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Summary: asset pricing (endterm)
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Extensive summary for Asset pricing lectures 11-15 (end term material). Written for the subject Asset pricing for the master Banking and Finance (University of Utrecht).
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Lecture 11: From efficient markets to behavioral financeEfficient market hypothesis (EMH)
In the 1970s, the efficient market hypothesis became the new paradigm (= way of thinking)
in finance. The EMH ties together finance and the real economy:
- economic agents have rational expectations
- asset prices reflect economic fundamental, they are intrinsically meaningful
We have been working with representative agents (replicas), who on average have the right
forecasts of the values of random variables. Moreover, prices reflect economic
fundamentals. Stocks are based on future dividends, etc.
Fama (1970) introduced three forms of market efficiency:
1. weak
a. prices reflect all past (no information of this quarter) public (everybody can
access it) information
2. semi-strong
a. prices reflect all past public information and all current public information.
Meaning, everything which is happening this quarter which is publicly
available is also incorporated in the stock price. You would not be able to
make any excess profits.
3. strong
a. prices reflect all past public, current public and private information. Intuition:
trading occurs until price == value. If a stock is worth 10, but is trading a 8,
you would buy the stock. This demand would drive up the price until its
equilibrium price of 10. There are incentives to trade on their private
information until equilibrium price. In this way, they make the market efficient.
However, anomalies (= things which we see in financial markets which are not in line with
theory, and do not quite know how to understand) began to be noticed. Key puzzle: volatility
in the financial market is way too high, with respect to the fundamentals.
Excess volatility
In the 1980s, academics realized that the EMH did not seem to hold for the aggregate stock
market. It did seem to hold for single stock, but if you aggregate them to a stock index, the
properties for the stock index did not line up well with the predictions of the EMH:
- changes in prices often occur for no fundamental reasons. Meaning, on days that
there is no news, there will still be some wild changes in prices. One could not make
sense of this considering theory.
- volatility of stocks is higher than the EMH predicts
According to the EMH, price == value. Then, the current share price Pt is the optimal
forecast of future dividends Dt+1:
(1)
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,We assume a zero discount rate wlog (without loss of generality). Here, price equals
expected future dividends.
Assuming rational expectations does not imply that the forecast is always right. There can be
a forecast error:
(2)
But, on average, the forecast is right. Meaning, the expected value of the error is 0. On
average, we make no mistakes.
(3)
The covariance of 0 (price and error) means that there is no correlation with public
information. If the covariance and price and error would not be equal to 0, this would e.g.
mean if prices increases, the error would also be positive. Meaning, some information which
is publicly available is not considered, and will end up in the error term. If you consider all
available information with EMH, the error should be uncorrelated with available information.
We take the variance of the LHS and RHS of (2):
(4)
The RHS is the sum of their variances. This is because their covariance is eliminated as it is
0, as said in (3).
Which implies:
(5)
Hence, dividends should be more volatile than prices. However, this is not the case as
dividends are famous for being sticky (they vary little). So, (5) is not verified in practice
whereas it is part of the EMH. Grossmand and Shileer (1981) find that:
- the link between stock prices and discounted dividends is weak
- price movements on the stock market imply an implausibly high level of risk aversion
Intuition: investors seem to require a huge risk premium for tiny variations in dividends. If you
see large stock price changes and tiny variations in dividends, investors require a large risk
premium for very little risk. Meaning, they are hugely risk averse. Meaning, (5) makes sense
in practice if individuals are super risk averse. However, this does not line up with actual
behaviour. Luckily, there came behavioral finance.
Behavioral finance
In the 1980s, the econometric analysis of prices and dividends:
- exposed the pitfalls of the EMH
- but did not say how to improve it
In the 1990s, however, research finally provided a theory update so we could explain the
new phenomena which contrasted the EMH.
Finance started to incorporate insights from cognitive psychology (= how we process
information) to understand investors’ decision making. We need to look if individuals are
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,prone to biases when incorporating information. We will focus on two main avenues of
research:
- feedback models
- obstacles to smart money
Feedback models:
The idea of feedback models is that of price-to-price feedback:
- upward-sloping demand curve:
- ∆P>0 -> ∆ Q > 0
- if the price of the stock increase, the demand of that stock also increases
However, this is not what a demand curve should look like, it is inverted. If the price goes up,
demand should go down as fewer people are willing to buy that asset.
The intuition of the feedback model is that an increase in price seems to:
- attract public attention
- promote word-of-mouth enthusiasm
- heighten expectations for further price increases
The reverse is true of a price decrease.
This creates a bubble: the price (P) deviates from the fundamental value (V):
Positive pubble: P > V (overpricing)
Negative bubble: P < V (underpricing)
The bubble will exist until the current market price is no longer sustainable. The price is no
longer sustainable (thus the bubble bursts and P goes back to V) when the price reflects the
beliefs of the investors. Meaning, no one thinks the price will change anymore.
For positive bubbles, the bubble bursts when it reaches the valuation of the most optimistic
investor:
P = Vmax
For negative bubbles, the bubble bursts when it reaches the valuation of the most
pessimistic investor:
P = Vmin
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, The presence of such feedback is also supported by research in cognitive psychology:
- Kahneman and Tversky (1979):
They showed that people try to predict on the basis of a recent pattern regardless of the
probability associated with it (representative heuristic). Investors tend to only focus on the
most recent trend in the asset price. They extrapolate this into the future. They think the
recent trend (very small piece of information) is representative of what is going to happen in
the future, and thus trade on it.
- Daniel, Hirshleifer and Subramanyam (1999):
The psychological principle of biased self-attribution can also promote feedback (if I get it
right, I trade more). Meaning, if I think the price will increase and I see that the price
increases, I am more likely to trade in the same direction again next period. Meaning, then,
we see the upward trend in prices and demand.
- Smith, Suchanek and Williams (1988):
In experimental markets (labs), we observe bubbles and crashes (over/underpricing) even
when two key ingredients are added: perfect information (= everyone has the same
information on what they are trading) and a finite time horizon. Typically, we thought that
bubbles existed because some investors knew more than others which led to inefficiencies.
Moreover, we thought that bubbles existed because people have different time horizons as
some people were more patient or risk averse than others. However, they showed that
bubbles and crashes still arise even when we took away the two main ingredients which we
thought caused them.
- Jegadeesh and Titman (1993):
Six-month winning (price increased) stocks beat six month losing stocks (price has
decreased) by 12% over the following year (momentum). If you can pick stocks based on
their past performance (short losers, long winners) and make large profits, market efficiency
is weak (efficiency is violated).
Smart money:
This is the subset of investors that have better information/skills than others, which enables
them to make smarter investments (hence the name). They are often referred to as
arbitrageurs.
The counterpart of arbitrageurs (sophisticated investors) is noise (or naive) traders. Naive
traders face short-sales constraints. They are unsophisticated investors. It is the subset of
investors who also trade for reasons other than fundamentals, such as:
- sentiment: optimism/pessimism
- word-of-mouth: they heard something going on about a stock
- liquidity
Smart money eventually corrects all the mispricing caused by naive traders. The
sophisticated traders trade at the expense of the naive traders. However, this is not
necessarily true. De Long, Shleifer, Summers and Waldmann (1990) find that smart money
may not work as expected. In particular, arbitrageurs may also:
- amplify this mispricing
- if they buy in advance of naive traders, they will cause a big price jump. But
the big price jump in the presence of feedback traders is going to attract a lot
of investors of unsophisticated investors of that stock. They think that that
increasing price will continue in the future (extrapolate short term trend). If
arbitrageurs see an overpriced stock and know it is overpriced, they can buy
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