Identifying the right private market investment opportunity is challenging. Unlike publicly traded companies, private company data is much less accessible, which means that investors need to spend a significant amount of time researching many different, and often, unstructured datasets to identify suitable opportunities to invest in.
While sourcing is challenging, investors are attracted to the private markets because of the excitement that comes from being associated with companies that promise “the next best thing,” and the outsized rates of return as compared to publicly traded companies. However, these private market investments typically have much greater risks – given the relatively limited amount of information available – which is why being able to conduct enhanced market research is so critical.
So, how can investors identify the next significant investment opportunity and do the appropriate amount of due diligence, while limiting the potential risk?
On a relative basis, of the ~27 million companies in the United States, less than 1% of them are listed on public exchanges. The typical process when sourcing potential private market investments involve introductions from one’s referral network, searches conducted by financial advisors, and/or primary fundamental research. For investors who want to identify comparable companies in a particular industry niche, the necessary relevant information is often unavailable.
Too often, investors rely on traditional business brokers, technologies like Google, or legacy datasets that do not have the breadth of companies or intelligence to properly identify all the necessary information or associations. A typical Google search does not deliver enough competitive insights and is not designed to surface details like the company’s financials or the competitive landscape of a specific company. In addition, the legacy datasets tend to be very limited in terms of the total number of companies that they cover, the depth of information associated with any one company, and their ability to serve up meaningful insights or associations between these companies.
As digitization increases access to global corporate data, the financial services industry has embraced new technologies like the cloud and AI to evaluate investment opportunities more efficiently. Access to unstructured corporate data is growing exponentially with the IDC projecting it will make up more than 80 percent of global data by 2025. This poses a challenge for businesses as they try to glean insights and gain a competitive advantage from all of the new and existing data.
The use of AI and NLP has become attractive in the financial services industry as professionals try to use these new technologies to quickly understand vast amounts of unstructured data and obtain unique insights. Properly trained AI can help investors identify or recognize patterns within data and adapt to changes in those datasets, while NLP enables systems to understand human language and the emotion behind the text. Combined, AI and NLP technologies allow investors to make more informed investing decisions or better identify and classify investment opportunities. For example, Cyndx’s Projected to Raise Algorithm identifies, with 86% precision, when a company is likely to need capital without having access to a company’s financial projections.
Research that would normally take months of manual labor can now be done in seconds. Investors can look at multiple factors and understand the influence they have on one another when making a potential investment decision – i.e., investors can look at where an executive worked historically, the number of patents filed and who owns competitive patents, acceleration of capital into a particular space, lines of business that are supported, education of the founding team, and the experience of the investment partners, among others.
As a result, investors can gain a 360-degree perspective on a company's associations and its competitive landscape, and AI can bring in new datasets that provide a deeper and instantaneous picture of a company or industry ecosystem. This approach goes beyond the standard classification and uncovers - through dynamic mapping - the most relevant concepts associated with any one company, even for extremely niche sector. With today’s technology, investors now uncover unique opportunities in a record amount of time and can maintain a consistent deal flow.
Like investors, entrepreneurs and company owners can reap the same benefits from AI and NLP technologies when looking for the right strategic partners for their company. Finding the right investor or the most attractive business opportunity and conducting the appropriate level of due diligence has traditionally been a highly manual and time-consuming process for entrepreneurs as well. Companies don’t always have access to the relevant information necessary to make the right decision as it is often opaque or unavailable. Today, AI and NLP tools provide greater visibility into the private market and improved the discovery of acquisition targets while reducing the time spent evaluating a business opportunity.
Advances in AI and NLP technologies simplify investors and entrepreneurs’ deal sourcing and investment strategy with tools that help quickly identify and access the right companies or investors. Consequently, the efficiency of the private markets should increase, while the risks associated with it should decrease over time.
© 2023 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.