Joey Engelberg University of California - San Diego Financial Risks International Forum March 21, 2014 Search Data and Behavioral Finance

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?

What we think What we search What we tell others What we trade Prices

What we think What we search What we tell others What we trade Prices 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 trade Prices Closed-end fund discount (Lee, Shleifer and Thaler, 1991)

What we think What we search What we tell others What we trade Prices Turnover and IPO Volume (Baker and Wurgler, 2006)

What we think What we search What we tell others What we trade Prices 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 trade Prices 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) 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 “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

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

First-day IPO Return

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

What we think What we search What we tell others What we trade Prices 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