Big data is undoubtedly one of the big topics of the moment as society leaves bigger data trails than ever before. The efficiency with which we can capture information has also improved significantly which allows companies to build evermore accurate customer profiles. One of the big challenges is to separate the wheat from the chaff in order to derive useful information – data for its own sake is not all that meaningful, especially in a world of Twitter and Facebook.
Computing correlation coefficients is not an obvious big data topic. However, we show that such calculations can quickly become cumbersome and thus require big data applications. In this article we update our correlation screens with the aim to investigate significant shifts in 36 large asset classes. Eurozone equities and the euro, REITs, emerging markets (EM) and some commodity-sensitive equity markets have recently undergone such regime changes.
The future is now
Poor data and analytics were identified as weaknesses during the global financial crisis. When Lehman Brothers collapsed in 2008, financial regulators, private sector managers and customers were unable to assess quickly the extent of market participants’ exposure to Lehman or to explore quickly and fully how the vast network of market participants were connected to one another.
Dave Patterson looks at how big data can drive disruptive innovation in financial services, and how it is the established companies who have the biggest potential advantage.
Production cuts have been announced in recent months – above all in China – that have lent buoyancy to the aluminium price. So far, however, any capacities that were shut down have always been offset by new ones, with the result that Chinese production is on track to hit a record level. Because China is still exporting large quantities of aluminium, the global market also remains amply supplied. In our view a significant price correction is needed to restore the market balance.