Why Investors Should Make Data-Driven Investments

In addition to a solid concept and enthusiasm, investors assess a variety of variables before investing in a project. Despite investors’ determination to make investments based on logic and knowledge about the entrepreneur, a great pitch always has a chance to captivate their attention and intention. However, according to this Chicago Booth Review article, investors should always make data-driven investments.

Make data-driven investments

The article opens by describing how, on TV series like Shark Tank and in private boardrooms across the world, startup founders passionately argue why their budding businesses are the next great thing. According to the article, although the business reasons they provide are important, so is the ability to pitch with charm, clarity, and a personal story. However, Princeton postdoctoral researcher Diag Davenport points out that when investing in businesses, venture capitalists depend far too heavily on the credentials of the founders. This offers them an edge in terms of earning more money since they make investment decisions based on evidence rather than their impressions. Making data-driven investments, according to the article, is a means for companies to make their judgments more objective and less susceptible to errors.

An algorithm trained to predict whether venture capital-funded firms will succeed was able to properly forecast their success rate in a series of studies. Surprisingly, the article mentions that when Davenport compared the algorithm’s predictions to the judgments of the investors, he discovered that venture capitalists spent millions of dollars to fund firms that they should have known will fail. He calculates that the lost income for his data set of $9 billion of investments amounted to $900 million after modeling substitute portfolios that removed low-performing startups and replaced them with bonds or equities. A founder’s history and expertise do matter, but according to the article, the algorithm suggests that venture investors depend on these factors too much. According to the article, more machine-learning approaches can help investors make better selections.

Though knowing a founder’s past and qualifications are necessary for venture capitalists to make effective data-driven investments, this article on the Chicago Booth Review website argues that they should not depend too much on these factors, a summary of which is provided above.