Private Equity Firms and Asset Managers – optimizing the application of Alternative Data into workflows
Alternative data is hot. It has been hot for the last 5 years, initially picked up by hedge funds, later followed by asset managers and now in areas like private equity and real estate as a source to improve investment performance. Within these investment companies, the use of alternative data spreads into areas like Risk and Business Intelligence.
But where to start? The variety of alternative data is enormous, going from social media to satellite imagines, traffic monitoring to news analytics. Like artificial intelligence, it has become a must-do and must-have, if investors want to retain their competitive edge in the foreseeable future.
The main reason for using alternative data is the potential it has to get additional signals can lead to increased performance. We also see more demand for alternative data from the risk domain in order to obtain better insights and control. But with interest come several challenges:
• Selection process from the increasing number of new alternative data vendors
• Determining the cost-benefit outcome and probability of success (e.g. through backtesting data and their relevance in current market climate)
• Integration of this data in existing investment process
• Quality assurance and alignment with internal procedures, governance and compliance
• Sources of the data – many alternative data vendors offer a black box solution with limited explanation on what happens beneath the surface.
In order to showcase the value of a new data alternative dataset, it must be usable in an investment process. The integration of unstructured with structured data is therefore more relevant.
In order to display the value of alternative data, the use of machine learning and artificial intelligence is of great help. With these techniques, an unstructured dataset can be turned into signals that improve insights for both the investment process, as well as risk management. The techniques can also be used for turning unstructured data into structured data.
Owlin does just that: it turns millions of news sources into actionable insights and signals by leveraging artificial intelligence and natural language processing. To make it usable for our clients, these insights can be either displayed in a terminal (web-based dashboard) or obtained via an API connection.
With our dashboard, clients directly see the “forest through the trees”, meaning they are able to spot the big picture and only focus on those elements that need attention. This alone saves a lot of time. The additional insights and sentiment analysis give direction in the investment process. Our data feed offering allows for easy integration to structured data, which enables the opportunity to add additional signals into e.g., multi-factor models, improving insights and performance.