研究論文

In most of modern finance, things tend to matter only if they can be measured precisely and frequently. By contrast, in alternative investment, that which matters most – performance – is remarkably difficult to pin down. Knowing what the precise performance is, of any fund, region or market, at any moment in time, is a complicated process, layered with judgement and requiring context. Perhaps this is why so much attention is paid to activity, sentiment and anecdote. But it is possible to understand more, if you have access to the right data and the patience to interpret it. One intriguing headline from our data could be: ‘China overtakes the US as the top performing VC market.’ However, much of that value is unrealized.

The profitability and investment potential into AI is not a contentious topic. But to what extent could AI disrupt and enhance the actual process of investing in alternatives itself?

We believe the scope for added value is significant, and growing quickly, both in terms of delegated investments and direct investment into high potential companies. This paper outlines some of the areas of investing into alternative investments (AI) where we think the application of artificial intelligence (AI) already makes sense and where it will become increasingly relevant in the future.

The quarterly movements of multiples of US LBO funds and major US stock indexes appear to be significantly positively correlated, and this observation is further amplified during the bullish market periods immediately before the markets contracted. US LBO funds exhibit consistently lower volatility of performance relative to the public market indexes. Fund managers need to see two successive quarters of strong and consistent shifts in public markets to reflect it significantly in their TVPIs. The main philosophy is to adjust portfolio values moderately but in line with the indexes, sometimes catching up with sustained strong movements.

Active venture capital funds made a very impressive closure of 2018, far exceeding historical averages. These high last-quarter returns remain associated with comparatively low risk and shorter time-to-liquidity, as VC funds capitalize on a favorable exit environment.

Most hurdle rates featured in private equity funds are set at 8% of internal rate of return. Although the origin of this specific figure is unclear, it is still largely applied to this day. It is of crucial importance for the fund manager: if this rate of return is not reached, then fund investors collect the full profits of the fund.

Our research indicates that private equity funds struggle to deploy capital at the same pace as they raise it, as distributions have outweighed capital calls by a significant and striking degree over the past five years.

This issue of FrontLine makes a comparison of return profiles and cash flow schedules of different types of LBO funds (small, medium, large and mega cap funds). The results of the analysis are in line with the well-established analogous findings from the public markets. Small caps perform better on average, but the large caps are better predictable and bear less risk for the investor.

To contribute to the ongoing debate about private markets “illiquidity premium”, this paper proposes a new approach to liquidity as a dimension of private equity investing, along with risk and return. Along those lines, the liquidity risk associated with private equity lies in the variation of holding periods over time. To assess this risk, an equivalent to holding periods can be approached by calculating an average time-to-liquidity, which is a function of multiples and IRRs.

Thanks to the high quality of the data provided by eFront Insight, it is possible to evaluate the average time-exposure of investors and estimate the variations around this average.