Behavioral Finance - How can we measure noise trader risk, and can we use it as an investor?
10 important questions on Behavioral Finance - How can we measure noise trader risk, and can we use it as an investor?
What is investor sentiment?
What is the dual hypothesis problem?
What are the suggested measures for investor sentiment?
- Exogeneous
- Surveys
- Investor mood
- Market based
- Dividend Premium
- Option Implied Volatility (fear index)
- IPO returns
- Retail investors (includes noise traders)
- Retail Investor Trades
- Mutual Fund Flows
- Trading Volume
- Corporate Decisions
- IPO volume (high prices -> more IPO's)
- Equity issues
- Insider trading
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How did Baker & Wurgler measure sentiment?
How did Baker & Wurgler make sure that sentiment measure sentiment and not fundamental news?
- Orthogonalization:
- run regression: Sent = a + B * MACRO FUNDAMENTALS + e
- and use residuals e
- Per definition, e is not correlated with (=orthogonal to) the macro fundamentals
- So, use epsilon!
What is the difference between equal weigthed and value weighted portfolios and why are the returns higher/lower with equal weighted portfolios?
- Small stocks have a much higher weight in equal weight portfolios than in value weighted portfolios -> so for small stocks the sentiment is stronger for small stocks than for big stocks
Why are s![]()
ome stocks more sensive to sentiment than others?
- Differences in sentiment-based demand shocks (if my sentiment is super high, which stock will I buy --> e.g. TESLA)
Difficulty/objectively in determining value
- Young, unprofitable, potential growth stocks
- Differences in limits to arbitrage (difficult to arbitrage stocks are more sensitive than easily to arbitrage stocks) --> most speculative stocks
- More costly to buy/sell, short
- High idiosyncratic volatility
- Young, small, unprofitable, extreme growth stocks
What are 2 methods to measure sensitivity to sentiment?
- In-sample
Out-of sample method
What are some other characteristics that predict sensitivity to sentiment?
- Small size
- Low age
- Low B/M
- Low earnings
- Low dividends
- High R&D
- Low assets
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