TY - JOUR
T1 - Do Indian Stock Market Message Board Discussions Really Matter? A Machine Learning-based Approach
AU - Sethi, Madhvi
AU - Gupta, Pooja
AU - Mukherjee, Shubhadeep
AU - Agrawal, Siddhi
PY - 2020/11/10
Y1 - 2020/11/10
N2 - Behavioral finance literature has long claimed that internet stock message boards can move markets. In this chapter, the authors study more than 2,000 internet board messages posted across finance message boards in India (Chittorgarh, etc.) for 110 companies that went for initial public offering (IPO) in the last one year. This study has multi-fold objectives. First, the authors try to identify the factors which lead to a discussion on an IPO stock in the message board. Second, the authors identify the factors which differentiate a widely discussed stock from the less discussed one. Next, the authors apply advanced machine learning technique to identify the topics which are discussed in the message board through automatic topic modeling. The methodology used includes a logistic regression model for identifying firm characteristics which leads to a probability of getting stakeholders’ attention and hence more discussion. The authors also use advanced topic modeling techniques to identify topics of discussion on the message boards through machine learning. The authors find that larger sized firms, younger firms, firms with low leverage, and nonmanufacturing firms get discussed more and the topics of discussion relate to their financial statements, trading strategies, stock behavior, and performance.
AB - Behavioral finance literature has long claimed that internet stock message boards can move markets. In this chapter, the authors study more than 2,000 internet board messages posted across finance message boards in India (Chittorgarh, etc.) for 110 companies that went for initial public offering (IPO) in the last one year. This study has multi-fold objectives. First, the authors try to identify the factors which lead to a discussion on an IPO stock in the message board. Second, the authors identify the factors which differentiate a widely discussed stock from the less discussed one. Next, the authors apply advanced machine learning technique to identify the topics which are discussed in the message board through automatic topic modeling. The methodology used includes a logistic regression model for identifying firm characteristics which leads to a probability of getting stakeholders’ attention and hence more discussion. The authors also use advanced topic modeling techniques to identify topics of discussion on the message boards through machine learning. The authors find that larger sized firms, younger firms, firms with low leverage, and nonmanufacturing firms get discussed more and the topics of discussion relate to their financial statements, trading strategies, stock behavior, and performance.
KW - Behavioral finance
KW - Crowd intelligence
KW - Initial public offering
KW - Investor behaviour
KW - Logistic regression
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85135197957&partnerID=8YFLogxK
UR - https://www.emerald.com/insight/content/doi/10.1108/S0196-382120200000036010/full/html
U2 - 10.1108/S0196-382120200000036010
DO - 10.1108/S0196-382120200000036010
M3 - Conference article
AN - SCOPUS:85135197957
SN - 0196-3821
VL - 36
SP - 201
EP - 216
JO - Research in Finance
JF - Research in Finance
ER -