Financial Algorithms

Setting a quantitative approach in Private Equity investments

By 1 October 2020 October 12th, 2020 No Comments

Information about unlisted companies is rare. That’s the reason why Private Equity was, for a long time, associated with limited transparency, a long investment horizon and a lack of consistent data.

However, after what happened with the public market with the development of Bloomberg and Refinitiv, two leading companies on providing financial information and analysis on listed institutions, some platforms are gathering more and more information on private companies.

In Private Equity, you have two types of investments: in a single private company or in a portfolio of private companies. When you are investing in the second type, you are buying some shares from a private equity fund. Preqin, a British private company, has become the leader of providing financial data on the alternative asset market. They are providing more specific performance indicators on more than 65 000 private equity funds all around the world.

So, the amount of data concerning Private Equity is always increasing but is still lower than the one available on the public market. Some private equity fund managers try to take advantage of this new data by using quantitative strategies to find the best new private company to include in their portfolio or to rebalance it in the best way.

These strategies are following the same steps:

  • A preliminary phase where they analyse all the data they have access to and where they make statistical tests to identify relationships and correlations between some parameters
  • A second phase where they build their model, using some Machine Learning algorithms, and back test it
  • And finally a third phase where they implement their model and make it easy to use new models are then created and help private equity managers, but also investors, to choose the right private company to invest in.

In WedgeInvest, we are using this quantitative approach in different levels, from matching algorithms to valuation and portfolio algorithms.

To match the unlisted companies to the most adequate investors, our algorithms are using data provided by our clients, but above all implied data coming from choices and actions made by our clients on our platform. The more active the buy-side and sell-side are on WedgeInvest, the more accurate their results will be.

This conclusion applies also for valuation algorithms. Most of the data required for them are coming from account documents given by subscribers, including balance sheet, profit and loss statement and cash flow statement. The data from these documents are filtered, reclassified, and then deployed in valuation algorithms that are computing a fair price and a score of the unlisted companies.

So quantitative approaches are used in many functionalities on WedgeInvest and will play a key role on the investment portfolio construction and management. Thanks to this new service, the clients will be able to create and manage different types of portfolios, from one only composed with unlisted companies to another composed of shares from private funds. Quantitative strategies will be essential to identify which right data should we use to get an optimal portfolio.

Grigory Shulmin

Author Grigory Shulmin

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