Search Data and Behavioral Finance
Joey Engelberg
University of California - San Diego
Financial Risks International Forum
March 21, 2014
Behavioral Finance Concepts
 Behavioral Finance: the union of finance and
psychology
 Thus many key concepts – e.g., attention and
sentiment – are concepts of the mind
 Notoriously hard to measure
 How can we test the theory?
Prices
What
we
trade
What
we tell
others
What
we
search
What
we
think
Prices
What
we
trade
What
we tell
others
What
we
search
What
we
think
 Popular measures of attention: trading volume, up/down
markets, etc.
 Popular measures of sentiment: closed-end fund discount,
trading volume, IPO returns, etc.
Prices
What
we
trade
What
we tell
others
What
we
search
 Closed-end fund discount (Lee, Shleifer and
Thaler, 1991)
What
we
think
Prices
What
we
trade
What
we tell
others
What
we
search
 Turnover and IPO Volume (Baker and Wurgler,
2006)
What
we
think
Prices
What
we
trade
What
we tell
others
What
we
search
 UBS/Gallup survey, Michigan Consumer
Confidence Index (Lemmon and Portniaguina,
2004; Qui and Welch 2006)
What
we
think
The Research Frontier is Measurement
 What’s next: “Now, the question is no longer, as it
was a few decades ago, whether investor sentiment
affects stock prices, but rather how to measure
investor sentiment and quantify its effects.”
(Baker and Wurgler 2007)
Prices
What
we
trade
What
we tell
others
What
we
search
What
we
think
 Attention: How many people searched for “AAPL”
today?
 Sentiment: How many people searched for
“recession” today?
A Motivating Example
 Google Labs recently developed an influenza-like illness
(ILI) prediction system based on search of 45 flu-related
terms (Ginsberg et al., Nature, Feb 19, 2009)
Google Flu Trends
A Motivating Example
 The result: search volume for flu-like symptoms can
report flu outbreaks 1-2 weeks before the Centers for
Disease Control and Prevention (CDC)
“Harnessing the collective intelligence of millions of users, Google
web search logs can provide one of the most timely, broad-reaching
influenza monitoring systems available today.”
- Ginsberg et al. (2009)
 Takeaway: search volume is a revealed measure, i.e. it
reveals the attention, interests, concerns of its users
 Perfect for behavioral finance: (almost) real-time insight
into the minds of a broad population
Application #1: Investor Attention
 “In Search of Attention” by Da, Engelberg and Gao
(Journal of Finance, 2011)
 Brief Summary:
 Use google search volume for stock tickers (e.g.,
“MSFT” or “AAPL”) as a way to measure retail
investor attention towards stocks
 Show that this signal predicts returns, especially
for IPOs
Google’s Search Volume Index (SVI)
The Data We Collect
 We collect weekly SVI for Russell 300o companies
from Google Trends from Jan 2004 to Jun 2008
 Firm names are problematic
 Investors may search firm names for non-stock related reasons
(Apple, Chase, Best Buy, etc)
 A firm’s name may have many variations
 We focus on stock tickers instead in most of our
applications
Tickers measure search for financial information
Alleviate problems associated with the firm name
We flag out “noisy” tickers (GAP, GPS, DNA, BABY, …, A, B, … etc.)
Most results improve we when we exclude “noisy” tickers (about 7%
of the sample)
 For analysis related to IPO, we search stock by company names




An Example
What We Do in the Paper
 Part 1: We show that our attention measure is correlated with but
not fully captured by other measures
 We regress SVI on standard attention measures and extract a residual.
(The paper’s results hold with both SVI and Residual SVI)
 Part 2: We show that our attention measure is capturing retail
attention
 Intuitively it should be individual, retail investors
 Part 3: Given we are dealing with retail attention, we consider the
Barber and Odean (2008) theory that shocks to retail attention
create price pressure
 We find retail attention predicts short term return increases
among smaller stocks
 We find retail attention predicts first-day IPO returns and
subsequent reversals
Change in SVI around IPO
Cross-Sectional Change of Search Volume Index (SVI) Values
1.00
SVI Change (Mean)
0.80
SVI Change (Median)
Change of SVI
0.60
0.40
0.20
0.00
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
-0.20
-0.40
Event Week
1
2
3
4
5
6
7
8
9
10
First-day IPO Return
Pre-IPO Key Word Search and Average First-day IPO Returns
20.00%
18.00%
Average First-day Return
16.00%
IPO First-day Return
14.00%
Return Difference: 6.78% ,
t-statistics: 2.17
12.00%
10.00%
8.00%
6.00%
10.48%
17.25%
4.00%
2.00%
0.00%
SVI_Change (Low)
SVI_Change (High)
Long-run Post-IPO Return, High SVI Change
Application #2: Investor Sentiment
 “The Sum of All FEARS” by Da, Engelberg and Gao
(Review of Financial Studies, forthcoming)
 Brief Summary:
 Use google search volume for sentiment-revealing
terms (e.g., “recession” or “great depression”) as a
way to measure investor sentiment
 Show that FEARS predicts returns, volatility and
fund flows in a way prescribed by theories of
investor sentiment
SVI for “Recession”
SVI for “Recession” and UM Consumer Sentiment
Prices
What
we
trade
What
we tell
others
What
we
search
 SVI for “recession” predicts the Michigan Consumer
Sentiment Index
What
we
think
What We Do in the Paper
 Part 1: We use the Harvard IV-4 and the Lasswell Value Dictionary
(Tetlock (2007), Tetlock et al. (2008)) to form a list of negative
sentiment-revealing terms
 Call this the Financial and Economic Attitudes Revealed by Search (FEARS)
 Part 2: We show that increases in FEARS today predict low market
returns today but high market returns over the following two days
 Part 3: Also find increases in FEARS predicts excess volatility and
fund flows out of equity funds and into bond funds
 Predictability for returns, volatility and fund flows consistent with
theories of investor sentiment (e.g., De Long, Shleifer, Summers and
Waldmann, 1990)
Conclusion
 Search data offer us an unprecedented window into the
minds of a broad population
 Well-suited for behavioral finance
 Ripe for future research