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Information Theory and Financial Markets

I would like discuss and implement ideas from papers applying information theoretical (IT) notions to trading in financial markets. I will provide links to all papers I'll read on this topic and describe certain concepts in more detail.

The current list is:

Untertainty analysis in financial markets: can entropy be a solution?, Andreia Dionísio, Rui Menezes and Diana A. Mendes (pdf)

Forecasting Foreign Exchange Market Movements via Entropy Coding, Arman Glodjo, Campbell R. Harvey (pdf)

Local order, entropy and predictability of financial time series, L. Molgedey and W. Ebeling (pdf)

These three papers all use Shannon Entropy in place of more traditional statistical measures. What is interesting however is that all of them apply entropy in different ways.
The first paper by Dionisio compares entropy as measure of uncertainty with variance/standard deviation in portfolio management.
The second paper by Glodjo applies techniques from coding theory (the original and most successful application of IT) to forecasting high frequency time series. Also it provides good arguments for using IT in finance.
The last paper by Molgedey is using conditional entropy directly on returns time series to quantify "local order" in highly stochastic time series. A local order would be a point in time where the next step is more predictable than average.

I will have a look at some of those techniques in more detail and might implement some of it to see if I can replicate the authors results.