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How Alternative Data can help to create new investment ideas

Marco Belmondo

The term “Alternative Data” identifies all those data coming from environments non-traditionally close to the world of financial analysis of companies, but those digital. All data potentially collected from forums, web, e-commerce platforms, web in general, or maps can be defined as alternative data. These data are often semi-structured or unstructured, such as textual data, images, video or audio, therefore complex to be automatically analyzed, but on the other hand, they can represent a source of information that should not be underestimated.

Alternative data are in fact a specificity of what is known as the Big Data explosion and they are characterized by astonishing growth rates. The market of alternative data is estimated to reach $ 17.35 billion by 2027 (Grand View Research Inc.).

We talk about alternative data especially within the financial world, particularly in the fintech sector, because these data can represent a great opportunity to get more information and therefore invest obtaining a competitive advantage over competitors. The combination of alternative data and machine learning is an incredibly powerful combination to improve the ability to assess a company’s credit risk, in order to optimize the loan portfolio or choices on the stock market. Furthermore, as we will see in this article, alternative data can be particularly powerful in creating new investment ideas.

How alternative data can create new investment ideas

Using alternative data is an excellent choice, not only to learn more about the companies in which you are already investing, but also to find new opportunities. There are now several studies from prestigious American universities, such as MIT or Indiana University, which show how the analysis of social media conversations has allowed traders to earn more compared to those who did not use this type of information. There are now at least ten years of history about this phenomenon, years in which social networks took on an ever-greater value in guiding the choices of investors.

Speaking of highly innovative sectors, one thinks of the social activities of Elon Musk, the now famous CEO of Tesla and founder of SpaceX. Today, it would be unthinkable to predict Tesla’s stock price without following this guru on his social channels. Another example is that of cryptocurrencies, a normally very volatile asset that strongly depends on the sentiment of the moment.

Coming back with our feet on the ground, and on more traditional sectors, the pandemic we have experienced, and that is still going through, offers us a striking example of how the alternative data are now essential. Who would evaluate the sustainability of a company on the basis of 2019 financial statements, today? And equally, how can general considerations be made on the attractiveness or otherwise of a particular sector without considering the effects that the pandemic has had on it?

Putting together traditional and alternative data is the winning solution to anticipate market trends and therefore understand where to invest. This is also because we live in a historical moment in which the search for high-yield investments plays out in a complicated arena, both for large investment funds and for private investors. The truth is that finance in general is looking for alternatives: speed of execution is increasingly important and traditional datasets no longer represent a competitive advantage, since they are now accessible to everyone. In this context, advanced Machine Learning technology and methodologies come to support us in the analysis of alternative data.

FinScience’s solutions based on alternative data

FinScience, fintech of the Datrix group, offers a software solution called Alternative Data Intelligence. The solution, one of a kind, allows you to collect millions of content from the web every day and uses artificial intelligence, providing insights of great value to private investors, represented in a simple and intuitive way. In particular, the platform, thanks to proprietary Machine Learning and Natural Language Processing algorithms summarizes in three indicators the immense amount of analyzed data:

  •     The global digital popularity value, that is the total popularity of a company on the web;
  •     Investor digital popularity value, or rather the popularity of the company specifically from the point of view of investors, that is in the financial sector;
  •     Sentiment, that is the positive or negative perception of the title in the digital world, more generally.

From these indicators, it will be possible to anticipate the main trends of a specific market, to understand and intercept the potential risks for investments and to know the most interesting companies in relation to a specific issue or sectors. In fact, this means identifying weak signals before they become news and providing investment portfolio proposals resulting from the combination of alternative and traditional data. All this with great flexibility and with solutions accessible by all to be used with simplicity.