Make Better Investments Than Competitors With Alternative Data
A situation hedge funds and other financial managers face prior to investing in a company is the amount of data they have to sort through. The sources of these data are usually financial statements, Security and Exchange Commission filings, management presentations, or press releases. But this data might not always be the most reliable or the most accurate.
Over the last 10 years, companies have emerged that collect, clean, analyze, and interpret “alternative data” that are sourced from financial transactions, credit card histories, IoT sensors data, mobile devices, satellites, public records, and the internet. That has led to an increase in the popularity of alternative data, with roughly half of all investment firms today using alternative data to base their decisions on.
In this article, we aim to explore more the concept of alternative data, how it’s generated, and where it can be used by financial investors and hedge fund managers.
What is alternative data?
Alternative data is a relatively novel form of data, gathered from unconventional sources, that guides an investment.
Advice for financial managers: Gain relevant and insightful data about a company by sourcing your information from alternative data sources such as the Internet, social media, and smart devices, rather than traditional data resources.
Use alternative data in moderation
Hedge funds and investors should aim to use both traditional and alternative types of data in order to maximize their ROI on their investment.
But where is the sweet spot between using the two types of data?
Figure 1 showcases the investment management (IM) constituents by phase. At both ends of the spectrum, there exists a risk: avant-garde hedge funds relying on alternative data alone might lose sight of equally important traditional financial information, like SEC filings.
On the other hand, firms that are reluctant to embrace the novel approaches to data collection will miss out on gaining a comprehensive picture of a company’s health than numbers alone are unable of producing.
The sweet spot, with the least risk and the highest reward, is somewhere in the middle of the curve, with companies using both the alternative and traditional data sources of data in tandem with one another to make informed and data-driven decisions.
What is the alternative data ecosystem?
The following are some of the stats regarding alternative data:
- By 2018, there were 445 authorized alternative data providers (Figure 2). That’s an increase of almost 641% since the turn of the century.
- 78% of hedge funds are expected to use alternative data, up from 52% in 2016.
- In 2020, 38% of hedge fund managers who were using alternative data globally believed that alternative data will be widely adopted in the next three to five years.
Recommendation: Choose amongst the growing number of alternative data providers by looking at where they specialize in data mining (i.e. is it credit card information? is it social sentiment?).
How is alternative data generated?
The reason why alternative data has flourished is due to the variety of its origins. There are mainly three ways by which alternative data can be generated:
Individuals produce huge amounts of data (1.7MB of data per second). Some of the locations where data is created are social media platforms, e-commerce websites, such as Amazon or eBay, and trends on search engines like Google, Bing, or DuckDuckGo.
Also known as “exhaust data” — as it mainly comes in the form of a by-product of different business processes, such as credit card transactions, sales transactions, and interactions with government agencies (e.g. tax filings).
In the era of technology and the IoT, devices like CCTV, machines, POS systems, and even parking lot sensors are able to pick up and transport information from the surroundings to the cloud.
Satellite imaging and Geo-location devices are other sources of alternative data being created from sensors.
What are the main types of alternative data?
Web data includes information relating to web traffic, popular web searches, demographics, click-through rates, etc. This type of data is useful for advertising campaigns or the popularity of websites or products, as well as providing excellent insights for market research and e-commerce. Retailers, for example, can themselves look at Google Trend’s data visualizations to learn more about the growing popularity, or lack thereof, of a product (see Figure 3).
Social sentiment data
This includes data resulting from the processing of social media posts and comments, including public reactions to the news, product ads, collaborations, etc. These data can come in the form of textual posts, images or videos, and are capable of providing insights into current trends and brands virality.
For example, in recent years Twitter sentiment (see Figure 4) has become a popular method of gauging public reactions to a product release, event, or public announcement.
Geo-location data is typically gathered from mobile electronic devices and GPS signals. This type of alternative data provides a significant amount of information for making location-based decisions.
For instance, retail stores can use this information to decide the best streets or neighborhoods to expand their business to.
Credit card transactions
Credit and debit card transactions are useful in tracking retail revenue. For instance, they can give insights on how often an individual pays their bills, which is a good indication of their moral hazard risk, and input into credit scoring models.
Email receipts, a substitute for paper receipts, refer to the electronic records obtained on the delivery of products or services. They are useful for keeping track of the sales amount.
In November 2016, more than 3 million product receipts of GoPro were evaluated, which indicated a drop in sales volume. This caused a drop in GoPro’s market share.
Similar to Geo-location data, satellite imagery is a tool for measuring the economic activities across a wide region, through the use of satellites or low-level drones.
An example could be an investment firm using satellite image data to extract information from parking lots to see how many cars are parked at different intervals during a working day. Car traffic could be a signal of the economical health of the area.
Data on weather patterns can be collected from precipitation sensors, pressure sensors, thermometers, etc. They are useful for analyzing how the environment of a business could affect its economic activity.
Advice for financial investors: Use alternative data to learn about how the environment in where the business is located could impact its day-to-day functionality.
How do you gather alternative data?
There are three ways by which alternative data can be obtained:
Web scraping is used for automating the extraction of data from targeted websites, by downloading the necessary and relevant information through a series of text processing functions. The information then can be saved onto a spreadsheet or converted into an interpretable format.
Raw data is the unprocessed information that is obtained from any source, such as a sensor. It mainly consists of numbers and is usually not subjected to any form of cleaning, noise removal, or other types of processing. Some public resources of raw data are:
- Government public websites (e.g. US Census Bureau, Data.gov)
- Data aggregation websites (e.g. our world in data)
Besides public sources, businesses can also reach out to data providers to buy their desired data. The following are data-driven lists of providers for:
There are licensed companies that are able to recover exhaust data, such as credit card transactions, POS transactions, and more from the company that is seeking to land investments. Thinknum, Yewno, and InfoTrie are some of the third-party providers of alternative data.
For more on FinTech
To explore other technologies used in finance, read our in-depth articles:
- Top 8 Chatbot Use Cases / Applications in Finance
- Anti Money Laundering Algorithms: Tackling AML with AI
- Top 5 RPA Use Cases / Applications in Accounting
- AI Credit Scoring Models: In-depth Guide
If you are looking for a FinTech solution for your business, check out our comprehensive lists of:
And we can help you find the right tool for your business:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
To stay up-to-date on B2B tech & accelerate your enterprise:Follow on
Next to Read
Your email address will not be published. All fields are required.