Enhancing venture capital investment strategies through automated web data collection and analysis

Abstract

“Identifying investment opportunities is crucial for every venture capital fund, yet this process is often labor-intensive, time-consuming, and profoundly relies on the gut feeling of investment professionals. With new companies being discovered every day and intensified competition among funds, it has become increasingly challenging to recognize the most attractive prospects quickly. Machine learning-based decision support brings objectivity to this process, reducing the need for intuition. By applying supervised machine learning techniques to rate deals identified through web crawling and proprietary fund data, I found that random forest models perform exceptionally well, significantly cutting down the time spent on deal sourcing.