Search Data and Behavioral Finance

1. Search Data and Behavioral Finance Joey EngelbergUniversity of California - San Diego Financial Risks International ForumMarch 21, 2014

2. 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?

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

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

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

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

7. UBS/Gallup survey, Michigan Consumer Confidence Index (Lemmon and Portniaguina, 2004; Qui and Welch 2006) What we search What we think What we tell others What we trade Prices

8. 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)

9. Attention: How many people searched for “AAPL” today? • Sentiment: How many people searched for “recession” today? What we think What we tell others What we search What we trade Prices

10. 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)

11. Google Flu Trends

12. 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)

13. 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

14. Google’s Search Volume Index (SVI)

15. 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

16. An Example

17. 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

18. Change in SVI around IPO

19. First-day IPO Return

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

21. 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

22. SVI for “Recession”

23. SVI for “Recession” and UM Consumer Sentiment

24. SVI for “recession” predicts the Michigan Consumer Sentiment Index What we search What we think What we tell others What we trade Prices

25. 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)

26. Conclusion • Search data offer us an unprecedented window into the minds of a broad population • Well-suited for behavioral finance • Ripe for future research