1. Search Data and Behavioral Finance
Joseph Engelberg
Counterpoint Mutual Funds
University of California - San Diego
December 14, 2016

2. (In)Efficient Markets and Why We Should Care
An Efficient Market is one in which an asset’s price is equal to its fundamental (correct) value.
You can buy and sell a ten-dollar bill for only $10 in an efficient market.
Believers are called “Rationalists” because they believe investors act rationally.
An Inefficient Market is one in which prices may deviate from fundamental value.
Believers are called “Behavioralists” because they believe investors have behavioral biases.
Implications for investing: In an inefficient market, a well-chosen strategy can beat the market.

3. The 2013 Nobel Prize in Economics
Gene Fama (U. Chicago)
Robert Shiller (Yale)
The Rationalists
The Behavioralists

4. Internet Activity and Our Investment Decisions
The Internet has increasingly become a place for commerce and information gathering. We are most interested in two types of users
Investors using the Internet to research an asset (e.g., a stock, San Diego real estate, etc.) before making an investment decision
Consumers using the Internet to purchase goods and services
Today there are many services which aggregate the Internet activity of investors and consumers.
How many people looked up the ticker “AAPL” today?
How many people visited today?
Can we use the answers to these questions to better inform our investment decisions in AAPL or EBAY?

5. 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) 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)

6. Application #1: Investors
Based on a paper by Da, Engelberg and Gao (Journal of Finance, 2011)
What we do:
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

7. Google’s Search Volume Index (SVI)

8. Google’s Search Volume Index (SVI)
How do we make comparisons?
Combine two searches in one

9. The Data We Collect
We collect weekly SVI for Russell 300o companies from Google Trends from Jan 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

10. An Example

11. What We Do in the Paper
Part 1: We show that our attention measure is correlated with but not fully captured by other measures
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

12. Change in SVI around IPO.

13. First-day IPO Return

14. Long-run Post-IPO Return, High SVI Change

15. Application #1: Investors (continued)
Based on a paper by Da, Engelberg and Gao (Review of Financial Studies, 2015)
What we do:
Use google search volume for sentiment-revealing terms (e.g., “recession” or “bankruptcy lawyer”) as a way to measure investor sentiment

16. SVI for “Recession”
NBER announced in December 2008 that we had been in a recession since December 2007…SVI for recession rising before

17. SVI for “Recession” and UM Consumer Sentiment
85% correlation. In fact, change in SVI for recession predicts change in UM sentiment at least a month ahead.

18. Application #2: Consumers
Firms announce sales/earnings four times a year
For example, a firm may announce in May the sales it had during the January – March period (Q1)
Internet activity data are available in real-time
For example, we will know how many people visited during January, February and March before sales are announced in May
Key question: can we use the internet activity we observe January – March to predict what announced sales will be in May?

19. An Example: Blue Nile
Back in October of 2012 I considered the stock Blue Nile…. Blue Nile is a retailer that sells jewelry exclusively online at Traded on NASDAQ (Ticker: NILE) At the time, about $ 500 million in market cap, followed by 8 analysts.

20. Blue Nile (Cont’d)
Measuring web traffic for Blue Nile is just like counting the number of people that go into and out of a brick and mortar store Because Nile sells all of its jewelry online, then site traffic for BlueNile.com should be correlated with Blue Nile revenue Is this true?

21. Blue Nile (Cont’d)
Alexa (an Amazon Company) provides daily site traffic data from Q to present day
I downloaded this data from Alexa then averaged the daily web traffic within each quarter so that my unit of observation was quarterly (just like my revenues)
This gave me 19 observations of quarterly web traffic (from Q to Q2 2012) that correspond to 19 observations of quarterly revenues over the same period.
Using these observations, the correlation between quarterly revenues and quarterly web traffic was 77%.
In other words, web traffic does an excellent job at forecasting what announced revenues will be for NILE
One bullet at a time

22. Blue Nile (Cont’d)
Given the historical correlation we observed between web traffic for bluenile.com and NILE revenues we can run a regression: Revenues = a + b*Traffic and the regression model estimates, given the traffic we’ve observed in Q3 2012, what announced revenues will be Hence we can use web traffic data to generate a trading signals: if predicted revenues are below company/analyst forecasts, SELL, if they are above, BUY

23. More Generally
Da, Engelberg and Gao (2012) and Chiu et al. (2015) use internet activity related to company products They show this activity can predict announced revenues and returns around earnings announcements