To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Abstract

Entrepreneurial pitch competitions have become increasingly popular in the start-up culture to attract prospective investors. As the ultimate funding decision often follows from some form of social interaction, it is important to understand how the decision-making process of investors is influenced by behavioral cues. In this work, we examine whether vocal features are associated with the ultimate funding decision of investors by utilizing deep learning methods. We used videos of individuals in an entrepreneurial pitch competition as input to predict whether investors will invest in the startup or not. We proposed models that combine deep audio features and Handcrafted audio Features (HaF) and feed them into two types of Recurrent Neural Networks (RNN), namely Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). We also trained the RNNs with only deep features to assess whether HaF provide additional information to the models. Our results show that it is promising to use vocal behavior of pitchers to predict whether investors will invest in their business idea. Different types of RNNs yielded similar performance, yet the addition of HaF improved the performance.