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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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• January barometer

鱼跃加速器最新版-快连加速器app

• Wikipedia: Calendar effect

鱼跃加速器最新版-快连加速器app

• SIEGEL, J.J., 2002. Stocks for the Long Run. books.google.com. [雷霆加速器ios永久免费] (74.11/year)
• THALER, R.H., 1992. The winner's curse. books.regehr.org. [ios免费国外加速器] (24.12/year)
• SCHWERT, G.W., 2003. Anomalies and Market Efficiency. [旋风加速器最新版官网] (18.12/year)
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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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• FRANGAKIS, C.E. and R. VARADHAN, 2002. Confidence intervals for seasonal relative risk with null boundary values. 免费加速器ⅴpn [永久免费加速加速器] (1.16/year)
• MILLS, T.C., et al., 2000. Seasonality in the Athens stock exchange. Applied Financial Economics. [Cited by 7] (1.11/year)
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• CLEVELAND, W.S. and S.J. DEVLIN, 1980. Calendar Effects in Monthly Time Series: Detection by Spectrum Analysis and Graphical Methods. Journal of the American Statistical Association. [Cited by 21] (0.80/year)
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• CLEVELAND, W.S. and S.J. DEVLIN, 1982. Calendar Effects in Monthly Time Series: Modeling and Adjustment. Journal of the American Statistical Association. [旋风加速器ios] (0.62/year)
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• THALER, R.H., 992). Calendar Effects in the Stock Market''in Richard H. Thaler, The Winner's Curse. Princeton: Princeton University Press,. [Cited by 1] (0.00/year)
• THALER, R.H., 1992. Calendar Effects in the Stock Market. [旋风加速器最新版官网] (0.07/year)
• MABERLY, E.D. and D.F. WAGGONER, 2000. Closing the Question on the Continuation of Turn-of-the-month Effects: Evidence from the S & P 500 …. papers.ssrn.com. [not cited] (0/year)
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• HOLDEN, K., J. THOMPSON and Y. RUANGRIT, European Journal of Operational Research (forthcoming 2004). 手游加速器哪个好用？8款好用的手游加速器下载推荐 - 知乎:2021-4-26 · 极速手游加速器是首款安卓手游网络加速器，极速手游加速器闪亮登场。网络加速神器，重度交互，实时网游必备。 软件特色 手机上的迅游，永久免费！ 1.首创的手机网络加速技术，支持所有热门网游加速，让PK，团战更流畅;. [Cited by 3] (?/year)
• CHELLEY-STEELEY, P.L., 1995. Calendar Effects and the Pricing of Risk.: the UK Evidence. The European Journal of Finance. [蚂蚁海外加速器永久免费版] (0.09/year)