BFF5520 Chap.4 Market Efficiency
Market Efficiency
Market efficiency concerns whether available information is incorporated quickly enough to prevent predictable risk-adjusted abnormal return after realistic costs. It does not require equal expected returns, perfectly accurate prices or individually rational investors. Testing efficiency requires a stated information set, return benchmark, trading rule, event or holding window, and implementation costs.
Event studies measure abnormal performance around the first public information date, while strategy studies must confront factor exposures, turnover, borrow fees, data mining and the joint-hypothesis problem.
What this chapter covers
- 01
Information sets
- 02
Expected and abnormal return
- 03
Event-study windows
- 04
Benchmark estimation
- 05
Long-short strategies
- 06
Momentum construction
- 07
Trading frictions
- 08
Joint-hypothesis problem
Worked example · free
Bridge raw return to an implementable residual
- +1Begin with the raw spread of 0.85% per month.
- +1Subtract 0.40% of benchmark compensation to obtain 0.45%.
- +1Subtract 0.24% turnover cost and 0.12% borrow cost.
- +1Residual = 0.85% - 0.40% - 0.24% - 0.12% = 0.09% per month.
- +1Conclude that the tradable unexplained return is much smaller than the raw spread and remains model dependent.
Key terms
- Market efficiency
- Market efficiency is the condition under which available information does not support predictable net risk-adjusted abnormal profit.
- Abnormal return
- Abnormal return is realised return minus benchmark-predicted normal return for the same period.
- Event window
- An event window is the interval over which abnormal return is attributed to a defined information event.
- Long-short portfolio
- A long-short portfolio buys one group and shorts another to isolate a return spread with low net investment.
- Data mining
- Data mining is specification search that selects apparent patterns after observing the same data used to evaluate them.
- Joint hypothesis
- The joint-hypothesis problem means an efficiency test simultaneously tests the benchmark used to define normal return.
Market Efficiency FAQ
Do efficient markets imply equal expected returns?
No. Correctly priced securities can offer different expected returns because they carry different systematic risks. Efficiency concerns abnormal return relative to a suitable benchmark, not equality of raw returns.
What does an event study measure?
An event study estimates normal return outside the event period, calculates abnormal return around the first public information date, and aggregates the response. Leakage, anticipation, confounding announcements and window choice must be considered.
Why are trading costs central to efficiency?
A paper return that disappears after turnover, bid-ask spreads, market impact or shorting costs is not an implementable abnormal profit. Efficiency claims concern opportunities available after realistic execution.
What is the joint-hypothesis problem?
Every abnormal-return test requires a model of normal return. A rejection can mean prices are inefficient, the model omits a priced risk, or both. Changing the factor benchmark can therefore change measured alpha.
How can a backtest avoid data-mining bias?
Specify the rule before evaluation, retain a genuine holdout period, report unsuccessful variants, adjust inference for repeated search and test performance after all implementation costs. A selected in-sample winner is not independent evidence.
Exam move
Organise every efficiency answer around five items: information set, benchmark, trading rule, cost and conclusion. For event studies, identify the first public date, estimation window and event window before interpreting price movement. For return strategies, distinguish raw return, factor compensation and net residual, then ask whether the design was specified before seeing the sample.
Pair the chapter with behavioural finance: a bias can exist without an exploitable trade, while a return pattern can reflect risk rather than irrationality. Avoid absolute claims and state the benchmark dependence explicitly. Build an event-study timeline that separates the rumour date, first public announcement, trading halt, estimation window and post-event window.
Write what leakage, anticipation and delayed reaction would look like on that line before reading a return chart. For an anomaly claim, prepare a raw-to-net bridge: subtract risk-model compensation, turnover costs, bid-ask spreads, shorting expense and realistic implementation delay. Preserve the distinction between statistical rejection and an investable profit.
Use a holdout test when comparing signals: define the ranking rule on one period, freeze it, and evaluate it on unseen observations. Record how many strategies were tried, because a single surviving result means less after extensive search. Practise the joint-hypothesis critique in one precise sentence: abnormal return rejects the combination of efficiency and the expected-return model, not market efficiency alone.
Conclude by specifying which information set appears incorporated too slowly or too quickly, under which benchmark, and whether the evidence remains after feasible costs.
Working through Market Efficiency in BFF5520? Sia is AskSia’s AI Finance tutor — ask any BFF5520 Market Efficiency question and get a clear, step-by-step explanation grounded in how BFF5520 is taught and assessed. Read this chapter free, then take your hardest questions to Sia.
