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Transcript of Joey Engelberg University of California - San Diego Financial Risks International Forum March 21,...
Joey Engelberg
University of California - San Diego
Financial Risks International ForumMarch 21, 2014
Search Data and Behavioral Finance
Behavioral Finance ConceptsBehavioral Finance: the union of finance
and psychology
Thus many key concepts – e.g., attention and sentiment – are concepts of the mind
Notoriously hard to measureHow can we test the theory?
What we
think
What we
search
What we tell others
What we
tradePrices
Popular measures of attention: trading volume, up/down markets, etc.
Popular measures of sentiment: closed-end fund discount, trading volume, IPO returns, etc.
What we
think
What we
search
What we tell others
What we
tradePrices
Closed-end fund discount (Lee, Shleifer and Thaler, 1991)
What we
think
What we
search
What we tell others
What we
tradePrices
Turnover and IPO Volume (Baker and Wurgler, 2006)
What we
think
What we
search
What we tell others
What we
tradePrices
UBS/Gallup survey, Michigan Consumer Confidence Index (Lemmon and Portniaguina, 2004; Qui and Welch 2006)
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)
What we
think
What we
search
What we tell others
What we
tradePrices
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)
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)
Takeaway: search volume is a revealed measure, i.e. it reveals the attention, interests, concerns of its usersPerfect for behavioral finance: (almost) real-time
insight into the minds of a broad population
“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)
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
The Data We CollectWe 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
What We Do in the Paper
Part 1: We show that our attention measure is correlated with but not fully captured by other measuresWe 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 pressureWe find retail attention predicts short term return
increases among smaller stocksWe 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
-0.40
-0.20
0.00
0.20
0.40
0.60
0.80
1.00
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
Event Week
Ch
ang
e o
f SV
I
SVI Change (Mean)
SVI Change (Median)
First-day IPO Return
Pre-IPO Key Word Search and Average First-day IPO Returns
17.25%10.48%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
18.00%
20.00%
SVI_Change (Low) SVI_Change (High)
IPO
Fir
st-
day
Re
turn
Average First-day Return
Return Difference: 6.78%, t-statistics: 2.17
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
What we
think
What we
search
What we tell others
What we
tradePrices
SVI for “recession” predicts the Michigan Consumer Sentiment Index
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