Who Sees the Future? A Deep Learning Language Model Demonstrates the Vision Advantage of Being Small

Paul Vicinanza, Amir Goldberg and Sameer B. Srivastava
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Paul Vicinanza: Stanford U
Amir Goldberg: Stanford U
Sameer B. Srivastava: U of California, Berkeley

Research Papers from Stanford University, Graduate School of Business

Abstract: Which groups are most likely to become visionaries that define the future of their field? Because vision is difficult to measure, prior work has reached conflicting conclusions: one perspective emphasizes the benefits of being large, established, and central, while another stresses the value of being small, upstart, and peripheral. We propose that this tension can be resolved by disentangling vision--the capacity to generate contextually novel ideas that foretell the future of a field--from the traces of vision that result in tangible innovation. Using Bidirectional Encoder Representations from Transformers (BERT), we develop a novel method to identify the visionaries in a field from conversational text data. Applying this method to a corpus of over 100,000 quarterly earnings calls conducted by 6,000 firms from 2011 to 2016, we develop a measure--prescience--that identifies novel ideas which later become commonplace. Prescience is predictive of firms’ stock market returns: A one standard deviation increase in prescience is associated with a 4% increase in annual returns, and firms exhibiting especially high levels of prescience (above the 95th percentile) reap especially high returns. Moreover, contrary to theories of incumbent advantage, we find that small firms are more likely to possess prescience than large firms. The method we develop can be readily extended to other domains to identify visionary individuals and groups based on the language they use rather than the artifacts they produce.

Date: 2020-05
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