Shannon entropy, $H(X) = -\sum_{i=1}^n p(x) \ln p(x)$ is a probabilistic measure of randomness or disorder within a random variable's probability distribution or histogram.
If we take rolling window segments, or snapshots, of a full-sample time series of stock returns $X$, can we expect the Shannon entropy of these windows to consistently increase or decrease as $t\rightarrow \infty$? i.e. each window has its own pdf.
or would their entropy merely be a function of the volatility (clustering) in each window, given that the spread of a distribution (volatility) and its randomness (entropy) are inextricably linked?
In thermodynamics, time entropy naturally grows with time, so mustn't there be a connection between the Shannon entropy and time entropy of a stock's returns?
How about the entropy of stock returns using expanding windows instead?