Follow the Money: A Patchwork Data Trail

by Preqin

  • 21 May 2019
  • PE
  • VC
  • HF
  • PD
  • RE
  • INF
  • NR

In this extract from our Don’t Believe the Hype whitepaper, we discuss the challenges of a lack of regulatory reporting requirements in the industry and how this can be overcome.

Knowns, Unknowns and Known Unknowns
Although most alternative assets industry participants have few or no regulatory reporting requirements, there are some known sources of information on which data providers and commentators rely. One of the largest is US-based public pension funds, which are required to release quarterly updates on their fund portfolios and the returns they are seeing from them. Given that many of these pension funds are very large institutions with expansive portfolios, this provides fund-level information on a sizeable proportion of the active fund universe.

The difficulty with the information they provide is that it (understandably) specifically relates to the returns those pension funds are getting from their investments. Being large and influential investors, public pension funds are often able to negotiate reduced fee rates, or access to sidecar structures and co-investment opportunities. This may mean that the returns a large public pension fund gets from a fund are different from the returns seen by a small wealth manager, or even another public pension fund that signed a different Limited Partner Agreement (LPA).

It is also challenging in that fund managers are not required to disclose every detail of their fund’s activities to investors. They will notify the investor of capital call-ups and distributions, but may not be bound to disclose the levels of leverage they use to augment their buying power, or the use of credit lines to regulate the cash flow of the fund. Where they do disclose details to investors, they may ask that the investor keeps the information confidential. There is some data, in fact, that might never be obtained through impersonal sources, as it will never appear in filings, in the news or in investor statements.

Another important source is government-mandated regulatory repositories. Bodies such as the SEC in the US and Companies House in the UK require funds above a certain size to register with them and submit filings on their fundraising activities. While this provides detailed information on new funds coming to market, there is no centralized categorization or search function for these filings, posing a challenge for those looking to aggregate all the available information. Notices announcing the formation of new alternative investment vehicles or holding companies can easily get buried among thousands of other regulatory filings for other corporations or financial products.

This is also not a globally applicable repository of information. Regulations regarding investment vehicles and their reporting requirements differ widely between countries and are further complicated by funds that are headquartered in one jurisdiction but domiciled in another. There is no single agreed-upon standard for reporting from either fund managers or investors, despite initiatives spearheaded by industry bodies such as the Institutional Limited Partners Association (ILPA)4 suggesting best practice. This means that the level of information provided to investors, and in turn to the public, can vary widely depending on the particular structure and location of the fund manager in question.

The Rise of the Machines
The most common way to collect this information, where it is available, is to use manual and algorithmic methods to extract quantitative data from news articles, regulatory filings and public pension statements. Machine learning and artificial intelligence tools can increasingly automate this process, being able to scrape web pages to extract the numerical data from text-based reports. Most data providers, including Preqin, are investing in these technologies to speed up the collection process and reduce the reliance on manual research and collection.

However, there are some key problems with this approach. The first is conformity. Assuming that web scraping programs are able to collect 100% of the available information, all companies employing such tools will have a ‘complete’ dataset that is indistinguishable from its peers – leaving nothing to differentiate between providers. It can also be prone to errors of extraction. An algorithm cannot account for every way that information can be presented or reported and is not able to read the full report and understand a number in its context. This is why all the information we collect from web scraping programs is cross-checked and curated by human researchers with an understanding of the industry and the dataset.

Algorithms are also unable to collect information held in non-public sources. Some investors, for example, have more information on their portfolios than they release publicly. In the US and UK, though, that information can still be accessed by making Freedom of Information Act (FOIA) requests to the investors concerned – something a machine cannot replicate.

Lastly, there is the significant challenge of corroboration. Most web scraping algorithms do not have sophisticated means of cross-checking information to ensure its accuracy, especially when information may come from different kinds of sources. Where possible, it is more timely and accurate to collect information from fund managers and investors themselves, which can then be corroborated both internally and with external sources to ensure its veracity. Preqin, for instance, encourages fund managers to submit their performance and fundraising information on a monthly or quarterly basis, ensuring that information on our database remains timely and up to date. More than 10,000 alternative assets funds submit their data to us on this basis – representing more than 25% of the known fund universe and more than 60% of its estimated market capitalization.

Most importantly, though, some of the most valuable and actionable information is simply not quantifiable from news sources and regulatory filings. We prioritize direct contact with fund managers and investors precisely because so much granular data on their activities can only be collected this way. It allows us to collect qualitative information on investors’ intentions for the next 12 months, their active mandates and their concerns with the industry. It allows us to find out what trends fund managers are seeing regarding their investors, what challenges they are facing, and where they perceive the best opportunities over the coming months.

"Every year we have direct conversations with more than 7,000 fund managers, and more than 9,000 investors."

We aim to speak to all fund managers and investors in our database at least once a year where possible, and more frequently in many cases: high-profile investors or fund managers currently fundraising might be contacted as frequently as every month. Every year we have direct conversations with more than 7,000 fund managers, and more than 9,000 investors, to find out qualitative and often exclusive information about their funds, investments, plans and challenges. This allows us to present industry participants’ activities in context – what they have done in the past, what their plans are, what their peers are doing – which is simply not possible when relying on automated data collection.

Find out more about Preqin’s data collection process or request a demo to view our product to see why our data is the best in the industry.

ILPA have released a series of templates and guidelines for fund managers to report to their investors, including resources such as their Fee Reporting Template.

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