FINS5513 Chap.7 Market Efficiency and Behaviour
Market Efficiency and Behaviour
Week 7 changes the question from how to construct a portfolio to whether information can support repeatable abnormal returns. Weak-form efficiency says current prices reflect information in past prices and trading data. Semi-strong efficiency extends the set to all public information, while strong-form efficiency includes public and private information.
These forms are nested claims about an information set; they are not labels for how quickly a stock price moved on one day. Empirical tests need a joint model of normal return. An event study estimates a benchmark relation, calculates abnormal return as actual minus expected return, and aggregates abnormal returns across time or securities.
A measured abnormal return can therefore reflect market inefficiency, a poor expected-return model, data problems or chance. Evidence on post-announcement drift, momentum and reversals motivates both risk-based and behavioural explanations. Behavioural finance studies mechanisms such as overconfidence, representativeness, conservatism, loss aversion and mental accounting.
Limits to arbitrage explain why sophisticated traders may not instantly remove mispricing when positions carry fundamental risk, implementation costs, short-sale constraints and noise-trader risk. Technical trading rules must be assessed after realistic costs and with safeguards against data mining. Efficiency is best treated as a disciplined, testable benchmark rather than a claim that prices are always correct.
What this chapter covers
- 01
Weak, semi-strong and strong efficiency classified by information set
- 02
Joint-hypothesis problem in every test of abnormal performance
- 03
Event-study estimation windows, event windows and benchmark returns
- 04
Abnormal return and cumulative abnormal return calculations
- 05
Momentum, reversals and post-announcement drift as empirical patterns
- 06
Overconfidence, representativeness, conservatism and loss aversion
- 07
Limits to arbitrage, noise-trader risk and implementation constraints
- 08
Technical rules evaluated after costs and out-of-sample validation
Worked example · free
Calculate an event-day abnormal return
- +1Insert the event-day market return into the estimated benchmark: 0.2% plus 1.10 times 2.0%.
- +1The benchmark expected return is 2.4% for that day.
- +1Abnormal return is actual return minus benchmark return, so 4.1% minus 2.4% equals positive 1.7%.
- +1The security outperformed this benchmark on the event day, but one observation cannot establish persistent inefficiency or causation.
Key terms
- Weak-Form Efficiency
- The claim that prices already reflect past price and trading information.
- Semi-Strong Efficiency
- The claim that prices already reflect all publicly available information.
- Strong-Form Efficiency
- The claim that prices reflect public and private information.
- Abnormal Return
- Actual return minus the return predicted by a stated benchmark model.
- Event Window
- The period over which returns associated with a specified event are measured.
- Limits to Arbitrage
- Risks and frictions that can stop rational traders from eliminating apparent mispricing.
Market Efficiency and Behaviour FAQ
Does efficiency mean every price is correct?
No. Efficiency is a claim about whether available information permits systematic abnormal profit after risk and costs. Prices can move, contain estimation error and later appear wrong without automatically creating an exploitable strategy.
Which form concerns public announcements?
Semi-strong efficiency concerns all public information, including announcements and published accounts. An event study of a public announcement therefore most directly tests a semi-strong implication, conditional on its benchmark model.
Why is every efficiency test joint?
Abnormal return cannot be observed without a model of normal return. Rejection might indicate inefficient pricing, but it might instead reveal an incomplete factor model, unstable beta or another benchmark error.
What is cumulative abnormal return?
It is the sum of abnormal returns across a chosen event window. The measure captures adjustment spread over several periods, but the window must be justified to avoid mixing the event with unrelated news.
How can mispricing survive rational trading?
Arbitrageurs face funding limits, short-sale restrictions, costs, fundamental uncertainty and the risk that mispricing widens before converging. Those constraints can make a seemingly attractive correction trade dangerous or infeasible.
How should a trading rule be evaluated?
Specify the signal before testing, reserve an out-of-sample period, include turnover and transaction costs, compare against a risk-adjusted benchmark, and avoid selecting only the best rule from many historical trials.
Exam move
Begin classification questions by underlining the information set. Past prices point to weak form, public news points to semi-strong form, and private information points to strong form. Do not classify a claim merely from the words fast or slow.
For event-study calculations, write the benchmark equation, substitute the market or factor returns, compute expected return, subtract it from actual return and then aggregate only over the stated window. In interpretation, include the sign, magnitude, benchmark and limits of inference. Build a two-column evidence table for anomalies: one column for a risk or data explanation and another for a behavioural mechanism.
Learn biases through decision patterns rather than isolated definitions. Overconfidence can create excessive trading, conservatism can delay belief updating, representativeness can encourage extrapolation, and loss aversion can make losses feel disproportionately painful. For technical strategies, insist on an implementable signal, a realistic cost assumption and an out-of-sample test.
Finish longer answers with the joint-hypothesis problem and limits to arbitrage. That structure shows why an empirical pattern is evidence to assess, not an automatic declaration that markets fail.
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