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Algorithmic Insights for Targeted Investment

Non-financial risk profiles for global SMEs

A fintech startup in Ann Arbor, Michigan is laser-focused on accomplishing a gargantuan task that may unlock economic growth opportunities globally: Mapping non-financial risk profiles of global SMEs to inform precision economic development and to uncover public/private investment targets.

The KeyStone Compact Group Ltd - founded in 2014 - has developed algorithmic models to garner nascent insights in non-financial risks associated with thematic stocks and flows of local and regional economies.

A Complex Undertaking

 Nearly 100 million small and medium enterprises (SMEs) are active in the US, EU and APEC regions, representing 70-90+ % of companies globally. The economic growth opportunities are equally massive in scale: SMEs contribute from 20 - 70% to GDP and employment. Big data analytics can uncover and leverage connectivity, value capture and investment grade assessments in developed and emerging economies seeking to scale and attract investment.

A lack of understanding of structural relationships in thematic areas of the real economy result in inefficient capital flows and investment allocations.

The figure below (left) illustrates the complexity of financial interactions (denoted by lines) between industry segments and companies active in the smart grid ecosystem - based on Bloomberg value chain data. The abstraction on the right reflects the sectors involved in making the smart grid happen. What is striking is that the sectors extend well beyond energy companies. Red circles denote anchor segments; blue circles represent catalyst industry segments

Beyond Economic Accounting and Visioning 

Economic developers tend to take an 'accounting view' of the economy to inform their vision for future growth. How many companies do we have in each sector, and what are the financials and strengths of companies in our regions and countries? Investors then leverage this information for loans and equity investment opportunities, based on financial information and expert knowledge.

On the other hand, the 'macro-trend view' of future economic growth opportunities is informed by demographic, environmental, behavioral and technology data, using lagging (financial) and leading (text -based) indicators.

Valuable information is left on the table: the fluidity of emerging industries, cross-sector networks, and capability risks of companies and economic regions.​

CodifyingTacit Knowledge

The accounting view needs to be integrated with the macro-trend view such that economic structures (stocks and flows) can be leveraged. The KeyStone Compact Group has learned valuable lessons from the evolution of management practices such as precision agriculture and precision nutrition: observe, measure, automate, respond.

The analogy between precision agriculture and precision economic development is not perfect.  In the latter, most decisions are informed by tacit (expert) industry knowledge, with only 25% or so being financial metrics.  Hence, the scaling of targeted economic development has been elusive.

The KeyStone Compact Group believes it may have 'cracked the nut', by codifying tacit knowledge learned from business decisions. Using a big data analytics approach that combines public and purchased data with proprietary KeyStone Compact analytics, the company uncovers 'anchor sectors' and 'catalyst sectors' in a wide range of thematic new industries. In addition, the KeyStone algorithms tease out investment grade and value capture potential for each company represented.

FactSet-type risk and growth metrics for the companies representing these anchors and catalysts then generate insights to inform strategic investment decisions.

  • 'Anchors' are industry verticals that are defined by cost-driven value chain optimization.
  • 'Catalysts' are defined by value-driven growth relationships, and have ample connections into anchors and their suppliers.  

Tracking relationships over time allows the user to understand the shift of companies from anchors to catalysts, and vice versa. For example, an anchor in one industry sector may be a catalyst in another, depending on how the assets and relationships are leveraged. Similarly, an anchor in one country may be a catalyst in another. 

Data-driven precision economic development is on the cusp of becoming reality.

From Emerging Industry Value System to Investment Target

The company has partnered with the Global CleanTech Cluster Association, a Swiss Foundation covering the World's global green economic development clusters. The Foundation's objective is to drive scale and insights in anchor and platform industry development, understand emerging cross-sector networks in thematic sectors and, most importantly, quantify the shifts of value capture in these fluid networks.

Currently represented in 27 countries by 52 clusters, encompassing nearly 10,000 companies, the GCCA aims at 'Making Local, Global', by building out global value systems in nascent economies in collaboration with long-term investors. The companies represent the business lifecycle, from startups to multi-national corporations (MNCs), capturing highly variable segments of green economy value chains.

The power of KeyStone Analytics is exemplified for Finnish companies in the smart grid industry ecosystem. Based on the network map, and drawn from 140 candidates in the business registry Finnish SMEs represent a mix of equity, non-equity, and bootstrapped companies in selected key platform and catalyst sectors. Each SMEs KeyStone Compact profile is assessed and mapped.

Stay tuned for future KeyStone blogs on the power of non-financial risk insights, or subscribe to our newsletter.

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