We are passionate about research and sharing our ideas with the investment community.
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.