We are passionate about research and sharing our ideas with the investment community.
Company culture is widely recognized to be a key intangible asset, yet few investors have attempted to formally measure it. We use natural language processing to build multidimensional culture profiles for each company. Firms with strong cultures have outperformed the stock market, while those with toxic cultures have lagged.
Value investing has struggled over the past decade. We believe this is due to its failure to incorporate intangible assets, which play an increasingly crucial role in the modern economy. We consolidate our prior research to construct a firm-level measure of intangible value. We find that expanding intrinsic value to include intangibles can help restore value investing to its former glory.
Searching for Superstars
The ability to attract and retain top talent is an important yet undervalued competitive advantage. We build a graph of human capital flows and apply network analysis to identify companies winning the war for talent. Firms able to attract superstars from elite competitors and universities have outperformed. We also include a March Madness-themed bonus section!
A Human View of Disruption
We examine the rise of technology from the perspective of its human creators. We measure companies’ investments in technical human capital to identify firms that are truly embracing the digital age. These intangible investments have generated strong stock returns across a wide variety of industries. We conclude with a discussion of the disruptive impact of technology on labor markets.
The Platform Economy
The technology platform has emerged as the preeminent business model after many years in ascent. We use natural language processing to identify platform companies and show that they have significantly outperformed the stock market. Platforms’ powerful network effects generate positive feedback and monopoly dynamics, which are disrupting traditional valuation approaches.
Investing in the Intangible Economy
The modern economy is increasingly driven by intangible assets, such as intellectual property, brands, and networks. However, common measures of value have failed to adapt to this transformation. The path forward involves both accounting reform and improved methods to directly value intangible assets. Investing in intangible-rich companies can be profitable as they are often misvalued by traditional metrics.
Monopolies Are Distorting the Stock Market
While Big Tech is drawing fire for monopolistic practices, industry concentration has actually been increasing more broadly since the 1980s. Most industries are now dominated by a few superstar firms. These firms enjoy higher profits and pay less to labor. The rise of monopolies explains currently elevated corporate profits and stock market prices. However, it also contributes to rising inequality and political unrest.
Value Investing Is Short Tech Disruption
Value investing has a long and distinguished pedigree but is currently in a deep thirteen-year drawdown. We believe this is because value has rotated into a massive losing bet against technological disruption. We isolate this exposure using machine learning and find it fully explains value’s losses. We offer takeaways for both stockpickers and asset allocators.
Deep Learning in Investing:
Opportunity in Unstructured Data
We discuss the potential role of deep learning in investment management. We explain how deep learning can help investors streamline their consumption of unstructured data. We apply transfer learning to adapt models originally trained on large-scale, out-of-domain datasets for highly specialized investment applications. Transfer learning allows even small niche firms to harness the massive resources of big tech companies. Despite its transformative potential in unstructured data, most investors are still trying to apply deep learning directly to asset price prediction. We run simulations on a large panel of alphas to demonstrate the limitations of this approach.
Investment Management in the Machine Learning Age
The investment management industry is still in the process of figuring out how to incorporate recent advances in machine learning. We highlight three areas where machine learning can add value: unstructured data, data mining, and risk models. More importantly, we present detailed case studies for each topic. Our goal is to present practical insights without the buzzwords and jargon that have hamstrung the adoption of machine learning in our industry.