Private Equity’s data technology puzzle – Overcoming intransparency (1/2)

In this first part of a two article series we look at the challenges when dealing with poor data.

Read this article and learn about:

  • The growing role of private equity among asset managers
  • How managing private equity portfolios is fundamentally different to traditional assets
  • The ‘data-mess’ of aggregating data in a timely, accurate, and complete format for private equity
Thomas Meyer 
Thomas Meyer, LDS Partners

The growth of illiquid asset classes, such as private equity, precipitates the need for specific IT solutions. Intransparency and illiquidity are often seen as obstacles to remove but are, in fact, a source of private equity’s success.

Private equity is now the largest alternative asset class for pension funds after real estate. Despite this, the software for managing such investments has failed to keep pace. Things are starting to change. 

Tightening financial regulation, increasing demands on pension funds for stakeholder transparency and a growing realization that larger private equity allocations present no meaningful liquidity risks are prompting a re-evaluation of the efficacy of existing systems. In addition, with increasing capital allocated to private equity the industry is becoming more competitive and efficient, requiring far more sophisticated approaches to portfolio management and more powerful IT system support. But the development and adoption of new technologies, never a trivial exercise, is particularly complex for private equity. 

Investment in private equity requires patient capital

Long-term investors in highly illiquid closed-ended funds are essentially locking away their capital for 10 years or more. Private equity funds are typically organized as limited partnerships and designed to shield portfolio companies in their early stages and those in need of being restructured from disruptive market influences, and to assure these companies’ continued financing.

 Figure 1

Figure 1: Typical private equity fund cash-flow pattern

 

Investing through funds managed by specialist managers has for good reasons become the mainstream modus-operandi of institutional investing into private equity. However, this successful model does not fit neatly into the wider investment landscape. A recent SimCorp poll[1] found that more than half its users prioritized concerns regarding data, e.g., users want data collection portals and integration with third-party data providers. In private equity, these include Burgiss, Cambridge Associates, Preqin, as well as initiatives to create common data exchange platforms in the private equity industry, such as AltExchange.

According to 31% of SimCorp users[2], cash-flow forecasting for fund investments is the highest priority, which is an immediate consequence of the assets class’s illiquidity. Many view poor data quality and the lack of a market providing liquidity as a ‘bug’ and defect that needs to be fixed, before larger allocations can be contemplated. Paradoxically, both perceived weaknesses are in fact an essential part of private equity’s value creation proposition. It is worth spending some time on why this is the case.

Legalized insider trading

Private equity’s value proposition is the search for inefficient market niches, implying the need to work with proprietary but incomplete data. The difficulties associated with getting access to high quality data enable what is often called ‘legalized insider-trading’. For alternative assets such as private equity one outperforms by finding inefficiencies that the market has not yet uncovered. As inefficiencies in financial markets become exploited, investors need to continuously explore under-researched but potentially profitable niches in order to stay ahead of the game. In this process they are guided more by proprietary data than what third party data services can provide as information.   

Investors in private equity aim to harvest the illiquidity risk premium that structurally illiquid asset classes may offer. In contrast to asset classes that may become illiquid as a result of financial turmoil and heightened risk aversion, investors in structurally illiquid asset classes, such as private equity and real assets, are aware ex ante of the risk they take. In fact, only long-term investors, whose liability profile allows them to lock capital in for significant periods (often 10 years or more) can take such risk.  Here, funds basically serve as commitment devices that force investors to back long-term-oriented investment strategies even through difficult market cycles and thus enable the creation of successful and valuable companies.

The private equity investment process poses specific challenges, notably the different time horizon for illiquid, long-term-oriented and traditional assets which can be rapidly disposed of via secondary markets. How one manages private equity portfolios is fundamentally different to traditional assets. Investment managers in charge of traditional assets have a rich set of standardized and customized tools available and make use of IT on a daily basis. They can rely on high-quality data made available in real-time and with little effort. Transactions are highly standardized; speed and reliability of execution front-to-back are paramount. Market value and liquidity are almost synonymous and consequently trading in order to regularly rebalance the portfolio is the most powerful risk management tool.

Even in public markets this may appear to be a glossy description, but the contrast with the situation in private markets is stark, nevertheless. In private equity there are comparatively few transactions, they are negotiated often over months and highly bespoke. Data is reported with a time-lag of months, is incomplete and of varying quality. Investment managers must ask their middle and back offices for analyses and reviews that require significant work to compile data from various sources. As a result, portfolio reviews, stress tests, scenario planning are done infrequently, typically on a quarterly, bi-annual or even annual basis. Designing the portfolio upfront is the major risk management tool.

The ‘data-mess’

Practitioners justifiably refer to the current situation as ‘data-mess’ where aggregating data in a timely, accurate, and complete format is difficult and painful and some even throw up their hands and declare this to be ‘mission impossible’.

Despite this widespread thirst for data integration, with typically not more than 20 limited partners as investors in a fund and with negligible changes in fund ownerships over the ten-year plus lifetime of funds, full automation so far has been elusive. The other half of the polled users rely on partly automated data capture, using Excel templates filled out and reported by fund managers, or data being inputted into portals by fund managers, or data input outsourced to generic service providers, or combinations of these approaches. Data inputting is increasingly outsourced, but this needs a high degree of familiarity with the specific transactions and high discipline in adherence to industry standards, something which low-cost providers are unlikely to assure, at least over a longer time frame.

In theory, the larger market participants could enforce standard formats for reporting data, but on the contrary they are using their strong power as key investors to impose their own proprietary reporting templates on fund managers. In fact, in an industry where legitimate information can be kept private, sharing data with other players gives no advantages whereas, in fact, keeping things secret offers a head start. So it does not come as a surprise that many powerful players, such as large funds-of-funds, sovereign wealth funds, and development financing organizations ask fund managers to fill out specific templates that are very detailed on portfolio companies, Environmental Social and Corporate Governance (ESG) factors, and impact statistics, for instance.

Predicting is difficult, especially about the future 

When looking at companies listed on stock exchanges, the efficient market hypothesis suggests that stock market efficiency causes existing share prices to always incorporate and reflect all relevant information. Various market crises have tarnished the theory, yet undoubtedly in financial markets high-quality (i.e. complete and precise) data made available in real-time is seen as a given.

Indeed, we tend to overlook how much the stock market is solving for us in regards of data: at any second, thousands of market participants are critically analyzing and reviewing information and – through trading – are correcting any mistakes in valuations. Contrast this to fund administrators in private equity that always have to conduct an extensive checking during the Net-Asset-Value calculation process.  For private equity, assets where only a handful of players sporadically transact all these tasks need to be done internally. Therefore, most institutional clients only collect minimum information on their private equity assets, usually just positions with limited additional classifications. The problems escalate, as missing or incomplete crucial information inhibits other steps in the investment process, including performance measurement, reporting to management and regulators, compliance, and others.

The challenge is to ‘marry’ traditional asset classes where markets provide reliable pricing data and instant liquidity with private equity and real assets where markets neither provide pricing information nor liquidity. Under these circumstances the complexity to monitor and evaluate ongoing investment risk increases greatly.

In the next and final part of this series we look at why cash-flow forecasting is a key requirement for investors in private equity and how this can be done. To manage data collection, operational risk, compliance and investment monitoring, investors require integrated IT platforms that are able to do this efficiently.  

About the author 

Thomas Meyer is partner and co-founder of LDS Partners, specialising in the development of investment strategies, portfolio management and asset allocation models for real assets (private equity, infrastructure, real estate). Other career stations include intelligence officer in the German Air Force and as CFO of Allianz Asia Pacific in Singapore. He was responsible for the creation of the European Investment Fund’s risk management function with focus on the development of valuation and risk management models and investment strategies for venture capital funds-of-funds.

Thomas has published several books on investment strategies and risk management for real assets. He authored ‘Private Equity Unchained’ (2015, Palgrave MacMillan) and is the co-author of ‘Beyond the J Curve’ (2005, translated into Chinese and Japanese), ‘J Curve exposure’ (2007) ‘Mastering Illiquidity’ (2011), all published by John Wiley & Sons, and two CAIA Level II books, which are required reading for level II of the Chartered Alternative Investment Analyst® Program.


1 Survey taken during SimCorp’s International User Community Meeting (IUCM)

2 Ibid.


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