{"id":591,"date":"2022-05-10T13:26:52","date_gmt":"2022-05-10T11:26:52","guid":{"rendered":"https:\/\/blog.zhaw.ch\/digital-futures-lab\/?p=591"},"modified":"2022-05-10T13:26:52","modified_gmt":"2022-05-10T11:26:52","slug":"on-the-frontier-of-sustainable-finance-with-the-help-of-a-new-supercomputer","status":"publish","type":"post","link":"https:\/\/blog.zhaw.ch\/digital-futures-lab\/2022\/05\/10\/on-the-frontier-of-sustainable-finance-with-the-help-of-a-new-supercomputer\/","title":{"rendered":"On the frontier of Sustainable Finance \u2013 with the help of a new supercomputer"},"content":{"rendered":"\n

The distinction between Finance and Sustainable Finance is what motivates <\/strong>Alexander Posth<\/strong><\/a>, <\/strong>Linus Grob<\/strong><\/a> and <\/strong>David Jaggi<\/strong><\/a> from the <\/strong>Institute of Wealth and Asset Management<\/strong><\/a> at the ZHAW School of Management and Law. The institute recently acquired a new supercomputer with which they can process huge volumes of alternative data and thereby detect companies engaged in \u201cgreenwashing\u201d.<\/strong><\/p>\n\n\n\n

What is the difference between Finance and Sustainable Finance?<\/strong><\/h2>\n\n\n\n

Alexander Posth:<\/em> Conventional or classical finance is all about risk and return. And we can assess these parameters by looking at conventional financial data, like a company\u2019s balance sheet or the stock price. But in sustainable finance we need to channel investments into the right direction to enable the transition towards a more sustainable economy. If we look at how this can be facilitated, if we look at the challenges we are facing in the upcoming years, we need to consider alternative data.<\/p>\n\n\n\n

What do you mean by alternative data?<\/strong><\/h2>\n\n\n\n

Alexander Posth:<\/em> To assess the sustainability profile of a company, it is not enough to look at the balance sheet. That\u2019s why alternative data is concerned with the so-called ESG criteria, that is the environmental, social, and corporate governance impacts (see also info box below)<\/em>. We need to understand where and how the company is operating but also their business model and value chain.<\/p>\n\n\n\n

The data necessary to understand this is not embedded in the classical context of finance, but comes from completely different sources, like news feeds, satellite image data or patent data. Some environmental impacts, such as deforestation or air pollution, can be measured with satellite images to see what is going on at or around the production facilities of a company. And only by this we can get an objective assessment of that activity on the immediate environment.<\/p>\n\n\n\n

What is the role of a company\u2019s innovations and their new products on the market?<\/strong><\/h2>\n\n\n\n

Linus Grob:<\/em> We also analyse patent data because they provide information on a company\u2019s new technologies. Most countries or regions have their own patent offices, but patent data are very unstructured data sets. Conventional finance data usually comes from finance data vendors in a nice format that is well understood and well prepared. But most of the alternative data we are looking at, such as patent data, needs to be interpreted and put into a financial context and in the context of the ESG criteria, which is what we are doing in our projects.<\/p>\n\n\n\n

Can you give an example of such a project?<\/strong><\/h2>\n\n\n\n

Linus Grob:<\/em> We recently finished a research project with EconSight and the University of Basel<\/a>, where we created a Proof of Concept about the classification of a patent to the innovation field, and an improved scoring of the patent value. We at the ZHAW first classified the patents to see to which technology field the patent contributes to. Then the University of Basel forecast the value score of the patents based on the metadata, the textual data and the numerical features of the patent data.<\/p>\n\n\n\n

David Jaggi: <\/em>A patent is a written document with a claim and a description of the innovation. To analyse this data, we use Natural Language Processing models. Each patent belongs to a certain technology field, such as electric vehicles, battery technology or waste management for example. To determine the right category with Natural Language Processing models, we analyse the text within the patterns from our samples and found certain words that correlate with each technology field. For example, if we look at electric vehicles, the text may contain words such as vehicle, battery, loading or charging. And if another patent contains the same words, it is likely that it also falls within this category.<\/p>\n\n\n

Why do we make use of Natural Language Processing Algorithms?<\/div><\/div>
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