Monetary Incentive and Stock Opinions on Social Media

Abstract

Not only is social media a new channel to obtain financial market information, it has also become a venue for investors to share and exchange investment ideas. We examine the performance consequences of providing monetary incentive to both existing and new amateur analysts on social media and its implications for online investor communities. We find that monetary incentive is effective in increasing the amount of content output and generating more interest from the community, but it leads to neither better nor worse stock recommendations. Additional analysis suggests that monetary incentive results in wider stock and industry coverage, a sign of increased content diversity. This study contributes to the understanding of the role of monetary incentive in stimulating the sharing of value-relevant information by investors in social media communities.

Key words and phrases:

• monetary incentive
• stock opinions
• social media
• wisdom of crowds
• investment advice
• incentives in co-creation

Notes

1. The premium partnership program was announced in an open letter from Seeking Alpha to contributors: https://seekingalpha.com/article/246803-an-open-letter-to-seeking-alpha-contributors. Over time, Seeking Alpha has made several changes to the program. For instance, in June 2013, Seeking Alpha started to make a minimum payment of $150 for articles selected as a Small-Cap Insight; in July 2014, Seeking Alpha added an additional flat payment of $35 on top of the $10 per thousand page views. Details about these changes are available at https://seekingalpha.com/article/1475331-why-were-boosting-payments-to-high-value-contributors and https://seekingalpha.com/article/2343015-an-end-to-our-relationship-with-yahoo-a-new-era-for-equity-research?page=2. Our study period is 2009 to 2012, and thus our analysis is not affected by the changes introduced after 2012.

2. Only a small number of SA contributors publish both regular and premium articles. If we include these contributors in our analysis, each of them would be in the control group and treatment group simultaneously. This would make it difficult to interpret our results, so we exclude them from the analysis to err on the side of caution. However, our results do not change if all these contributors are assigned to either the treatment group or the control group.

3. Different contributors may have different writing styles, and their level of use of negative words could also be different. We test the robustness of our results by applying contributor-specific medians when classifying articles into bullish or bearish ones, and our results remain the same (results are available upon request).

4. As a robustness check, we also construct the article quality measure using the cumulative one-month and six-month abnormal returns. Our results remain the same (See Table 5).

5. To formally test the parallel trend assumption under a regression framework, we estimate a model similar as Equation (1)(1) $Y_{it}={\beta }_0+{\beta }_{1}Treatmen{t}_i\times Afte{r}_t+{\beta }_{2}Afte{r}_t+{\beta }_{3}Log\left(ActivePerio{d}_{it}\right)+{\beta }_{4}Log\left(DaysOnS{A}_{it}\right)+f_i+{\epsilon }_{it}$(1) on the data prior to the launch of the premium partnership program (year 2009 and 2010 only). We consider a placebo event that took place at the beginning of 2010 and define a variable PlaceboAfter, which is 1 if the year is 2010 and 0 if the year is 2009. The dependent variable is either the total number or the average accuracy of articles written by a contributor in a year. The coefficient estimates for the interaction term, Treatment×PlaceboAfter, are statistically insignificant at the 10 percent level in both regressions, which suggests that the trends of the dependent variables follow a parallel trend for the two groups prior to the launch of the premium partnership program.

Additional information

Funding

The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 11531516).

Notes on contributors

Hailiang Chen

Hailiang Chen ([email protected]; corresponding author) is an Associate Professor at the Faculty of Business and Economics, University of Hong Kong. His research focuses on social media, financial technology (FinTech), multichannel management, business analytics, venture capital, entrepreneurship, mobile commerce, economics of information systems, and design science. His work has been published in elite business journals in information systems, finance, and management, including Information Systems Research, Journal of Management Information Systems, Management Science, Review of Financial Studies, and Strategic Management Journal. His research received wide coverage in media venues, such as Wall Street Journal, Forbes, Reuters, Seeking Alpha, and others.

Yu Jeffrey Hu

Yu Jeffrey Hu ([email protected]) is the Sharon A. and David B. Pearce Professor, Director of China Program, Co-Director of Business Analytics Center, and Associate Director of Master of Science in Analytics Program at the Scheller College of Business at Georgia Institute of Technology. He is also a Digital Fellow at MIT’s Initiative on Digital Economy. Dr. Hu is an expert on big data, business analytics, Internet retailing, social media, mobile commerce, consumer behavior, and online advertising. He has been an expert, consultant, or project leader for governments and many large companies around the world. His research has been published in top  journals such as Management Science, Information Systems Research, MIS Quarterly, Review of Financial Studies, and MIT Sloan Management Review. His research has been discussed extensively and cited by many media outlets.

Shan Huang

Shan Huang ([email protected]) is an Assistant Professor of Information Systems and Operations Management at the Foster School of Business, University of Washington. She holds a Ph.D. in Management from MIT Sloan School of Management. Dr. Huang’s research focuses on social influence, network marketing, and motivations to generate and diffuse information in social networks. She designs and analyzes field experiments in massive social networks to understand social behaviors and generate business insights. Her current work examines heterogeneous effects of social influence across different behaviors and various kinds of products.