“Knowledge is power” – Sir Francis Bacon.

#DataDrivenInvesting #PE #VC #PrivateEquity #FamilyOffice #Investing #Fintech

I explained in my last post “The Next Edge in Private Equity” why I believe that tech-savvy, data-driven investors can gain an advantage.

I have been working on an initiative to leverage technology, as well as traditional and alternative sources of data, to improve my efficiency. I believe technology can provide an edge through differentiated insight and productivity gains. Over 200 solutions, tools, and data sets have been identified. I guess that you can label me a data junky! I have been using several resources for a long time, tried some more recently, and plan on trying others occasionally in the future given the high price points or the more niche use cases. It is a burgeoning field; I am constantly discovering new resources.

Why Should Every Investor Evaluate its Tech Stack?

The resources identified are not only helpful for deal sourcing, but also for due diligence (HR, social analytics, market research, financial), work flow improvement (project management, marketing, collaboration, data visualization, etc.), portfolio monitoring, as well as overall firm management. In short, the analysis covered the entire private equity technology stack.

Last fall, a fellow investor brought  the results from a survey of early stage venture capital funds’ tech stack realized by PEVCTech and Blue Future Partners to my attention. It was interesting to see how their findings largely corroborated my own despite my involvement in later stage private equity investments.

Opportunities and Challenges.

David Teten, a Managing Partner with HOF Capital, highlights the opportunities and the challenges of automating the private equity profession. “Historically, investing was a manual, artisan process. An investor had few hard metrics other than the actual financials, and little technology to make the process scalable. Over the past few decades, better metrics became available, and investors could take a more analytical, data-driven approach. The extreme example of this are algorithmic investors in the public markets, who design algorithms which trade on the designer’s behalf, as opposed to making trading decisions directly. High-frequency trading, algorithmic by its nature, is estimated to account for at least 50% of US equity markets trading volume. Quantitative, technology-enabled investing in private companies makes sense, but is structurally very difficult, and will become a more common strategy at a much slower rate. The private markets are more opaque; they offer less of the hard data critical to a true quant approach.” My view is that PE is no longer a cottage industry and investors’ tech stack must evolve. However, adopting a data-driven approach for direct private market investing requires a shift in organizational capabilities and major, long term investments. Currently, only a handful of VC funds as well as large PE, sovereign wealth funds and pension funds have the required in-house resources (tech savviness and AUM) to justify the investment. Perhaps greater collaboration and partnerships between data providers and investors is needed before a data-driven approach is more widely adopted in the industry.

Recent Initiatives Indicative of Where the Industry is Headed.

Blackstone is making a big push with major investments in technology companies (two investments in Canada, among others: the Financial and Risk business of Thomson Reuters Corp. and Titus) and in its internal capabilities. Blackstone Innovations (BXi), its in-house technology team of more than 250 people is growing. They serve both internal and external clients. During a recent earnings conference call, Tony James, the firm’s president and COO, shed some light on the importance of data and artificial intelligence as a growth driver and as a tool for efficiency across its business. Their entire PE group (from global head to analysts) attended Singularity University in Palo Alto to get up to speed with new technologies and the use of data. They also launched the Blackstone Data Initiative; the firm-wide team focused on data science, big data, and advanced analytics. According to a LinkedIn job posting by the company: “this is a new initiative, launched in response to significant demand across Blackstone and its portfolio companies. The team partners directly with the firms’ investment professionals to improve the investment process, make new investments, and optimize our existing portfolio company operations. The team consists of data scientists and data engineers that analyze complex datasets, build predictive and analytical models, and productionize robust data flows. Every team member works across each of the business units, with a near-term emphasis on private equity, private real estate, and growth equity, and working directly with portfolio company management teams.” Their president admitted that it is only the beginning of a long journey that will require a change in culture, education, and will impact all facets of their business.

At the other end of the spectrum, VC firm Correlation Ventures did not make a single investment in its first four years according to an HBS article. “Instead, the two founders met with many other VCs and entrepreneurs and negotiated nondisclosure agreements with data providers. They eventually aggregated data on more than 60,000 financings dating back to 1987, representing some 98% of the pool of deals in that period. Its co-founder calls it “the most complete and accurate database of US-based venture capital financings in the world”.” They believe that their data-driven due diligence approach enabled them to make quick decision and uncover investments in companies such as Casper who had not received much VC attention at the time. Perhaps this example of a VC firm adopting a data-driven approach can inspire others across the PE spectrum and contribute to bringing additional capital to underserved market segments (such as the lower mid-market, emerging markets, etc.) which have been harder to serve or scale efficiently.

Strive to Leverage Technology to Augment Professionals.

To conclude, I don’t think most investment decisions in private equity will be (and neither should they be) solely driven by data and automated like they are at some successful quantitative hedge funds. However, I do believe software and data can contribute to the generation of alpha by making investment professionals more efficient and effective. Not only for asset selection, but also to help provide value add to portfolio companies (something outside the scope of passive HF). Good PE investors are not just superior allocators of capital, but also great builders of companies. More than ever, the economy needs entrepreneurial investors with judgement, soft skills, and experience empowered by data and tools that help make their craft more scalable.

Feedback: I am curious and delighted to hear your thoughts on this subject. I look forward to discovering new resources and sharing ways to improve investment processes.