The New Reality: Persistent Automation for Fund Management
Following is the first part in a two-part guest post from Branden Jones, Global Head of Marketing at Liquid Holdings Group, Inc. based in New York, NY.
This is the year for big data. Across industries, firms have unprecedented amounts of both public and private information sets – from user profiles and consumer habits to business outputs and proprietary algorithms. But access to data, or information at large, does not guarantee a valuable yield. Jonathan Shaw, managing editor of Harvard Magazine notes, “The [data] revolution lies in improved statistical and computational methods, not in the exponential growth of storage or even computational capacity.” Data is ubiquitous but not intrinsically valuable – it needs to be smartly processed, not just farmed.
For hedge funds, data processing is the quiet, invisible process that moves through the trade lifecycle—accessed from external entities like exchanges and brokers, modified and adjusted in execution, and at times, frozen in snapshots for an increasingly complex group of investors and regulators. More operational credibility and regulatory compliance is required than ever before, with increased scrutiny of the secret buy-side manna that goes along with it.
Smarter data management can be expensive and time-consuming as funds seek to keep up with regulatory, compliance, and transparency requirements while navigating through a sea of market opportunities. Good fund management starts and ends with precise, accurate data management. Truly taking advantage of data, and smarter computational methods, requires not only shedding the skin of outdated models, but categorically understanding a whole new data ecosystem, with new methods of processing, through selective automation and augmented observation. Once that new data ecosystem has been embraced, fund managers can spend their time mastering alpha generation and capital building initiatives.
While data management has historically been the purview of three separate functions (front-, middle-, and back-office), funds are now considering data inflows and outflows as simultaneous and holistic activities that not only govern market data and transparency capabilities, but also the capacity to be position-aware. This new viewpoint not only extends to in-house modifications, but will play an increasingly larger role amongst fund/service provider relationships. According to an Aite report from earlier this year, “…regardless of whether firms currently outsource or plan to outsource, the most common impressions of the benefits of using a single front- to back-office vendor for fund operations revolve around the attractiveness of holistic functionality, the expected contribution of a specialized vendor’s experience gained from other firms, and the vendor’s potential to better service clients.”
Essentially, funds are approaching operations as an ecosystem – instead of a train-like pipeline where only one train moves in one direction. The ecosystem houses converging cross-office data functionalities that are near-simultaneous activities, beyond the linear progression of the traditional lifecycle. Risk is moving to the front office. Portfolio management is constant. And compliance is everywhere. No longer do funds hand off a piece of paper from their trader(s), to the risk officer, over to compliance for the stamp of approval, call down to the floor to reconcile all activity, and then spend countless hours updating disparate systems and colleagues, and later investors, of the impacts on performance and risk. That is the pre-data model from the ‘80’s and 90’s – non-computational and hindered by actual human movement, where data moves in a single line, waiting in turn to be moved in and out of an outdated fund architecture by personnel who may or may not exist in today’s hedge fund reality.
The data map has changed – it’s time for a new hedge fund model.
Part 2: Be sure to read part two this article, which examines the new data model firms should look to leverage: one that supports processing, normalization, historical and defensive measures.
Photo Credit: Liquid Holdings