3. Asset Pricing Flashcards
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
Why do consumers turn to fellow customers when choosing among products instead of relying on expert advice?
Because of:
1. The emergence of social media platforms;
2. The increase in creation, popularity & consumption of user-generated content.
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
What are the 2 Research Questions of this paper?
- Do peer opinions include value-relevant news or do they merely constitute “random chatter” (i.e. the task should be left to professional analysts)?
- Are some users attempting to intentionally spread false “information” and mislead fellow market participants?
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
What are the goals of this study?
- To assess the performance of investors-turned-advisors
- To test whether investors can turn to their peers for genuine, useful investment advice.
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
What is the main source of the data in this study, and how is it measured?
Seeking Alpha (SA)” - one of the biggest investment-related social media websites in the US.
The data is measured in the frequency of negative words used in an article.
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
What are the two channels in SA for investors to voice their opinion?
- Through opinion articles which are reviewed by a panel and are subject to editorial changes;
- Through commentaries written in response to the opinion articles.
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
What are the key variables used to make a regression in this study?
X1 & X2:
1. NegSAi,t – an average fraction of NEGATIVE WORDS across all single-ticker ARTICLES published on SA about company i on day t
- NegSA-Commenti,t – an average fraction of NEGATIVE WORDS across all SA COMMENTS posted in an article about company i on day t
Y:
3. ARet - abnormal returns - “the difference between RAW RETURNS minus RETURNS on a value-weighted portfolio OF FIRMS WITH SIMILAR SIZE, book-to-market ratio, and past returns”.
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
What is the main finding of this study?
“The fraction of negative words in SA articles and the fraction of negative words in SA comments both negatively predict stock returns over the ensuing three months”.
As per our dear Kostja:
If negative words in articles/comments are up by 1%,
the future abnormal returns are lower by 0.332%/0.194%.
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
What are the possible reasons (channels) behind the main finding of the study?
Possible reasons behind findings:
- Predictability Channel: SA articles & commentaries contain pieces of value-relevant information which are not fully factored into the stock price as of the article publication date. As investors learn from the SA view, prices gradually adjust.
–If this reason is true:
—-SA views indeed predict future stock market performance;
—-Social media outlets are a useful source of value-relevant advice - Clout Channel: SA views reflect false information and cause investors to trade in the direction of the underlying articles and comments and move prices accordingly” (i.e. exploiting naïve investors)
–This reason is less likely to be true due to:
—-SA followers’ insufficient capital to cause real & sufficient market movements.
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
What is an Earnings Surprise?
The difference between:
–the REPORTED earnings-per-share (EPS) AND;
–the average of financial ANALYSTS’ EPS FORECASTS issued within 30 days prior to the earnings announcement.
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
How do the authors prove if the information in SA is value-relevant (Predictability Channel) or false (Clout Channel)?
They regress the number of negative words in articles/comments (the same X1 & X2 as before) on subsequent earnings surprises (new Y).
If they find that negative words predict earnings surprises: the information in SA is value-relevant and is not fully factored into the stock price as of the article publication date.
If they find that negative words do NOT predict earnings surprises: the information in SA is false as users cannot affect the company’s earnings (no relationship between X1, X2 & Y).
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
Do the authors find that the SA articles & commentaries contain pieces of value-relevant or false information?
They find that:
“The fraction of negative words in SA articles & comments strongly predict earnings surprises,” which suggests that the opinions expressed in SA articles & comments.
indeed provide value-relevant information.
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
Why should a SA user share their insights of value-relevant information publicly?
- Popularity: (users want attention & recognition they receive from posting opinions that subsequently are
confirmed by the stock market. Sometimes, SA articles
are referred to and discussed in Forbes, WSJ, etc. - many users strive to become online celebrities) - Monetary Compensation: (each SA contributor earns $10 per 1,000-page views & potentially more depending on the article quality)
–is TESTED on this paper - Immediate Feedback: (users correct bad
articles, which, on the assumption that the crowd is educated, increases the informativeness of social media)
–is TESTED on this paper - Convergence to Fundamental Value: (if SA users have some price impact they can expect the direction of market prices to go to what authors perceive to be the
fair fundamental value)
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
The authors test whether Monetary Compensation and Immediate Feedback drives users to post more value-relevant information on the paper. What do they conclude?
- Monetary Compensation Findings
–“The number of page views AND the number of times an article is read-to-end are increasing with the author’s historical level of consistency.“
–SA followers can “differentiate between authors that offer historically good VS bad advice;
–SA followers give more “popularity” to the good than the bad advice given. - Immediate Feedback:
–The authors test “to what degree SA commentaries are of a different tone than the underlying SA article”
–They find that “followers disagree with authors more when the authors’ articles have been inconsistent.”
–They find that if “the authors’ articles are inconsistent, it is the tone of COMMENTS that more reliably predicts subsequent stock market performance.”
“Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media” (Chen, H., De, P., Hu, Y. J., Huang, B., (2014))
What are the key takeaways from the article?
–Peer-based advice is Useful & Value-Relevant
–Social media outlets enable direct & immediate interaction among users which, combined with intelligence of a “crowd,” are one of the primary reasons social media platforms are able to produce value-relevant content.
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
What are the two pillars of asset pricing?
When we try to determine whether the price of an asset is correct, there are two possible sides on how to look at it:
- Efficient Market Hypothesis
- The existing models of Asset Pricing
*
The models of Asset Pricing are usually derived assuming Market Efficiency; AND you can prove Market Efficiency by using a model of Asset Pricing (a chain that should work both ways).
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
What is the Efficient Market Hypothesis?
EMH claims that asset prices reflect ALL available information (past and present, public and private).
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
What are the 3 possible forms of Market Efficiency?
3 forms of Market Efficiency:
1.Weak Market Efficiency:
–prices reflect only past information
–prices reflect only publicly available past information
2.Semi-Strong:
–prices reflect past AND present information
–prices reflect only publicly-available information
3.Strong:
–prices reflect past AND present information
–prices reflect public AND private information
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
What is the Joint Hypothesis problem?
Introduction:
When you want to test for Market Efficiency, you have a goal of “determining whether the expected return of a certain asset (which is derived from a certain asset pricing model) is equal to the actual return of the asset.”
Joint Hypothesis problem:
If the expected return is not equal to the actual return (aka Market Efficiency rejected), it can mean that either:
1. The Market is indeed Inefficient OR;
2. The chosen Asset Pricing Model is bad.
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
What are the ways how to test Market Efficiency?
- With Event Studies;
- With Predictive Regressions;
- With Time-Varying Expected Stock Returns;
- With Bubbles
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
How to test Market Efficiency with Event Studies?
Fama has tested Market Efficiency by looking at an adjustment of stock prices to a specific company announcement (=an event, in this case, stock splits).
What is a stock split?
A stock split is an action in which a company divides its existing shares into multiple shares to boost the liquidity of the shares.
-The number of shares increases by a specific multiple (the most common split ratios are 2-for-1 or 3-for-1, which means that the stockholder will have two or three shares for every share held earlier), BUT;
-The price per share after the 3-for-1 stock split will be reduced by dividing the price by 3 (so, the real market value of the company does not change - no. of stocks multiply, but stock prices divide).
Continuing on Fama:
-In efficient markets, stock prices should adjust accurately to new (present) information.
-In his study about an announcement of a stock split, Fama finds that all stock split-related information is incorporated into prices months before the split, confirming market efficiency in short term periods.
-However, he says that in long term, expected returns are larger than the price of the effect of a studied event, thus, JHP becomes relevant again.
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
How to test Market Efficiency with Predictive Regressions?
Irving Fisher’s Market Efficiency Hypothesis claims: interest rates should contain expected real return plus the best possible forecast of the inflation rate.
And Fama finds out:
1) Bond and Real Estate prices incorporate the best possible forecast of inflation (nominal interest rate=real return+forecast of inflation).
Therefore, for bonds & real estate:
–we accept market efficiency proposition that bond and real estate prices incorporate the best possible forecasts of inflation;
–we accept a model of market equilibrium in which expected real returns vary independently of expected inflation.
2)Stock prices do NOT incorporate the best possible forecast of inflation (findings are the pure opposite - when expected stock returns are higher, expected inflation is low: nominal return»>real return+forecast of inflation)
A question appears - why did this equation not work for stocks? Two answers appear (JHP):
–either is it due to poor inflation forecasts (market inefficiency) OR;
–we chose a bad model of stock market equilibrium/asset
pricing model)
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
Can a Market be Efficient with Time-Varying
Expected Stock Returns?
Earlier: Efficient Market assumed expected stock returns to be constant over time.
Now: Both Risk & My Willingness to bear risk are not assumed to be constant over time. But can a Market be Efficient with Time-Varying Expected Stock Returns?
Fama thinks: YES
–predictable (!!!) volatility in expected returns on stocks is rational (aka it is rational and predictable to change risk-willingness over lifetime);
–therefore, even a market with variation in risk or people’s willingness to bear risk is an efficient market.
Behavioralists think: NO
–volatility in expected returns on stocks is due to investor irrationality (irrational price swings, herd behavior, etc.)
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
Can Bubbles be a part of an Efficient Market?
A bubble is defined as: “an irrational strong price increase that implies a predictable strong decline.”
Fama thinks: (maybe) YES
–he denounces the “predictable strong decline” part of the definition with the argument that “there is no academic evidence that price declines are EVER predictable”.
–he also denounces the “irrational price increase” part of the definition, saying that no, large swings (price increases, in this case) in prices are not irrational, but are rational responses to large swings in REAL economic activity. And, as per EMH definition (asset prices reflect ALL available information), if there are large price swings/trends in the real economy, efficient stock markets will reflect this available information and it will portray into stock prices.
–why (maybe) YES? Fama is cautionary against the use of the “bubble” word at all without more careful definition and empirical validation.
Behaviorists think: NO
–“irrational price increase” is a part of human psychological behavior which constitutes that a market with bubbles cannot be efficient.
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
What are the types of Asset Pricing Models?
- Standard Equilibrium models:
–work from theoretical assumptions
–e.g. CAPM - Empirical models:
–work backwards from observed patterns in average returns and try to explain them by proposing factors
–e.g. Fama-French 3 or 4-factor model, APT
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
Does Fama think that CAPM is a good Asset Pricing model?
Fama thinks: NO
–although the model was needed to lay the foundations of asset pricing theory;
–it has been proven that only β (the only unique variable in the model) is not sufficient to explain expected returns
“Two Pillars of Asset Pricing” - (Fama, E. F. (2014))
Does Fama think that the 3-factor model is a good Asset Pricing model?
Fama thinks: YES and NO
–3-factor model adjusts well on the anomalies for:
—-size;
—-sales growth;
—-various price ratios
–(however, here is long-standing controversy about the effect of the size on average returns)
–BUT the model doesn’t absorb other anomalies.
–Fama claims that momentum should be added as an explanatory factor (which it later is, in 4-factor model)
