In this paper an exhaustive characterization of financial markets was given.

**Dependence:** Autocorrelation in returns if largely insignificant. (Exceptions being at the tick level and annual returns.)
**Distribution:** Approximately symmetric, increasingly positive kurtosis as the time interval decreases and a power-law or Pareto-like tail.
**Heterogeneity:** Non-stationary (clustered volatility).
**Non-linearity:** Non-linearities in mean and (especially) variance.
**Scaling:** Markets exhibit non-trivial scaling properties.
**Volatility:** Volatility exhibits autoregressive conditional heteroskedasticity. Long-range dependence of autocorrelation, log-normal distribution, non-stationary, non-linear and scaling.
**Volume:** Distribution decays as a power law, also calendar effects.
**Calendar effects:** Intraday effects exist, the weekend effect seems to have all but disappeared, intramonth effects were found in most countries, the January effect has halved, holiday effects exist in some countries.

from *Characterization of Financial Time Series* by Martin Sewell.via maxdama

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Tags: algorithmic trading, calendar effects, dependence, distribution, distributions, education, heterogeneity, HFT, independence, kurtosis, non-linearity, probability distribution, scaling law, stat algo, stat arb, trading volume

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