Okke

Research question#

• Alphabet bias on $Pr$[stock price crash]

• This is something you should argue for very clearly:

  • Since these investors have less expertise and relatively trade more stocks higher up the alphabetic list, this could mean a higher risk of stock price crashes (?).

    You should spell out why that would be the case. Trading a stock is one thing, but also rational investors would respond to bad information by selling the stock, also causing a stock price crash. So how and why do ‘bias-prone’ investors respond differently from ‘expert’ investors?

    In other words, the channel you suggest would also work for stocks that are being traded predominantly by expert investors.

• If you want to imply that shares whose trading is dominated by traders prone to alphabet bias have a higher probability to crash, it implies they are also prone to some kind of overreaction

• Maybe you can make that plausible

Contribution:

• Your contribution would then be that alphabet bias is correlated - or maybe even identical to - other kinds of bias which cause overreaction to bad news

Data:

• Compustat has (afaik) a lot of US-data, also on small cap stocks
• You might want to try several packages in Python/R (if you use that) to find stock data, possibly also in Europe

Variable definition & Methodology:

• I think you somehow need to find the proportion of investors that are prone to alphabet bias
• I think you need a multivariate regression:
  • Dependent variable: Strongly negative stock return during a period yes/no
  • Independent variable:
    • Bad news
    • Alphabet / proxy for: Proportion of traders prone to alphabet bias
    • Interaction effect between bad news & alphabet
  • How to get bad news?

Conclusion:

• I think the research proposal is feasible. You do have to pay attention to the mechanism leading to the increased probability in stock price crashes: what is the reason, and what are the implications thereof for your empirical model?