Traditionally, investment firms and financial advisors have had to rely on information from sources like press releases, news stories, quarterly financial reports, and stock offerings to decide which companies and assets to recommend or invest in.
But as both people and information move faster due to the rapid advance of technology, investors are looking for more immediate ways to gauge what’s going on in the market. So they are increasingly turning to alternative sources of data for quicker insights and different angles than what they would get from traditional financial information sources alone.
But what is this “alternative data”, and how are investors using it to make financial decisions faster and with greater accuracy? We’ll explain by way of some key definitions, as well as a selection of alternative data use cases to demonstrate financial functions that can benefit from using alternative data. Here’s what’s in store:
We’ll start with an explanation of what alternative data is, and why organizations are increasingly utilizing it.
“Alternative data” is a term used in the financial services industry to describe data collected from non-traditional sources. This includes any data that an organization can’t either generate from its own operations or collect from official public sources (press releases, government agencies, etc.).
Alternative data can be any type of data, but one type that’s being increasingly used is geospatial data. This is because it’s important for investment firms on both the buy-side and the sell-side to understand the geospatial components of consumer behavior, as well as the insights they can provide. For example, seeing that foot traffic to a specific store has decreased over a certain time period may be valuable for analysts looking to make a recommendation on which brands to invest in (or not).
“Buy-side” and “sell-side” don’t refer specifically to buying and selling assets. Instead, these terms refer to different roles within the investment industry. “Buy-side” companies and jobs have more to do with directly making investment decisions for specific funds. “Sell-side” companies and jobs, on the other hand, are more about providing services and information to help out multiple investors at once.
Here, we’ll explain in more detail what an analyst in each type of role does, and why they might use alternative data.
Sell-side analysts typically work for brokerage firms or investment banks. Their job is to provide and/or sell financial services to the firm’s clientele. These include useful market information and recommendations on what securities to buy, sell, or hold onto. Their goal is to keep clients doing business with the firm so that the firm gets paid a commission whenever a client completes a transaction.
They are typically under pressure to provide the newest, most relevant, and most accurate financial information to clients before their competitors do. However, researching companies and collecting data to build reports can be very expensive and time-consuming. That’s why they may rely on alternative data sources to broaden and speed up their analyses.
Buy-side analysts typically work for firms that, on behalf of clients, directly invest in other companies by buying and selling assets (e.g. stocks). Their job is to survey one or more business sectors for good investment opportunities, then make recommendations exclusively to fund managers (as opposed to all of their company’s clientele or the public) based on a fund’s investment strategy.
Because buy-side analysts usually have to monitor more of the overall market at once, they often rely on getting credible information from sell-side analysts. However, since buy-side analysts also have a very small margin for error, they will often do their own research to check it against the recommendations of sell-side analysts. Again, this is where using alternative data can help broaden and speed up traditional research that often takes a lot of time and money.
So those are the kinds of people in finance who are likely to use alternative data. But what exactly can they use it for? We’ll cover some possible scenarios in the next section.
Alternative data has several applications for companies and positions on both the buy-side and sell-side of the financial markets. Here are a few examples of what alternative data can be used for in investing, and what type of role would most typically make use of it.
Role type: Sell-side
A big part of sell-side analysts’ work is making educated guesses about how a stock’s price or a company’s financial standing will change. Alternative data helps them factor in many different variables that could affect this change, such as supply of goods/materials, consumer demand, and economic trends. In some cases, alternative data can be used to predict financial performance in a new market based on modeling done in a previous market.
Role type: Sell-side
Sell-side analysts can also use alternative data to anticipate future increases or decreases in consumer demand for certain products or services. This can be critical for predicting how particular investments will perform. Foot traffic and transaction data are especially useful for this, because they show how consumers interact with specific stores and brands.
Oftentimes, financial analysts will develop demand forecasting models by looking at foot traffic or transaction data in two areas with a similar geography, or the same area in two comparable time periods. POI data with open and close attribution can also be useful for this analysis, because it indicates how many stores are opening or closing in an area. This can be used to measure the relative health of a business or industry.
Role type: Buy-side
On the buy-side, analysts from hedge funds, private equity firms, and the like leverage alternative data in their research. After all, their initial investment research is critical for finding a profitable deal that avoids unnecessary risk. So the more details they have about an investment, and the more accurate those details are, the better their reporting and recommendations will be.
For example, buy-side analysts can enrich their financial analysis with POI, property, and mobility data to see how consumers interact with stores at specific locations. What other points of interest are in the area, and do they help or hurt the business? How much foot traffic does the area get, and when? Do people actually enter the store or just walk past it? Do customers tend to buy particular brands at a specific store? All of these factors can signal whether a potential investment is worth seriously considering or not.
Role type: Buy-side
Once buy-side analysts have narrowed down the assets, funds, or companies they want to seriously recommend their fund managers invest in, they must perform rigorous due diligence. They must learn as much as possible about the potential risks and benefits associated with the investments they’re considering. There is potentially a lot of money on the line, so analysts need to be extremely confident in their reporting and recommendations.
Alternative data allows analysts to dive extremely deep into their due diligence research and consider all the angles. They might be able to uncover unforeseen anomalies that point to an investment having more risks than its potential rewards warrant. On the other hand, they may also discover some hidden opportunities that make an investment more lucrative than it was first believed to be.
Role type: Buy-side
Even after a firm purchases shares in a company or other financial assets, they still have to maintain their investment portfolios. Past the initial deal sourcing and due diligence, private equity firms and hedge funds need to monitor how well each investment is doing and plan its future with their company. Alternative data can help monitor changing dynamics related to each business or asset, and thereby help firms make decisions about whether or not to maintain specific investments.
Role type: Sell-side
Sell-side analysts tend to have a wider margin for error than buy-side analysts, so they’ll usually prefer speed to accuracy when collecting and disseminating financial information. This is where alternative data is useful, because it’s often more immediately available or accessible than traditional financial metrics.
As an illustration, official sources of information such as press releases and quarterly financial reports are usually accurate signals of a company’s performance. But they are only sporadically accessible, as they are usually only released on the company’s timetable or in reaction to a major event. In contrast, companies tend to update their social media feeds much more often in an effort to stay engaged with their customers.
By paying attention to these latter channels, analysts can get a more immediate sense of what’s happening at a company, and how consumers are interacting with the brand. This can give them signals that allow them to provide predictive investment advice faster than some competitors, who may merely react to financial news once it becomes official.
Read our blog about up-to-date store open/close data to learn how alternative data is used to stay on top of a rapidly changing business landscape.
Role type: Both
Another use of alternative data is in observing relationships between stores, brands, or business sectors, especially over specific geographic areas. For example, some companies may be doing better or worse in certain places because of other businesses in close proximity or in the general area. So performance trends may be dependent on competing or complementary businesses moving into or out of the vicinity.
To illustrate, a health food store or restaurant may be doing well because nearby gyms attract fitness-conscious people to the area. But it may see a sudden downturn if those types of complementary businesses close down or move away. Analysts may want to take relationships like these into account when choosing which companies to invest in, and when.
POI and mobility data can also point to potential future partnerships, mergers, or acquisitions. As an extension of the example above, a business moving into a new market may be able to establish itself quickly if it cross-promotes with other nearby companies that cater to similar lifestyles but don’t directly compete.
On a more granular level, by looking at publicly-available data on corporate transportation, analysts can find patterns in where a company’s executives have been traveling lately. Based on which similar businesses are at the destination(s), it might allow analysts to anticipate an acquisition, merger, or other partnership in the works before it becomes widely known.
Read our ultimate guide to trade area analysis to see how geographic patterns can inform investment decisions.
Role type: Both
Research, marketing, entertainment, and business transactions are increasingly moving to the online world. So it makes sense that the Internet can provide a wealth of data that could be turned into financial insights. This includes not just people’s online buying, bidding, and selling behaviors, but also their other online activity as a reflection of how they might spend money in the real world.
As an illustration, if people visit a website or use an app associated with a specific brand, it could indicate that they intend to spend money on it. For example, if someone visits a musician’s website or social media feed, it may signal that they’re a fan and will buy that artist’s music or concert tickets (at least at some point in the near future). Or if someone downloads an application to track a sports league, it’s reasonable to think that they may eventually buy game tickets or merchandise (if only for a specific team).
Analysts can use web traffic and app usage to see which companies are hot or not, though they may need to dig a bit deeper to determine how much of that attention is positive or negative (as bad publicity can signal an impending downturn).
Taking this a bit further, online shopping has become increasingly common due to the convenience of being able to buy, sell, and bid from the safety and comfort of one’s own home. So foot traffic at brick-and-mortar stores might not tell the whole story of how well a business is doing. Financial analysts may need to look at things like online payments (through credit cards or payment processing services such as PayPal or Venmo), or activity surrounding logistics companies or warehouses. This data, when combined with information regarding consumer-facing storefronts, can give analysts a more complete picture of a company’s overall sales and performance.
Learn more about connecting multiple alternative data sources for deeper financial insights.
In short, alternative data allows investors and analysts to look at companies and assets through several unique lenses. This can enable them to anticipate market trends before they happen, or to see investment opportunities or pitfalls that they might not if using only traditional financial data. For more information on how alternative data is shaping the financial markets of today and tomorrow, check out SafeGraph’s financial services use cases for geospatial data.
