The Wall Street Journal published an article titled “Wall Street’s Insatiable Lust: Data, Data, Data”. This article focused on the data mining and pattern identification practices that financial services and investment management firms utilize in order to make effective investment decisions.
Presently, the “quest for yield” is a common expedition for asset managers of all sizes—large enterprises and boutique firms—and of all investment strategies—growth, value, and long/short hedging practices; client demand for beating the markets, particularly when speaking of institutional clients, is strong, and managers are in need of any sort of edge they can get in order to maximize returns. Bloomberg and FactSet, while requisite, are no longer enough for research analysts to gain an edge on the competition.
“Data hunters” search for, and utilization of, alternative and indirect sources of data can be time-consuming and is not a practice of instant gratification—combing through large sets of abstract data is laborious and can leave a “hunter” with only a small amount of telling information. But when taken into an aggregate of all data analyzed, these small ( and dare I say, nonmaterial) conclusions can add up to something very significant and rewarding. For those folks who are versed in equity research and valuation, the CFA Institute calls this concept the “Mosaic Theory”.
But while investors are demanding yield, and while crunching abstract data sounds like a (more) surefire way to achieve strong returns, these practices are not feasible, nor wise, for all investors. In fact, I would argue that the practices described in the article are best reserved only for firms like the hedge funds it mentions.
My rational behind this sentiment is twofold; for one, “data hunting” or data mining is a costly venture, both in terms of time and money. Firms must attract employees who have great experience and knowledge in handling complex data sets and applying advanced statistical concepts to them in order to extract useful information (if any exists). These types of people—“quants”, mathematicians, or data scientists—don’t necessarily come cheaply when it comes to salary negotiation. Secondly, some of the data that is utilized in these practices is highly subjective, and the application of the extracted information in investment decisions can just as easily lead to great capital loss as it can lead to capital appreciation. Hedge funds are inherently risk-loving operations with a preexisting affinity for quantitative analysis, already possess the talent to engage in these practices, and are more accustomed and familiar with tremendous capital loss.
So in closing, I think the greatest part about data mining’s presence within the investment world is more of what it signals about the evolving thought processing of investors; analysts are becoming more abstract in reasoning, and they are looking beyond the income statement and balance sheet for answers. But just as its taught in microeconomic principles, the opportunity cost for pursuing those answers is just too high for too many people.
Article Source: http://www.wsj.com/articles/wall-streets-insatiable-lust-data-data-data-1473719535