Conference Agenda

Session
BF-1: Behavioral Factors in Valuation: Empirical Evidence
Time:
Thursday, 22/Aug/2019:
13:30 - 15:00

Session Chair: Francesco D'Acunto, Boston College
Location: D -112

Presentations

Background Noise? TV Advertising Affects Real Time Investor Behavior

Jura Liaukonyte2, Alminas Zaldokas1

1Hong Kong University of Science and Technology, Hong Kong S.A.R. (China); 2Cornell University, USA

Discussant: Daniel Schmidt (HEC Paris)

Using minute-by-minute television advertising data covering approximately 326,000 ads, 301 firms, and $20 billion in ad spending, we study the real-time effects of TV advertising on investor search for online financial information. Our identification strategy exploits the fact that viewers in different U.S. time zones are exposed to the same programming and national advertising at different times, allowing us to control for contemporaneous confounding events. We find that an average TV ad leads to a 3% increase in SEC EDGAR queries and 8% increase in Google searches for financial information within 15 minutes of the airing of that ad. Such advertising effects spill over through horizontal and vertical product market links to financial information searches on closest rivals and suppliers. The ad-induced queries on advertising firm and its key rival are linked to higher trading volumes of their respective stocks. For large advertisers, 0.8% of average daily trading volume can directly be attributed to advertising.This suggests a sizeable product market advertising effect on financial markets.

efa2019-BF-1-496-Background Noise TV Advertising Affects Real Time Investor Behavior.pdf


Cultural Biases in Equity Analysis

Vesa Pursiainen

Imperial College London

Discussant: Jordan Nickerson (Boston College)

I study the role of cultural biases in equity analysts' stock recommendations. I construct a Eurobarometer-based measure of cultural trust bias between European countries and find that a more positive trust bias by the analyst's country of origin toward the firm's headquarter country is associated with significantly more positive stock recommendations. I also find evidence of a significant positive home country bias. The cultural bias effect in recommendations varies over time, increasing with the aggregate level of pessimism in Europe and decreasing with consumer confidence. It is stronger for analysts from countries with a more negative attitude toward globalization and for eponymous firms whose names mention their home country, suggesting firm names can have a priming effect on cultural biases. The bias effect increases with analyst tenure, while analysts working at larger brokers are less affected by cultural bias. I also find evidence of a new negative North-South bias during the European debt crisis and a Franco-British bias during the Iraq war.

efa2019-BF-1-806-Cultural Biases in Equity Analysis.pdf


Household Portfolio Underdiversification and Probability Weighting: Evidence from the Field

Stephen Dimmock1, Roy Kouwenberg2, Olivia S. Mitchell3, Kim Peijnenburg4

1Nanyang Technological University; 2Mahidol University; 3The Wharton School; 4EDHEC Business School

Discussant: Baolian Wang (University of Florida)

We test the relation between probability weighting and household portfolio choice in a representative household survey, using custom-designed incentivized lotteries. On average, people display Inverse-S shaped probability weighting, overweighting the small probabilities of tail events. As theory predicts, our Inverse-S measure is positively associated with portfolio underdiversification, which results in significant Sharpe ratio losses. We analyze respondents’ individual stock holdings and find that people with higher Inverse-S tend to pick lottery-type stocks and hold positively-skewed equity portfolios. Furthermore, Inverse-S is positively associated with stock market nonparticipation. We find evidence indicating that these choices reflect preferences rather than probability unsophistication.

efa2019-BF-1-598-Household Portfolio Underdiversification and Probability Weighting.pdf