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Strategic Insights

Alternative Alpha

This report explores the challenges facing active asset managers, describes our proprietary framework to analyse alternative data, and considers how managers can extract revenue and cost benefits from harnessing such data.
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Executive Summary

In recent years the investment management industry has witnessed a dramatic and sustained shift in investor preferences, with a flight of assets from active to passive funds. Much of this has been driven by widespread underperformance of active managers, particularly net-of-fees. In response, investors are voting with their feet, with USD 340 billion of net outflows from active US funds alone in 2016.
Active managers are consequently finding themselves in the midst of a perfect storm. Fund outflows, together with ongoing fee pressures, are weighing on revenues, while increased regulations – particularly the go-live of MiFID II in January 2018 – are driving up costs. In response, many active managers are introducing passive products (particularly exchange traded funds (ETFs)) and are undergoing a wave of M&A activity in an effort to capture economies of scale, deliver cost savings, and preserve margins. However, we see this race- to-the-bottom sitting at odds with their core value proposition and fundamental fiduciary duty: alpha- generation.
In a world where traditional financial information is ubiquitous and where investment analysis remains largely homogenous, we believe alternative data provides a critical avenue by which active managers can look to stay relevant. While still in its infancy, we see alternative data having profound implications for buy-side players that are able to effectively leverage its use, both from a revenue and cost perspective. In order to do this, a number of key factors must be considered, from identifying the right data to its practical incorporation in a fund manager’s investment process.
Not all alternative data, however, has alpha- generating potential. As such, there will be a need for managers to evaluate both the data type and its source across a number of criteria, such as its uniqueness and quality, in order to identify the most appropriate data to use. Moreover, internal capabilities need to be evaluated with respect to resources needed to both procure and analyse the data, including the potential use of machine learning techniques, which has major implications for cost reduction and efficiency, especially in a post-MiFID II compliance-driven world. An effective strategy is needed.
Given ongoing revenue and cost headwinds, we see active managers who continue to operate under traditional business models stand to see their profit margins compressed from an industry average of 40% at present to 25% by 2022.
Through successfully leveraging alternative data, we believe profit margins for leading managers have the potential to reach 55%. For every USD 100 billion of assets under management (AuM), this translates to a profit uplift of USD 100 million. With such a compelling case around its adoption, we believe it is time for active managers to seek out alternative alpha.

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