Why Quantum Analytics?


Noun (Kwɒntəm)

The smallest quantity of some physical property, such as energy, that a system can possess according to the quantum theory

Through advanced analytics we are able to provide our users with the most advanced tools and visualisations available to guide their own business decisions.

Quantum Analytics Positioning

Bodies of IP data are available on other platforms, but the key point of differentiation is that the Quantum Terminal associates and links all data points to specific companies and combines it with its financials if publicly listed.

Allows users to easily identify all IP belonging to a company, sector, or entire industry.

Data is sourced from scholarly, patent, source code, and financial data providers and includes sources like PubMed, Microsoft Academic, Crossref, USPTO, Orcid, OpenCitations, DOAJ, WorldCat, PMC, UnpayWall, GRID, WIPO, Core, GitHub, Nasdaq, CME, Quandl, and Compustat.
Comprehensive IP data on individual companies can be identified with just a few clicks, including the entire patent application, images, claims, and classifications.

Allows expert investors and advisors to get the clearest view of a company’s innovation cycle and compare it against peers.


Why Quantum Analytics?

For years, the world’s financial professionals have relied on corporate fundamentals and published information. But the world is changing: Since the last years, we have seen a massive increase of intangible assets across all asset classes, with intangibles currently making up more than 86% of the entire stock market value. However, detailed analytics and insights into intangibles has been a difficult task, even tough they represent the majority of the value and have proven strong correlation with future operating performance.

We introduce the concept of ‚Quantum Analytics’, which is the analysis of a corporation on a micro-level: Each technological element of its Intellectual Property forms the individual knowledge profile, which, combined with fundamental information, allows a completely new level of understanding of the true operation model  of any company.

The Quantum Analytics Platform provides solutions to analyze corporate IP, across teams, across asset classes, and at every stage of the investment process. Our goal is to provide a seamless user experience spanning idea generation, research, portfolio construction, trade execution, performance measurement, risk management, reporting, and portfolio analysis across the front, middle, and back office to drive productivity and performance. Our flexible, open data and software solutions can be implemented across the portfolio lifecycle or as standalone data feeds serving different workflows in the organization.

“The research driven data insight of intellectual property rights and fundamentals which Quantum provides has been accurately developed over the years and contributes to the uprise of technological innovation. Thank you Quantum.”

Daniela Herrmann, Founder & President, Topan AG

The Global Innovation Race is Heating up

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. To compete, companies can innovate in-house, or they can acquire others’ innovations. 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. As these technological advances began driving innovation, the size and diversity within company asset portfolios began increasing consistently.

This dramatic shift is evident in the market capitalization of S&P 500 in 2015, which was made up of 84% intangible assets, as noted by MSCI. In 1975, intangible assets only made up less than 20% of the total asset value.

However, investors devote considerable attention to analyst reports, fundamental data, earning calls or news articles, while intangible assets receive less attention. Evidence exists that individuals pay less attention to, and place less weight upon, information that is harder to process. Information about intangible assets and innovation is hard to process, because it requires developing and applying a theory of how the economic fundamentals of a firm or its industry are changing. It also requires an analysis of the quality and path from intellectual property & inventions to final products on the market, the profit of which can be highly uncertain and long deferred.

In such a situation, where a majority of the S&P 500 is in intangible assets, it is only prudent to be employing the best in class techniques for research and benchmarking, to ensure that decisions are based on data of the highest quality.

Lauren Cohen et. al have demonstrated in a Harvard research book that the stock market is unable to distinguish between “good” and “bad” R&D investment, despite the fact that successful innovation is in fact predictable. They found that the market consistently misvalues information in an ex- ante, predictable way. Specifically, the market does not take into account the information in firms’ past R&D abilities. Firms that have been successful in the past and that invest heavily in R&D as percentage of sales earn substantially higher future stock returns than firms that invest identical amounts in R&D, but have poor past track records.
Over the last decades, the analysis of company patent portfolios has become an increasingly important part of competitive intelligence activities, as well as a key tool in analysing national, regional, and company technology strengths. Implicit in these analyses is the idea that identifying a company’s intellectual assets, specifically those intangible assets that patents protect, is tantamount to identifying areas of strength within a company. Given the rise and exponential growth of Artificial Intelligence technology, the focus on Artificial Intelligence related intellectual property is key to a company’s future growth and success. Companies with focus on Artificial Intelligence are two times more likely to grow exponentially than companies without.

In addition, the environment for innovation and technology has become more and more complex and difficult to analyse, especially with technologies emerging in the field of Artificial Intelligence. According to the market research firm Tractica, the global artificial intelligence software market is expected to experience massive growth in the coming years, with revenues increasing from around 9.5 billion U.S. dollars in 2018 to an expected 118.6 billion by 2025. The overall AI market includes a wide array of applications such as natural language processing, robotic process automation, and machine learning.

There is a growing awareness that the 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 solutions is investment in research and development (R&D) combined with the strength and impact of its Intellectual Property. 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 solutions, 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 month. We also demonstrate that a company’s 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.

Artificial Intelligence Learns the Operating Model of a Company