Title of Abstract
iPBA: Behavioral Financial Analysis
Abstract
Dealing with data is arguably one of very few commonalities between researchers of different disciplines. A year ago the interdisciplinary program for big-data analytics (iPBA) was formed with a goal of creating a forum for faculty to share thoughts and research ideas related to dealing with data. An interdisciplinary team of three faculty and two graduate students from finance and computer sciences was formed, and an extensible infrastructure was developed to collect and manage real-time twitter streams and individual stock trade data.
One of the early project ideas focused on studying the social media and stock markets, which was inspired by the fact that the former may have an impact on stock prices (Hachman, 2011). We investigate whether a bi-directional intraday relationship between stock volatility and tweets exists by analyzing minute-by-minute stock price and tweet data for the 30 stocks in the Dow Jones Industrial average over a random 13-day interval from June 2 to June 18, 2014. We find strong evidence of a bi-directional relationship between returns and tweets, both between lagged innovations and current conditional volatilities and between immediate and persistent volatilities. These results may help traders achieve superior returns by buying and selling individual stocks or options.
iPBA: Behavioral Financial Analysis
Dealing with data is arguably one of very few commonalities between researchers of different disciplines. A year ago the interdisciplinary program for big-data analytics (iPBA) was formed with a goal of creating a forum for faculty to share thoughts and research ideas related to dealing with data. An interdisciplinary team of three faculty and two graduate students from finance and computer sciences was formed, and an extensible infrastructure was developed to collect and manage real-time twitter streams and individual stock trade data.
One of the early project ideas focused on studying the social media and stock markets, which was inspired by the fact that the former may have an impact on stock prices (Hachman, 2011). We investigate whether a bi-directional intraday relationship between stock volatility and tweets exists by analyzing minute-by-minute stock price and tweet data for the 30 stocks in the Dow Jones Industrial average over a random 13-day interval from June 2 to June 18, 2014. We find strong evidence of a bi-directional relationship between returns and tweets, both between lagged innovations and current conditional volatilities and between immediate and persistent volatilities. These results may help traders achieve superior returns by buying and selling individual stocks or options.