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Intangible Value: A Sixth Factor
The "Intangible Value Factor" (IHML) can play an additive role in factor portfolios alongside the established market, size, value, quality, and momentum factors. This Six-Factor Model avoids the problematic "anti-innovation" bias of traditional factor portfolios and can be easily implemented using ETFs.
Digitizing the Old Economy
Old Economy stocks trade at low valuations due to the perception that they are being disrupted. In reality, tech hiring data show that many Old Economy firms are embracing digitization. Stocks of these Early Majority firms have outperformed. Big tech layoffs may accelerate technological diffusion, unlocking further upside for these overlooked stocks.
Investing in Influence
Companies invest billions of dollars in political influence through lobbying, campaign contributions, and other means. However, as with other intangible assets, political capital tends to be overlooked by the market. We show how investors can earn excess returns buying undervalued political capital.
Liquid Venture Capital
Venture capital has delivered great historical returns but is illiquid and hard to access. Fortunately, innovation does not occur only at venture-backed startups. We replicate venture capital returns using liquid small-cap public equities and find the underlying innovation premium also exists at large innovative firms. We also show that crypto tokens can provide a liquid complement to blockchain venture equity.
Investing in Innovation
Innovation stocks have become a popular investment theme but are facing scrutiny. We build a half-century backtest of innovation that invests in a rotating portfolio of technologies trending in patent data. Innovation has delivered positive long-term returns distinct from growth and other traditional factors. While innovation is prone to speculative bubbles, this can be mitigated using valuation and other intangible pillars.
Value Investor's Guide to Web3
Web3 is attracting a flood of investor interest but is rife with hype and speculation. A value investing approach can help. We adapt our “intangible value” lens to crypto and build a value strategy in small-cap tokens. We also create Web3 industry classifications and crypto stock portfolios.
Brand in the Influencer Era
Social media is democratizing consumer influence, empowering ordinary individuals to shape brand perception. We identify brands with strong positioning using social media discourse and network structure. We find strong brands have outperformed the stock market. We also explore the trends of sustainable and Millennial brands.
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.