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Finance FHS Tutorial 4 Answers
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- Finance
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- University Of Oxford
Finance FHS Tutorial 4 Answers: Behavioral finance
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Finance Tutorial 8Question 1
What is the rational expectations hypothesis when applied to expectations about fundamental
processes like earnings growth? Relate it to Bayesian updating. Describe two methods of testing the
rational expectations hypothesis.
Rational expectations hypothesis would mean that in response to new information regarding a firm and
other external factors affecting it (eg. Macroeconomic or regulatory factors), the rational agent (the
investor or the analyst) updates their probabilistic beliefs of future earnings growth paths that the firm
might take.
This can be expressed in the form of Bayesian updating:
P(ehigh|good news) = [P(good news|ehigh) × P(ehigh)]/P(good news)
Assuming that there are only 2 possible earnings growth paths: high future earnings growth
(ehigh) and low future earnings growth (elow)
A similar equation can be obtained with P(e high|bad news) if bad news is received
Under Bayesian updating, expected future earnings growth changes in response to new
information. If expectations are rational, adjustments should not be excessive (overreaction) or
too mild (underreaction)
Two methods of testing the rational expectations hypothesis
1. Conduct regression analysis on forecast errors (dependent variable) given observed expectation
reactions to news (independent variable). If expectations are rational, there should be no
statistically significant relation between these variables, since there should be no predictable
forecast errors as of the time of forecasting given rational expectations.
a. Conduct regression analysis: (earningst+h/earningst)1/h – LTGt = a + b(LTGt – LTGt-k)
b. Values of h and k can be varied for different time periods of expectations and forecast
errors
c. Empirically, we find statistically significant negative coefficient ‘b’ suggesting predictable
overreaction to news
2. Experimental behavioural economics approaches in controlled incentivised game environments
to uncover biases in expectations formation
Rational expectations: forecast errors are unpredictable (zero on average)
Bayesian updating: update beliefs to maintain rational expectations.
Example:
Rational expectation: initially expect that the distribution of exam grades this year will be
roughly the same as last year
Bayesian updating: we notice a higher absence rate for lectures this year, so exam grades will be
poorer
2 methods of testing rational expectations hypothesis
(1) Predict forecast errors from new data on price and return
o Empirically, we find that they are predictable (against rational expectations)
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