Professor, financial analyst, and herding spoke at 6th Annual Social Mood Conference
Dr. Nerissa Brown is an associate professor of accounting at the University of Delaware. Her research has spanned numerous topics relevant to socionomists, including how herding, sentiment and limited attention affect how investors use accounting information.
Dr. Brown joined a diverse roster of speakers at the 2016 Social Mood Conference on April 9 in Atlanta, GA. Before the conference, she spoke with us about how her research on herding overlaps with the study of social mood and offered a preview of her upcoming Social Mood Conference presentation.
Click the play button to listen to the conversation, or read the transcript below.
Socionomics Institute: You’re currently teaching accounting at the University of Delaware, but the work that got our attention was from your tenure at Georgia State. Can you tell me about your background and what attracted you to a career in academia?
Nerissa Brown: First of all, my attraction to academia has been just primarily from my general inquisitive mind and my interest in learning about how financial markets operate. I’m originally from Jamaica, and I worked for a large multi-segment firm as a financial analyst after completing my undergrad and master’s degrees in accounting. It was during this time while working as a financial analyst that I became quite fascinated with how markets respond to accounting-based information, especially information that is disclosed voluntarily. From Jamaica, I moved to the Washington, D.C. area and completed my doctoral degree at the University of Maryland. It was there in working with my advisor that I actually became really interested in herding and other behavioral biases. My dissertation looked at how herd behavior influences the disclosure of financial information. My advisor at the time was Russ Wermers, who did quite a bit of work on mutual fund herding. From there, we started working on the piece that you guys are familiar with in respect to herding by mutual funds on analytic information. Actually, the paper that got you guys interested was something that I was working on since about 2006, while I was at the University of Southern California. It took a little while to get it published. By the time it was published, I was at Georgia State.
SI: It sounds like you’ve been in many different academic locations. As you mentioned, the research that we were really attracted to is the behavioral biases that you focus on – herding and how sentiment affects market participants using different accounting information. What kind of overlap have you found between your research and the study of social mood?
NB: Many of the market biases that I study are direct reflections of social mood. For instance, stock market sentiment, which is something that I’ve worked on previously, is highly correlated with social mood in a larger context. For example, market optimism and pessimism have been argued to generally arise from an eclectic mix of cognitive and emotional biases that persist across individuals. It is this mood across individuals that translates into aggregate market sentiment. In connection with other research that I’ve done, one of my studies looks at how financial reporting is partly affected by sentiment and social mood in a larger context. My work on herd behavior is also linked to social mood, but it has argued that investor herding is actually more prevalent when market sentiment is high. At the conference, when I talk a little bit about my work in herd behavior, one of the things that I’ll highlight is that in my study of how mutual funds herd, we actually find that mutual fund herding is higher during the 1990s when you would say sentiment is relatively high during the boom period.
SI: Some interesting research that I came across when preparing for our interview was something you published on distracted driving laws and stocks. Can you tell me about your findings and the connection between those two?
NB: That was a pretty interesting project. I started working on that project with two PhD students while I was at Georgia State University. At first I thought the connection between distracted driving laws and stock market activity would be null, or zero. But interestingly, we found a pretty strong result. That study primarily focuses on how mobile communication, or how the use of mobile technology across investors and other market participants actually affects the stock activity of local stocks. We used distracted driving laws as a mechanism simply because large scale data on how investors and other market participants use mobile devices is actually unavailable to us as researchers. So we used distracted driving laws as a shock to the usage of mobile communication within a particular state to then try to tease out how stock-trading volume actually changes around the enforcement of these laws. We were pretty surprised with the results that we got. The magnitude of the results is not huge, but we could actually see a very persistent effect. The restriction of mobile communication usage resulted from the enforcement of distracted driving laws. For example, we found that firms within the state when these laws are enforced actually received a drop in internet search activity for their stock ticker. We also found a reasonable percentage drop in liquidity as well.
SI: Well from just our conversation, I’ve gathered that you’ve done quite a lot of research about how financial markets operate. Can you offer a preview of what exactly you’ll be discussing at this year’s Social Mood Conference?
NB: At the conference, I will focus my discussion primarily on herd behavior—herding not just necessarily by investors but also by firm managers, equity analysts and other market participants. I’ll connect or link my work with respect to herd behavior to social mood in a broader context, and hopefully I’ll also get some time to discuss how social mood connects with other behavioral trends that have been observed in capital markets. Social mood is, you would say, a bias that does have a pervasive impact on financial and business activities across the board. It’s not just linked to herd behavior or stock investment decisions, but prior studies have shown that social mood or sentiment is correlated with mergers and acquisitions, how firms use debt financing and IPO volume. And also in accounting, we’ve seen how sentiment or broad social mood is connected with accounting events such as asset write-downs, goodwill repayments and how firms actually manipulate earnings in trying to present their firm in perhaps a better light when investors are overly optimistic. In my presentation, I hope to draw a connection between social mood and not only investment activities, but also accounting events that we see in financial reporting.
SI: It sounds like it’s going to be a very fascinating presentation, and I’m very much looking forward to it. Thanks for chatting today.
NB: You’re very welcome!
The Socionomist enables you to see trend-shaping waves of social mood, as reflected by the stock market — and capitalize on them. The June issue looks at phases of social mood and features a “Research Roundup,” which highlights five academic studies that shine a light on important aspects of socionomic theory. Mood Riffs topics include “quiet luxury,” loneliness, the Writers Guild strike and efforts by countries to trade in currencies other than the U.S. dollar. Read the June issue when you become a Socionomics Premier Member.
