Alternative Data for Astute Investors 

In 2016, the world generated 16.1 zettabytes (ZB) of data and it is projected to grow tenfold by 2025, to 163 ZB. Most of this new data is unstructured, coming from end-user devices. The rapidly growing sources of alternative data can provide trading signals and help make better predictions, which could encourage investment managers to unlock the potential of alternative data with the aim of alpha generation.

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Alternative Data for Astute Investors:

What is Alternative data

Investing has always been a data-driven enterprise. Classic examples of conventional financial data sets include asset prices and trading volumes, corporate earnings reports, economic forecasts of employment, inflation, housing starts, and consumer spending, exchange rates; and yield curves.

Alternative data refers to the domain that covers essentially metrics beyond the mainstays of government macro figures and firm-level data from financial statements and stock markets. Such data can be gathered from speeches, news stories, television, press releases, presentations, websites, web traffic, Internet of Things sensors, proprietary databases, social media, online communities, communications metadata, satellite imagery and geospatial information.

In the world of finance, Alternative data refers to proxy metrics or information originating from unofficial or non-company sources that individuals can use to gain insight into an investment. Alternative data often provides investors or businesses with an edge over their competitors and companies use it to generate predictive insights. It also helps improve the confidence of analyst estimates or simply improve the speed of estimate generation.

What is Alternative data Examples of Alternative data 

Figure 1: Examples of Alternative data - source:

Growth of Alternative data

Although the idea of seeking information asymmetry has been around for as long as the markets have, using alternative data for an information advantage is a more recent phenomenon. The global demand for alternative data is predicted to grow at a Compounded Annual Growth Rate (CAGR) of 44% to reach 11.1B, by 2026, according to Research and Markets.

Market size for Alternative data

Figure 2: Market size for Alternative data - source:

Pioneers of Alternative data

Hedge funds have been at the forefront of Alternative data innovation. They were among the first innovators to deploy Alternative data methods for competitive advantage. A benchmark for Investment Management firms’ adoption of Alternative data for alpha is Everett M. Rogers’ Diffusion of Innovations Curve (Figure 2). 

Diffusion of Innovation Curve 

Figure 3: Diffusion of Innovation Curve - source:

As early as in 2008, MarketPsy Long-Short Fund LP, a quantitative hedge fund, fed social-media sentiment data into its investment models. Around that time, alternative data also gained traction with the academia, where a research study by Bollen, Mao and Zeng reported correlations between Twitter mood and the Dow Jones Industrial Average (DJIA), including an 87.6 percent accuracy rate of predicting the up and down movement in the DJIA within a few days’ time. Based on these findings, a top, London-based hedge-fund manager even launched an exclusive fund.

Advantage Alternative data

Relative to their government counterparts, three potential benefits of non-traditional data sources stand out. 

Timely measurement of the economy: Learning in close to real time about aggregate developments in the economy that will be reflected only later in statistics released by the government is a distinct feature of the use of unconventional datasets. As the Covid-19 pandemic unfolded in the US, steep job losses were beginning to occur as early as in March 2020. These huge deficits were visible in the Bureau of Labour Statistics (BLS) report released only in the first week of May. However, data from payroll processor ADP by the end of March 2020 indicated that private employment was declining sharply. By the end of April, the ADP data clearly portrayed an unprecedented collapse and proved to be quite accurate in forecasting the employment calamity.

US Employment data

Figure 4: US Employment data - source:

Granularity: Besides providing timely information about aggregate statistics, unconventional data often also allow for more detailed measurement or granularity. The finer granularity could be related to frequency (e.g., daily data), geography (e.g., data broken down by region), or individual characteristics. The ability to do granular analyses in almost real time could allow for faster evaluations of the costs and benefits of the variables at play. Due to their nature some non-traditional data sources may provide leads on aspects of firm or consumer behavior for which no standard government data source is available (even with a lag). 

Impact of Covid-19

Figure 5: Impact of Covid-19 - source:

As an example, during the early weeks of the first wave of the pandemic, the north-eastern parts of the US experienced more severe COVID-19 outbreaks compared to the rest of the country. Aggregate statistics generated at that time did not allow a thorough assessment of the economic effects of the pandemic. Instead, non-traditional data helped to better understand the links between health shocks and the responses of economic variables. For example, the data on public transportation in New York City helped many analysts to get a better understanding of how individuals and businesses would react to rising COVID-19 cases.

Crisis-specific data gathering: The availability of data from a variety of source points allows decision makers to answer specific, unanticipated questions that are unique to a certain situation. Consider office occupancy which had a precipitous drop during the pandemic because businesses switched to remote work or because they laid off their employees. Data on transit ridership and on office occupancy informed in real time how quickly employees stopped coming to offices and, later during the pandemic, how quickly businesses returned to in-person work. These metrics indirectly convey information about the state of the labor market and the location and form of the majority of employment.

Within the domain of financial markets, the varied nature of datasets has given rise to many different potential applications of Alternative data. Identifying alpha elements, Predictive analytics, Research verification, Sentiment analysis and Surveillance to identify financial crimes are but a few examples of use cases for Alternative data by professional investors.

Alternative data is helpful to investors in the following ways: 

  • Offering a competitive edge 
  • Aiding in predictive analytics 
  • Supporting investing and trading decisions 
  • Preventing risks 

Competitive edge 

The new information provides an opportunity for investors to gain a competitive edge within the financial markets space. Hence, players use alternative data together with conventional data to improve the latter dataset or come up with novel and unique insights. Companies are integrating alternative data sets with core financial information and readily available data describing people, products, and industries, thereby deriving a unique view of the markets. 

Predictive analytics

Investment managers use alternative data to track e-commerce, real estate offerings, news and job change data and subsequently predict earnings reports. 

Support for investing and trading decisions 

A 2017 report from Deloitte has warned that investment firms that do not update their processes to incorporate alternative data within the five years ending 2022 could face certain strategic risks. And that competitors who effectively integrate Alternative data in their operations can outmanoeuvre the firms. The report encouraged investment managers to use Alternative data to support their trading decisions.

Risk prevention 

Trading frenzies such as the one on GameStop and AMC could have been well managed had the investment management firms optimized public data to generate informed predictions. Firms can also look for short-term data from various sources that will help them anticipate and respond appropriately to news.

Examples of Alternative data for the investment sector

On the investment landscape, there are four types of alternative data that are playing an increasingly important role. 

Media Sentiment: Even while it reports on share price movements, the media also often influences those movements through news and op-ed articles. At a time when thousands of articles and blog posts are produced every day, it is no trivial task for an investor to gauge whether overall media coverage of an asset is positive or negative. Sentiment indicators offer a solution. Sophisticated algorithms that can digest up to 160,000 articles every day and report the overall tenor and intensity of media coverage on specific equities are now available.

Consumer Behaviour: Consumer foot traffic has a huge influence on the bottom line of retailers. Earlier, analysts used to visit stores in person to count the number of cars in a parking lot, thereby predicting future earnings. Today, data providers track Global Positioning System (GPS) signals from smartphones to ascertain when phone-carrying consumers are in a store, and then tally the total number visiting that store in a given time frame. 

Institutional Investor Behaviour: Assessing consumer behaviour can provide important insights on an individual company's prospects. But by assessing trends in behaviour by fellow Institutional Investors such as pension funds and hedge funds, asset managers may set themselves apart from the rest. Sectors, assets and countries that receive the most capital inflow can be tracked along with over- or under-weightage status. 

Environmental, Social and Governance (ESG): Never before have the environmental, ethical and social impacts of companies been under the microscope like they are now. Investors are rightly concerned about publicly traded companies being at risk of reputational damage, government fines or other consequences due to poor ESG practices. To meet this growing demand for ESG information, data providers have developed metrics and are assessing companies on everything from their carbon footprints to the diversity of their boards. 

Jens Nordvig, Exante Data explains in this Barron’s podcast the evolving role of alternative data in making investment decisions.

Alternative data: Obstacles to adoption

Investing in Alternative data raises hopes but implementing it is no easy task. Seen through the lens of an investment management firm, there are many obstacles to consuming alternative data.

  • Alternative data is unstructured -- makes it difficult to receive, integrate and consume content
  • Often, the data is incomplete or unverifiable
  • Finding the right data is hard
  • Concerns about privacy

Take home

Strategic deployment of alternative data to extract insights and drive informed decision-making is at a nascent stage in the investment management industry. Combining traditional data with alternative ones will immensely benefit firms and help deliver a competitive edge through information advantage, which in turn will help drive alpha. The advantages of infusing alternative data sources into investment strategies far outweigh the challenges and firms must take steps proactively toward incorporating them within their operations.

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Visit for a FREE trial to choose the Alternative data points that can help in your quest to generate alpha. 

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