The combination of emerging financial services and technology has given rise to the industry called FinTech. Within this broad industry the “alternative data” sector is gathering an ever-increasing importance and it is becoming particularly attractive to investment funds, listed companies and companies which are about to be listed on the stock exchange.
What is alternative data and why is it important to understand to get ahead of the game? The story is recent, but it starts long ago. Aside from a few, large companies that can leverage technology to ripe the full benefit from high-frequency trading (HFT), the rest of the players are forced to look for ever-evolving investment models. Today’s models can handle a far greater amount of data than only five or six years ago, exploiting infinitely increasing computing capabilities. This favourable context, however, does not seem to be sufficient to avoid many hedge funds, typically more receptive to the use of new technologies, from suffering. However, as demonstrated by HFT, technology alone is not enough: BlackRock‘s CEO, Larry Fink, pointed his finger at the active management of his equity funds, and has since shifted budgets and resources to “data-driven” portfolios management.
The keyword is “data-driven”: data is at the heart of everything. The “traditional” financial dataset is available to all operators, but today, in many cases it is no longer enough to create added value, even with a data analyst team at hand and that’s why the search for a new type of dataset. This new type of dataset is best known as alternative data, most of which are digital datasets taken directly from the Net. Some of this alternative data is “structured”, such as weather forecasts or e-commerce data which is easily integrated into traditional models, but in the vast majority of cases it is an unstructured type of data, namely: social posts, blogs, news, comments, reviews, etc. and this unstructured type of data is far more valuable.
Anyone who wants to deal with this unstructured data needs to know it and knowing it is to understand the logic of the digital environments where it is created, the underlying communication logic and the logic distribution in digital contexts. The main reason why many players in the financial arena approaching the world of alternative data encounter enormous difficulties is the lack of digital expertise.
Companies that deal with alternative data and make it available to investors already exist, but there are more emerging around the world, such as American Dataminer, the Irish Eagle Alpha or Canadian Quandl. The first “alternative data” company to be set up in Italy is FinScience. Founded by former senior managers at Google Italia and by digital experts. FinScience has a profound knowledge of alternative data, currently the final users are national as well as international professionals such as hedge funds, Asset Management companies and business banks.
DoItBetter.info went to see how FinScience is structured; we found that digital experts work hand-in-hand with highly experienced investors.
Today, alternative data companies are increasingly incubated in large investment funds and / or business banks, or are developed independently but then acquired, or eventually become investment funds themselves, effectively removing their product from the market. What is certain is that, as certified by HFM Global, the trend for alternative data is now irreversible so you should take full advantage of it.
FinScience adopts an innovative approach to digital data processing for financial purposes, thanks also to the exploitation of the most advanced cloud technologies. The service offered to asset management companies can be summarised in two phases:
- Analysing and identifying the most interesting signals (i.e. themes) revolving around financial assets of interest (stocks, bonds, commodities, … or indexes). The customer is provided with a shortlist of digital signals which, based on a combination of digital and financial data analysis, are potentially “price sensitive” for the underlying assets. For example, FinScience suggests monitoring the “lithium” signal in addition to a number of other signals for the stock Tesla.
- Monitoring signals and scanning new digital signals through a cloud software platform (platform.finscience.com). The platform allows you to monitor all the updates on each signal and at the same time allows you to cross check each digital signal with more “traditional” financial data (prices, volumes, volatility, etc.). You can also access the alternative data through an API system to integrate it into your own quantitative investment models without having to access the online platform.
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