What is alternative data?

Alternative data is data generated either by individuals, business processes, or sensors that are collected from non-traditional sources. For example, traditional data is exclusive to data that's produced by companies themselves, while alternative data relies on the collection and scraping processes. Specifically, traditional data can include investor presentations, SEC filings, financial statements, and press releases. In contrast, alternative data includes externally sourced factors such as company size based on headcount or revenue, location of HQ and branch offices, website traffic, reviews, employee salaries, organizational structure and much more. 

Alternative data is most commonly acquired through web scraping of public web data, acquisition of raw data, and third-party licensing.

There are various ways businesses and investors can leverage alternative data. For instance, within investing, the 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.

Additionally, businesses can utilize alternative data to help fuel and build AI-based technologies, generate more business opportunities, conduct competitive analysis, and enrich their existing datasets. More specifically, HR tech companies, lead generation platforms, B2B, and B2C companies are all able to extract valuable insights from alternative data.

What is alternative data in finance?

So, what exactly is alternative data in finance? 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 vs. traditional data

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 the 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 into 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.

The different types of alternative data

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.

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 at what they entail.

Individuals:

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:

• Social media
• Product reviews
• Search engines
• Comments and web interactions

Business processes:

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:

• Credit card transactions
• Sales data
• Corporate data
• Government data

Sensors:

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:

• Geo-location
• Satellites
• POS data
• CCTV
• Other devices

How is alternative data acquired?

As mentioned previously, alternative data is acquired through web-scraping, acquisition of raw data, and third-party licensing.

Web scraping

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

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

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.

Who uses alternative data?

Due to alternative data's multiplicity of use cases, many professionals have found a 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 predictive investing. Specifically, investors consider factors such as scarcity, granularity, history, structure, and coverage when choosing what types of alternative data sets to invest in.

What data do investors use?

Investors utilize some of the most well-known alternative data sets, including social sentiment, credit card transactions, and web data. These data types provide investment firms with competitive intelligence and risk management insights due to their pervasiveness in the alternative data space.

Additionally, investment firms 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 and hedge funds. On one hand, private equity investment decisions tend to focus more on the long-term, while on the other hand hedge funds focus on both long-term and short-term. Both hedge funds and private equity firms benefit from alternative data because alternative provides robust short-term and long-term insights.

Continue reading for an overview of what data Coresignal offers and its uses.

Coresignal's alternative data

Below you will find our full data offering consisting of a total of 20 unique alternative data sources in 8 categories.