Live long and think mathematically

http://www.nytimes.com/2016/04/25/opinion/the-mathematicians-90th-birthday-party.html?_r=0

Beyond inequality

Beyond inequality, this talk on Europe-in-crisis also resonates concerns from developing economies. Notably, the speakers clarifies his view as to what he considers to be fundamental signatures of the current crisis. His tax  innovations proposals are interesting and tuned for Greece. Analogously, there could innovations to address SA crisis issues which emanate from the sources where small changes in thinking and action can make a big difference in socio-economic evolutions.

Video: Yanis Varoufakis,  "And the Weak Suffer What They Must?" (Talks at Google)

Final....

Learning zero-cost portfolio selection with pattern matching

Tim Gebbie, Fayyaaz Loonat

(Submitted on 15 May 2016)

We consider and extend the adversarial agent-based learning approach of Gy{\"o}rfi {\it et al} to the situation of zero-cost portfolio selection implemented with a quadratic approximation derived from the mutual fund separation theorems. The algorithm is applied to daily sampled sequential Open-High-Low-Close data and sequential intraday 5-minute bar-data from the Johannesburg Stock Exchange (JSE). Statistical tests of the algorithms are considered. The algorithms are directly compared to standard NYSE test cases from prior literature. The learning algorithm is used to select parameters for agents (or experts) generated by pattern matching past dynamics using a simple nearest-neighbour search algorithm. It is shown that there is a speed advantage associated with using an analytic solution of the mutual fund separation theorems. It is argued that the expected loss in performance does not undermine the potential application to intraday quantitative trading and that when transactions costs and slippage are considered the strategies can still remain profitable when unleveraged. The paper demonstrates that patterns in financial time-series on the JSE can be systematically exploited in collective but that this does not imply predictability of the individual asset time-series themselves.

Comments:	28 pages, 21 figures
Subjects:	Computational Finance (q-fin.CP); Portfolio Management (q-fin.PM); Trading and Market Microstructure (q-fin.TR)
Cite as:	arXiv:1605.04600 [q-fin.CP]
	(or arXiv:1605.04600v1 [q-fin.CP] for this version)

SVAN 2016 : IMPA : Brasil : "Detecting intraday financial market states using temporal clustering": Dieter Hendricks, Tim Gebbie, Diane Wilcox

IMPA from March 28 to July 1, 2016 Rio de Janeiro (Brazil)

http://svan2016.sciencesconf.org/

http://svan2016.sciencesconf.org/conference/svan2016/pages/07b_DieterHendricks.pdf