Do Indian Stock Market Message Board Discussions Really Matter? A Machine Learning-based Approach

Madhvi Sethi, Pooja Gupta, Shubhadeep Mukherjee, Siddhi Agrawal

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)201-216
Number of pages16
JournalResearch in Finance
Publication statusPublished - 10 Nov 2020


  • Behavioral finance
  • Crowd intelligence
  • Initial public offering
  • Investor behaviour
  • Logistic regression
  • Machine learning


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