The global innovation race is heating up, with advances in artificial intelligence, block chain, biotechnology, data storage and other cutting-edge technologies transforming sectors and global markets. This is also evidenced in the fact that the ratio of tangible to intangible assets of industry leaders around the world has been seeing a drastic change over the past four decades. The market capitalization of the S&P 500 is currently made up of 84% intangible assets, up from 20% in 1975 (according to MSCI). With our research, we demonstrate that analysing intangible assets of a company in great detail allows building models to accurately forecast a company’s ability to convert its intellectual property into stock value in the following quarters. We also demonstrate that a company’s AI-IP impact correlates significantly with its future stock returns.
Our research offers a new innovation-related measure, IPI, which is contemporaneously associated with market valuation and predicts future operating performance and stock returns. Existing studies relating to innovation and market performance focus on the effects of either the input (R&D) or the output (patents) of innovation separately. Our study differs in focusing on Artificial Intelligence based innovative efficiency as a ratio of innovative output to input, based on the idea that efficiency should be highly value relevant. We find that the predictive power of AI innovation is incremental to that of other innovation related variables such as R&D intensity, significant R&D growth, patent counts and citations. We demonstrate that portfolio analysis confirms our hypothesis and show that a portfolio of top companies generated a return of 237% (+159% to baseline), which is significantly higher than the market (+78%).
A.I. Market Growth
According to the market research firm Tractica, the global artificial intelligence software market is expected to experience massive growth in the coming years.
There is a growing awareness that the Artificial Intelligence intellectual property owned by companies can be an important factor in their commercial success. Intellectual property, particularly in the form of patents, provides the technological foundation upon which new products and services are built. Background research provides a strong rationale for the expectation that companies with strong IP portfolios, especially with intellectual in the Artificial Intelligence segment, will perform better in the stock market. A method devised to accurately measure the quality of company technology and innovation should therefore have a significant predictive effect on company stock performance.
Furthermore, information of this type should be particularly valuable because it is not currently available to market analysts, leading to the strong assumption that the quality of company technology might not be properly valued in the market. Deng et al. showed that companies with high-quality patent portfolios had market-to-book valuations that were 25 percent higher than other companies in the same industries with lesser-quality portfolios, both contemporaneously and for a number of years in the future.
The activity at the heart of our research is investment in research and development (R&D) combined with the strength and impact of its intellectual property, especially its Artificial Intelligence intellectual property (AI-IP). Given that R&D stimulates innovation and technology change, which can in turn lead to improvements in productivity, living standards and economic output, the quality of R&D investment and the ability to turn AI-IP into revenues and profitability is a critical task of the market.
With our research, we demonstrate that analysing intangible assets of a company in great detail allows building a model to accurately forecast a company’s ability to convert its intellectual property into stock value in the following quarter. We also demonstrate that a company’s AI-IP impact correlates significantly with its future stock returns.
Our findings add to a growing amount of literature highlighting the market’s inability to properly value investments in R&D. An alternative argument for why innovative efficiency would predict higher future returns derives from the q-theory (Cochrane, 1991, 1996; Liu Whited and Zhang, 2009). Firms with higher innovative efficiency tend to be more profitable and have higher returns on assets. All else equal, the q-theory implies that higher profitability predicts higher returns because a high return on assets indicates that these assets were purchased by the firm at a discount (i.e. that they carry a high-risk premium). Specifically, suppose that the market for capital being purchased by a firm is competitive and efficient. When a firm makes an R&D expenditure to purchase innovative capital, the price it pays is appropriately discounted for risk. For concreteness, we can think, for example, of a firm that acquires a high-tech target at a competitive market price. In this scenario, a firm on average achieves higher return (large number of patents, resulting in high cash flows) on its innovative expenditures as fair compensation if its purchased innovative capital is highly risky, and it receives low return if capital is relatively low-risk. Past innovation efficiency is, therefore, a proxy for risk, so firms that have high past innovative efficiency should subsequently be productive in patenting (Dierickx and Cool, 1989) and earn higher profits and stock returns. In other words, q-theory also predicts a positive innovative efficiency return relation.
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IP Assets: An Awakening in the Market?: Intangible assets increase to 84% of the S&P 500’s value. Learn how to exploit IP related stock market inefficiencies.
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