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Machine Learning & Digital CleanTech Networks

Corymbus Partners with University of Michigan Data Science Program

· Finance,big data

Can machine learning and AI of alternative data sources provide investment alpha in public and private equities focused on sustainability? Corymbus is betting on green fintech.

Corymbus Asset Management has entered in an applied research collaboration with a University of Michigan Data Science Research and Education Program. The Program of Financial Technology for Sustainability (UM-ProFiTS) is based in the College of Engineering, and is affiliated with the Michigan Institute for Data Science (MIDAS).

ProFiTS was set up in 2016, and has evolved as a cross-campus collaboration with the Center for Venture Capital and Private Equity Finance (CVP; Ross School of Business), and the Center on Finance, Law and Policy (CFLP; School of Law) at The University of Michigan. The program engages NGOs, trade groups, FinTech companies as well as economic development and financing institutions.

ProFiTS' mission is to apply data science to uncover trends in finance and sustainability, and to design new financing and pricing models for green growth ('Green FinTech').

Why partner with data science research at UM?

The University of Michigan's data science Institute and associated computational, policy, law and finance programs across top-10 Schools and Colleges are unparalleled in their scope and application use cases. In addition, UM hosts a range of integrated sustainability programs across campus, including the Graham Sustainability Institute, and the Erb Institute which offers dual MS/MBA degrees in sustainability.

In finance, the CVP (Director: Prof. David Brophy) has led growth capital, global private equity, and technology commercialization conferences and MBA courses for 40 years. The CFLP (Director: Michael Barr) emphasizes big data and finance, behavioral finance, and fintech, leveraging a rich history in public policy and financial regulation at the US Treasury. ProFiTS (Director: Peter Adriaens) has led global initiatives in environmental risk finance and green growth, including at the Research Institute of the Finnish Economy (ETLA).

Sustainability finance, whether as projects under the Paris Accords, or in the broader ESG investment realm, is a profitable value proposition, with participation of all private actors and investment models.

To achieve scale and 'mainstream value' in finance, transparency, liquidity, and consistency ('TLC') are key. This is where data science delivers.

Cloud computing, big data, and the deployment of machine learning and artificial intelligence (AI) have started to influence investments and financial transactions. From back-end digital fund platforms, 'funds-in-a-box', algorithmic funds, and blockchain finance models, emerging data structures and trends based on natural language processing (NLP) are becoming financial pillars.

  Sustainability Finance becomes Green FinTech.

The KeyStone Compact: A data platform for business investment risk & opportunity

Corymbus' KeyStone Compact® platforms use inputs from social media (e.g. Twitter, LinkedIn, Owler) and other data providers (e.g. CBInsights, Google) to garner information on the competitive and financing landscape, management and advisor profiles, and business model differentiation in various industries.

The business model analytics have yielded predictive learning models to understand risk profiles of investable companies, at high level of granularity.

Eight key risk factors are extracted from large datasets for each company and relevant industry sector value chain, and form the basis for the KeyStone Compact algorithms. Models are available for startups, small & medium enterprises, and corporate lines of business. Post-processing of the investment risk characteristics to market, industry and management benchmarks is based on further statistical analysis based on industry and market factors external to the company.

Uncovering trends in digital CleanTech hubs: The Global CleanTech Cluster Association.

Since 2011, Corymbus has provided business model and financial risk analytics for the Global CleanTech Cluster Association (GCCA), a Swiss Foundation focused on scaling green growth globally. By leveraging its partnerships with 55 economic development clusters in 27 countries, the GCCA has developed the capacity to curate investment in over 10,000 companies across the business life cycle (startup to enterprise).

More recently, the GCCA has embarked on an ambitious program for digital deal sourcing between corporates and startups, modeled after the Nordic Innovation Accelerator (NIA).  These digital deployment hubs (D-Hubs) have now been implemented in Finland, Switzerland, Canada, the US, and are being explored in emerging economies in Asia.

With annual calls for Later Stage Awards since 2011, Corymbus has collected - under contract with the GCCA - data on nearly 1,000 companies, to select top-10 winners of investable business models.  

Longitudinal GCCA study: Predictive analytics for VC/PE/debt investment success

Using KeyStone Compact analysis of GCCA Later Stage Award companies nominated by global CleanTech clusters, three cohorts were structured in collaboration with UM-ProFiTS.  These included the top-10 (60 companies), top-30 (180 companies), and the non-selected nominations (750 companies).  The predictive analytics demonstrates that the top-10 companies consistently outperform the top-30 companies in the VC/PE, corporate investment, and debt investment categories.  

When considering annual assessments, the data show that the top-10 companies outperform top-30 companies for four out of six years. The implications are that the investment prediction accuracy has improved over time, as the analytical algorithms gather more data and adapt. The first three years, 6% of top-10 predictions were 'false positives', relative to the benchmark of the top-30. The last three years, all top-10 predictions outperformed the benchmark.

With 72 company and industry context metrics informed by 1000's of data points each, the prediction accuracy rate will steadily improve.​

High resolution temporally-sensitive information with underlying A.I. learning is key to understanding and scaling investment risk and opportunity in digital CleanTech hubs. As the impending UN SDG (sustainable development goals) finance initiative, and decreased public finance commitment to sustainability in the US are demonstrating, fintech-informed investment and pricing models will emerge to scale green growth.

For more information, check our recent (2016) book: "Financial Technology for Industrial Renewal" (P. Adriaens and A.-J. Tahvanainen). Digital copy available for free at link.

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