVI, denoted by

ASVI

separately. Intuitively, ASVI

1-5

and ASVI

reflect the percentage change

of SVI over previous weeks. The contemporaneous correlation between ASVI

1-5

and ASVI

is 0.406 in our sample. Since our interest is in the difference between

the weekday and weekend search, we subtract ASVI

1-5

from ASVI

to compute

the incremental component, denoted by ASVI

. We find that in our sample, the

contemporaneous correlation between ASVI

and the total ASVI measure by Da et

al. (2011) is rather low at 0.02, which indicates that the information content in the

incremental component is very different from that in the ASVI measure in Da et al.

(2011).

With our constructed series of weekday and weekend search, we first study a

puzzling question surrounding Friday earnings announcements in Dellavigna and

We focus on the S&P 500 mainly because of the limited availability of the daily SVI. Except for

large stocks, the daily SVI is missing for many small stocks. In fact, Cziraki et al. (2017) find no

evidence of an empirical relation between SVI and future abnormal stock returns when using S&P

500 stocks. However, after differentiating stock search volume in weekends from weekdays, the

predictive power of ASVI in our study sharply increases and becomes significant among S&P 500

stocks.

2019-2976-FIN

Pollet (2009), which shows investors react less to Friday announcements than

announcements on other weekdays. It is unclear whether this reaction is due to

investors’ inattention during weekends or the fact that Friday earnings

announcements that elicit weak responses often occur on Fridays. Our strategy is

to investigate whether the immediate market responses are correlated with

weekend search activity. To the extent that weekend search is a good proxy for

retail investor attention, we expect that the response is stronger with more positive

weekend ASVI and weaker with more negative weekend ASVI. The results of our

analysis indeed suggest that the Friday earnings announcements effect is stronger

(weaker) during weekends with low (high) ASVI.

Our second exercise is to explore the stock return predictability of weekend

search. Da et al. (2011) find that a higher ASVI (using total search volume in a

week to calculate the abnormal search volume index) leads to higher returns in the

following weeks. Their sample is based on the Russell 3000, and the predictability

exists mostly for small-cap stocks. We explore whether distinguishing search time

can help to improve the predictability for large-cap stocks. The firms in our sample

are among the largest in the S&P 500, and we confirm that there is no

predictability of total ASVI and ASVI

1-5

on future DGTW (Daniel et al. 1997)

abnormal returns. In sharp contrast, we find that ASVI

has strong predictive

power for future DGTW abnormal returns in the subsequent week. For example, a

one standard deviation increase in ASVI

is associated with 1.17% increase in the

stock return in the subsequent week. The above results are robust when we include

holidays or reorder the days in a week.

Our finding that weekend searches can predict the subsequent week’s returns

for the largest firms is novel to the literature. Large stocks typically receive the

most coverage from financial analysts and the news media. Therefore, it is striking

that we can still find predictability for the stocks with the largest market caps by

refining the measure of investor attention.

Further, we extend our results to the emerging Chinese stock markets using

the Baidu search index. The Chinese stock markets have developed into one of the

largest and most active markets in the world.

Baidu is a dominant Internet search

engine and provides SVI that resembles Google Trends. The Baidu’s SVI for

stocks has another advantage as the stock tickers in China are unique, which

further reduces the noise in the data.

The Chinese stock markets have a higher

Note that throughout the paper, the “Friday earnings announcements effect” or the “Friday

announcements effect” refers to the empirical fact that the immediate market response to Friday

announcements is weaker than non-Friday announcements. Therefore, a stronger Friday

announcements effect is equivalent to a weaker market response to Friday announcements.

A new world record for one day’s trading volume was created in the Chinese stock market on

December 5, 2014: 1.0741 trillion RMB.

Chinese stock tickers are six digits and uniquely defined. Unlike US stock markets, searching for a

six-digit stock code in Baidu is almost identical to searching for the corresponding stock with very

2019-2976-FIN

proportion of retail investors than the US stock markets, so we expect to find a

stronger impact of limited investor attention. Our findings with Chinese stocks are

indeed consistent with and stronger than what we find with US stocks.

Finally, we explore variations in weekend search that can further strengthen

our argument for using weekend search over weekday search to measure investor

attention. We argue that not all weekends are equal and some weekends are “busy”

with an overabundance of information and events. During such busy weekends,

investors’ cognitive abilities are restrained, and weekend search can be a noisy

measure of investor attention as well. We analyze periods with high investor

sentiment and periods with high market volatility and find that search during busy

weekends is less effective as a measure of investor attention in predicting stock

returns.

Background and Hypothesis Development

Information Technology, Information Environment, and Attention Measures

In this era of big data, information plays an important role. The wisdom of

crowds based on massive information has 1great power and possibility in

influencing the financial market. For example, to companies, the use of social

media is associated with firm equity value, and the transformative power of social

media is crucial to company development (Luo et al. 2013). To investors, the use

of Twitter can significantly reduce information asymmetry and increase the

liquidity of stocks (Blankespoor et al. 2013, Das and Chen 2007). With respect to

the stock market, Wikipedia can improve the information environment in the

financial market and moderate the timing of managers’ voluntary disclosure of

companies’ bad news (Xu and Zhang 2013).

The empiricists in financial economics face a substantial challenge in testing

theories of limited attention because direct measures of investors’ attention are

difficult to obtain. Instead, researchers have resorted to indirect proxies for

attention, such as extreme returns (Barber and Odean 2008), trading volume

(Barber and Odean 2008, Gervais et al. 2001, Hou et al. 2009), news and headlines

(Barber and Odean 2008, Yuan 2015), advertising expenses (Chemmanur and Yan

2009, Grullon et al. 2004, Lou 2014), and price limits (Seasholes and Wu 2007).

These proxies are indirect because of the reliance on the critical assumption that a

stock’s extreme return, turnover, or coverage in the news media should guarantee

investors’ attention. This assumption can be especially problematic when

investors’ attention is too scarce to cover all of these events or when attention is

not associated with these events.

little noise. For instance, searching for 601857 -- the ticker for China National Petroleum Corp -- in

Baidu shows trading and financial information for this company at the top of the result list.

2019-2976-FIN

The development of the Internet and information technology not only

facilitates information acquisition but also enables real-time recording of users’

searching activities. Numerous types of information about searches conducted by

users through search engines can be recorded, including the search location, time,

and frequency. Taking advantage of these features, Da et al. (2011) introduce the

SVI from Google Trends as a timely and efficient proxy for measuring individual

investors’ attention.

Compared with traditional indirect measures, the SVI has the

following advantages. First, Internet users commonly use a search engine to

collect information; search engines such as Google and Baidu continue to be

favorites in the United States and China. Indeed, up to now Google has accounted

for more than 70% of all the search queries performed in the United States. The

search volume reported by search engines is thus likely to be representative of the

Internet search behavior of the general population. Second, and more critically, a

search is a direct measure of attention: if you search for a stock in Google, you are

undoubtedly paying attention to it. Third, Google Trends or Baidu Index offers

timely (weekly/daily) SVI data for most keywords, which can effectively measure

the real-time attention of investors. Therefore, the aggregate search frequency in

Google Trends or Baidu Index is a direct and unambiguous measure of attention.

These advantages have led to the widespread adoption in empirical studies of the

SVI as a measure of investors’ attention.

Can Weekend Search Explain the Friday Earnings Announcements Effect?

We first reexamine the well-known findings on Friday earnings

announcements. Dellavigna and Pollet (2009) document that the immediate stock

response to earnings announcements on Friday is 15% weaker than on other

weekdays, and they attribute this effect to limited investor attention during

weekends. However, a recent study by Michaely et al. (2016) argues that the

previous findings are driven by selection bias that the firms that make

announcements on Fridays are different from the rest, and they find no evidence

that investors pay less attention to Friday announcements after correcting for

selection bias.

To the extent that weekend search volume captures retail investor attention,

we should be able to identify the channel through which investor attention affects

the market response. Specifically, we can distinguish the weekends with high and

low retail investor attention and examine whether the Friday announcements have

different effects on stock returns across high and low ASVI weekends. If we found

no difference, our results would be consistent with Michaely et al. (2016) that

investor attention plays no role in the Friday earnings announcements effect. On

the other hand, if we find that the Friday announcements effect is more

Ben-Rephael et al. (2017) also extend this approach to capturing institutional investor attention

using Bloomberg view volume.

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