Calendar effects (sometimes less accurately described as ‘seasonal effects’) are cyclical anomalies in returns, where the cycle is based on the calendar. The most important calendar anomalies are the January effect and the weekend effect. The following books include sections on calendar effects: Thaler (1992), Siegel (1998), Lofthouse (2001), Constantinides, Harris and Stulz (2003), Singal (2004) and Taylor (2005). Relevant papers include Lakonishok and Smidt (1988), Hawawini and Keim (1995), Mills and Coutts (1995) and Arsad and Coutts (1997).
Sullivan, Timmermann and White (2001) highlight the dangers of data mining calendar effects and point out that using the same data set to formulate and test hypothese introduces data-mining biases that, if not accounted for, invalidate the assumptions underlying classical statistical inference. They show that the significance of calendar trading rules is much weaker when it is assessed in the context of a universe of rules that could plausibly have been evaluated. They are correct to highlight the dangers of datamining, but don't mention the fact that classical statistical inference is already flawed. A more useful reality check is to remember that a surprising result requires more evidence, Bayesian reasoning makes this clear.
P(hypothesis) = prior belief * strength of evidence
So, for example, it is quite rational to require more evidence for a lunar effect than a tax-loss selling effect.
Many calendar effects have diminished, disappeared altogether or even reversed since they were discovered.
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Sullivan, Timmermann and White (2001) are correct in what they say, but don't mention the fact that classical statistical inference is already flawed.
A more useful reality check is to remember that a surprising result requires more evidence.
Bayesian reasoning makes this clear:
P(hypothesis) = prior belief * strength of evidence
So, for example, it is quite rational to require more evidence for a lunar effect than a tax-loss selling effect.
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