• 7 October 2020

Alternative Data Sources for Hedge Funds

Alternative Data Sources for Hedge Funds

Alternative Data Sources for Hedge Funds 768 432 FinScience

What is a hedge fund?

A hedge fund is an investment fund which trades in relatively liquid asset and is able to make extensive use of more complex trading, portfolio-constructions and risk management techniques to improve performances. The hedge funds are considered as alternative investment, which is an investment in any asset class excluding stocks, bonds and cash. There are completely different from the regulated investment funds available to the retail market, in fact they have a huge ability in an extensive use of leverage and of complex investment techniques.

They are also dissimilar to the private equity funds, that are typically limited partnership with a fixed term of 10 years. As matter of fact they are generally open-ended, meaning that they allow investors to invest and withdraw capital periodically based on the fund’s net asset value.

How can it be combined with alternative data?

Alternative data refers to data used to obtain insight into the investment process. These data sets are often used by hedge fund managers and other institutional investment professionals within an investment company, in order to gaining an edge over their competitors. Alternative data sets are information about a particular company that are beyond the typical company filings or fundamental datasets.

They are published by sources outside of the company, which can provide unique and timely insight into investment opportunities, so that they are called alternative data.

Alternative data sets are often categorized as big data, which means that they may be very large and complex and often they cannot be managed by a traditional software. Furthermore, since they originate as a product of company’s operations, these data are often less readily accessible and less structured than traditional sources data.

Despite all the difficulties connected to the alternative data, many investors have turned to alternative data after finding official number too slow in reflecting the modern times, especially during the collapse in economic activity due to Covid-19. In fact, a recent MarketWatch article pegged total Alternative Data spending for buy-side firm at $1.1 billion in 2019 and projected sales of $1.7 billion for 2020.

The growth of alternative data

More than half of hedge fund managers are now using alternative data to gain a competitive edge by generating outperformance and supporting the risk management processes.

The growth in alternative data has a number of drivers. After all, the advent of machine-learning technology and the drop in the cost of computing power have made it much less expensive to crunch ever-larger sets of data, a phenomenon that’s led a number of traditional, active asset managers to increasingly incorporate quantitative investing techniques into fundamental-oriented models.

How to manage alternative data

As already said, nowadays the alternative data analysis is a source of competitive advantage for the investment management sector. In fact, by properly managing these data sets it is possible to gain more and more real time information that could be crucial for the decision process. 

FinScience understood these new trends and provided lots of solutions in order to find out the signal hidden in the massive data generated by the digital ecosystem. The goal of FinScience solutions is to uncover unique investment opportunities:

  • From massive volume of Alternative Data
  • Through proprietary AI algorithms
  • By human expertise & methodology.

FinScience extracts information form Alternative Data and it is possible to get them through the Platform, which puts the power of AI with a unique, intuitive, and easy-to-use dashboard. FinScience Platform leverages the power of machine learning and skilled analysts to: monitor specific companies, spot trends before they are trending, give access to a high added-value selection of companies and themes, and analyze in depth specific topics or phenomena (Knowledge Graph). Moreover, FinScience Platform has some main indicators, such as:

  • The Digital Popularity Value (DPV): which represents a proprietary indicator that measure the popularity of a digital signal on the web related to specific companies or topics
  • Sentiment: which measures an entity’s perception within a specific environment
  • The Investors DPV: which is a DPV component calculated considering exclusively digital content related to the financial ecosystem
  • DPV Volatility: that is the amount of DPV change an entity experiences over a given period of time.

An example that can help to understand how the FinScience Platform is useful to analyze all the needed information to take decision is the next one: an investment firm needed to unveil possible risk factors when Tesla stock price was rising although negative event such as “deadly auto-pilot crash”. The solution implemented by FinScience started to monitor, analyze and set automatic alerts on Tesla’s company in order to uncover other relevant and hidden signals that may impact the automotive company’s financial performances. The investment firm uses that information to better manage their financial positions.

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