Networks meet Finance in Python - July 27 2014
This the repository of the talk of the same name.
The talk was supported by some IPython notebooks which you are welcome to try out. To get a feeling of what is in there, you can take a look at the static version. For running the widgets and playing with the data, you'll need an IPython server running, though.
I am using anaconda as a distribution and following packages
planarity (installed through pip, if you have problems installing it on Mac OS take a look at my fork)
The main inspiration for this talk is from blog posts
and following papers:
- for the main results
Spread of risk across financial markets: better to invest in the peripheries F. Pozzi, T. Di Matteo and T. Aste 2013, Nature Scientific Reports 3, Article number: 1665 doi:10.1038/srep01665 http://www.nature.com/srep/2013/130416/srep01665/full/srep01665.html
- for looking into historical market correlations
Quantifying the Behavior of Stock Correlations Under Market Stress Tobias Preis, Dror Y. Kenett, H. Eugene Stanley, Dirk Helbing & Eshel Ben-Jacob 2012 Scientific Reports 2, Article number: 752 doi:10.1038/srep00752 http://www.nature.com/srep/2012/121018/srep00752/full/srep00752.html Temporal Evolution of Financial Market Correlations Daniel J. Fenn, Mason A. Porter, Stacy Williams, Mark McDonald, Neil F. Johnson, Nick S. Jones http://arxiv-web3.library.cornell.edu/abs/1011.3225?context=physics
- for considering exposure networks
Early-warning signals of topological collapse in interbank networks Tiziano Squartini, Iman van Lelyveld & Diego Garlaschelli 2013 Scientific Reports 3, Article number: 3357 doi:10.1038/srep03357 http://www.nature.com/srep/2013/131128/srep03357/full/srep03357.html
You might wish to have a look at Yves Hilpisch's talk and slides, as goes through some of the financial concepts I mention.