October 15, 2020
With a market size of 1.06 billion in 2019 according to Grand View Research, the alternative data space is becoming a necessity for investors, analysts, and hedge fund managers alike. But why is alternative data on the rise? The answer isn’t as straightforward as one would think. Alternative data compiled with traditional data offers investors and analysts business insights that are both predictive and reliable. This article will explain the essentials of alternative data as well as its importance to investors and hedge funds.
So, what exactly is alternative data? Alternative data refers to data that exists beyond the sphere of traditional data sources. These alternative sources are utilized by investors, analysts, and hedge funds to aid in company and investment evaluation. Some brief examples of alternative data include public social media/sentiment, satellite and weather sensor data, transactional data from credit and debit cards, email receipts, survey data, and much more.
In increasingly data-driven financial markets, alternative data provides investors with fresh sources for enhancing their alpha. Alpha, also known as an active return on investment, measures the performance of an investment based on an appropriate market index. Ultimately, alternative data provides investors with robust data insights for their investments, increasing their active return on investment.
Alternative data, sometimes referred to as exhaust or external data, involves data generated by three sources: individuals, business processes, and sensors. Whereas traditional data is exclusive to data that’s produced by companies themselves, alternative data relies on collection and scraping processes. For example, traditional data can be things such as investor presentations, SEC filings, financial statements, and press releases.
While both alternative and traditional data provide financial insights of a given company, the progress surrounding the alternative data sector provided investors and hedge funds with a more complete picture of short-term and long-term investments and business decisions.
Over the past decade, alternative data sources have grown significantly. In its infancy, alternative data sources included credit card transactions, web-scraped data, and geolocations. As alternative data continues to prove its success, along with the advent of new data sources, experts in alternative data are finding new ways to retrieve these datasets as well as analyze them.
Alternative data, sometimes referred to as exhaust or external data, involves data generated by three sources: individuals, business processes, and sensors.
According to Forbes, alternative data has expanded to include a taxonomy of 24 different types of data. Beyond these 24 data types, alternative data, as mentioned earlier, is generated from three main sources: individuals, business processes, and sensors. Let’s take a more detailed look as to what they entail.
While individuals provide large amounts of robust qualitative data, alternative data generated by individuals also proves to be highly unstructured and often difficult to sift through. For this reason, it’s important that datasets consisting of individual data are well-structured and that only the essential data points get picked. Some individual data sources include:
Unlike individual-driven data, businesses tend to produce structured data that provides investors with comprehensive financial insights for a given business. Some business process data sources include:
Another largely unstructured data type, data generated from sensors, is expanding in the alternative data field. Due to the interconnectivity of devices and the development of sensor-based technology analysts and investors alike find this data extremely valuable. Some sensor data sources include:
As mentioned previously, alternative data is acquired through web-scraping, acquisition of raw data, and third-party licensing.
Web-scraping processes unstructured data from web pages and online sources. Through data parsing methods, the data is transformed into structured and readable CSV and JSON formats ready for interpretation by analysts and investors. This is the sort of data that Coresignal offers.
Raw data acquisition is the process of obtaining sizable amounts of unprocessed data, obtained from multiple source types. This data type contains lots of noise and requires significant processing in order to be analyzed and utilized by investors.
Third-party licensing refers mainly to recovered financial data involved in transactions. Companies recover said transactional data like POS data, credit card transactions, etc. and package it in easy to read formats for investors.
Due to alternative data’s multiplicity of use cases, many professionals have found use for alternative data. Professionals that utilize alternative data the most are algorithmic traders, also known as quants, who use the datasets they acquire to construct computer models for trade. However, alternative data is also utilized by hedge funds, analysts, and other institutional investment professionals.
Alternative data covers a broad range of factors that influence a company’s alpha, making alternative data sets a commodity for predicative investing. Specifically, investors consider factors such as scarcity, granularity, history, structure, and coverage when choosing what types of alternative data sets to invest in.
Hedge funds utilize some of the most well-known alternative data sets, including social sentiment, credit card transactions, and web data. These data types provide hedge funds with competitive intelligence and risk management insights due to their pervasiveness in the alternative data space. Additionally, hedge funds find alternative data favorable due to the accessibility of alternative data compared to traditional data, which sometimes takes longer (due to legal processes) for official figures.
Further, some widely used data sets such as credit card transactions and traffic patterns, are important to both private equity firms. Because private equity investment decisions differ from hedge funds’, in that they tend to be focused more on the long-term, these data sets ability for long term scope provide both short term and long term insights.
Whether you are an investor, an analyst, or an algorithmic trader, understanding the untapped potential of alternative data sets is paramount. Based on the proven success of alternative data, it seems like the sky's the limit to what a business can achieve by leveraging it.
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