Thursday, February 9, 2017

(Interesting Books) Why Nations Fail (2013) Acemogly & Robinson

A thought provoking read highlighting why "trade-test innovation" is at the heart of economic
success when states are "inclusive" rather than "extractive". That an organized and centralize state may be a critical component for economic success but that the centralization must avoid the path to extractive economics by an elite, and balance the need for centralization against interests of societies members. That the incentives created by inclusive organization seem to generate the associated trade-test innovation necessary for the creative destruction that has historically delivered economic growth. Creative destruction that perpetually threatens the controls by the elites of any organized and centralized state, but through trade and innovation creates the opportunities for a better and more prosperous life for those in broader society.

Why Nations Fail (2013) ACEMOGLU AND ROBINSON


" ... Why Nations Fail answers the question that has stumped the experts for centuries: Why are some nations rich and others poor, divided by wealth and poverty, health and sickness, food and famine?

Is it culture, the weather, geography? Perhaps ignorance of what the right policies are?

Simply, no. None of these factors is either definitive or destiny. Otherwise, how to explain why Botswana has become one of the fastest-growing countries in the world, while other African nations, such as Zimbabwe, the Congo, and Sierra Leone, are mired in poverty and violence? ..."

SAFA 2017 Talk : 18-20 January 2017 Cape Town, South Africa

Title: Learning zero-cost portfolio selection with pattern matching for intraday trading

Speaker: Tim Gebbie

Authors: Tim Gebbie, Fayyaaz Loonat

Abstract: We consider and extend an adversarial agent-based learning approach to the situation of zero-cost portfolio selection in the domain of quantitative trading. 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. We demonstrate 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. We show that these types of machine learning algorithms are well suited for intra-day quantitative trading.