Understanding the Value of Big Data
While the markets have delivered for investors in the past five years, it’s fair to say hedge funds have lagged. Hedge funds captured less than a third of the S&P 500’s return (posting just a 47% total return since Q1 2009 while the S&P has returned 156%) and funds-of-funds have performed even worse, annualizing at 5%. Consequently, it’s understandable investors are giving hedge fund fee structures some scrutiny these days: How can hedge funds justify their fees when they’re so badly trailing the performance of index funds?
The answer used to be obvious: with a hedge fund, investors got access to superior managers, top-rate market intelligence, and investing strategies that traditionally paid off handsomely. Yet today, rather than improving their product, hedge funds are caught up in the cycle of reducing fees and shifting to more liquid, “beta” alternatives. Their focus on transparency, while laudable, finds them trying to explain real-time outcomes instead of using data to reveal and isolate the key drivers ofreturns and eliminating or reducing behaviors that diminish returns.
The same problem exists for investors – while hedge funds battle with their fee structures, institutional investors face the difficult challenge of identifying truly skilled managers from an increasingly large pool of funds. The number of hedge funds has more than doubled since 2000, naturally making it more challenging for investors to make the right allocation-decisions. This massive growth in the number of funds coupled with regulatory changes aimed at increasing transparency means that there’s now a vast amount of information at the hands of investors. More information for investors is great, yet with little or no infrastructure in place to sort through the noise in search of the signal, the use of this new information is underutilized or outright ignored. Therefore, it’s not a data problem as much as it’s a processing one.
Without the right tools and technologies to process all of this information, too many institutional allocators are still operating on “gut instinct” and focusing on return-based analysis. This strategy was fine in the early ‘90s, but it’s a foolhardy way to invest in today’s data-flooded marketplace. Failing to take advantage of the massive amount of information accessible to investors is essentially the same as a portfolio manager investing in a company without looking at the financials.
The reality is that many investors are stuck in a pre-big data world. They ignore critical amounts of data because it’s simply easier to do so or because they’re overwhelmed by the volume of data coming in. Some investors deploy a handful of analysts that enter data into a set of disconnected spreadsheets that produce static reports, which likely offer little insight into the overall health of their portfolios. The result? Poor allocation decisions are made with limited and delayed information, ultimately leading to poor performance.
Neither institutional investors nor hedge funds should be in the data-crunching business: they need to be in the business of investing. Success in the latter is a question of having an information edge. Today’s analytics-based technologies can provide investors and hedge funds with the foundation they need to help them beat the odds and outperform in the long run. These technologies can empower investors with an intelligence-rich information edge that allows them to make better decisions that ultimately result in higher, more consistent and predictable returns.
By outsourcing data management and analytics to the right third-party providers, investors can gain access to granular data on thousands of managers, within a framework that allows them to isolate the components of how returns are generated as opposed to just reporting or calculating them. For example, the framework we use at Novus to help our clients analyze their manager’s breaks down how managers generate return into five measurable components, which can help investors identify persistent manager skill-sets:
- Exposure Management: How exposures are managed in relation to the market.
- Category Rotation: How the manager allocates to different areas of the market.
- Idea Selection: What names the manager chooses to own within those areas.
- Position Sizing: How the manager sizes trades.
- Tactical Trading: How often (and correctly) the mangers moves things around.
Now imagine a portfolio that consistently employs each component within this framework into the due diligence and portfolio management lifecycle – this is what data-driven, intelligent investing looks like. Whether investors decide to use Novus’s framework or their own, investors need to begin the transformation from a relationship-based investment landscape to a data-driven, analytical, and predictive investment business.
Employing such a framework that’s powered by a machine capable of consuming exposure data, position-level information, return/attribution data, public regulatory filings, and more across an entire portfolio is the next frontier of institutional asset management. Investors that choose to embrace such technologies will see a drastic improvement in the visibility and predictability of the components driving their managers’ returns and increase their odds of making better allocation decisions. Investors that continue to operate based on their gut-feelings or historical performance-based research may get lucky in the short-term, but will likely underperform in the long-run, only now, with lower fees.
The winning investors of the next decade have already began this transformation. They invest confidently with predictable intelligence at their fingertips fueled by a modern data and analytics infrastructure. These investors will not attribute their success to having better fee structures than their peers, or shorter lock-up periods. They’re going to credit their success with their ability to quickly and persistently examine the skill-sets of their managers, identify market trends, effectively allocate their capital, and efficiently execute their allocation decisions. After all, the best way to minimize the cost of mistakes is not to make them in the first place.
Basil Qunibi is the founder and CEO of Novus, a portfolio analytics and intelligence platform for institutional investors. He started his career at Merrill Lynch where he was responsible for over $4 billion in allocated capital across the chemicals, energy, metals ... View Full Bio
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