Alternative data

What Is Alternative Data?

Alternative data refers to non-traditional data sets gathered from diverse sources to provide insights into market trends, company performance, and economic activity, going beyond the information available through conventional financial reports and public filings. Within the broader field of investment analysis, alternative data offers a crucial edge by providing a more dynamic and often real-time understanding of underlying factors influencing asset values. This type of data is increasingly employed by financial professionals, particularly in hedge funds, to inform their strategies and refine their investment process.

History and Origin

The concept of leveraging non-traditional information for investment decisions is not entirely new; pioneering quantitative investors have utilized such data for decades. However, the widespread adoption and institutionalization of alternative data began to accelerate significantly in the last decade, fueled by the explosion of digital information and advancements in data processing technologies³⁷.

Initially, alternative data was primarily the domain of sophisticated "quant" funds and market makers who possessed the technological capabilities to ingest, warehouse, and interpret large, often unstructured, data sets³⁶. As the digital economy expanded, so did the volume and diversity of data generated as a "data exhaust" from various activities—ranging from consumer transactions to satellite imagery. ³⁵This growing availability, coupled with the competitive pressures on asset managers to differentiate themselves and seek alpha generation, propelled alternative data into the financial mainstream. A 2021 report by the Alternative Investment Management Association (AIMA) in collaboration with SS&C highlighted this rapid growth, noting that the number of alternative data providers had grown more than 20-fold over the preceding three decades, with over 400 active providers compared to just 20 in 1990.

³⁴## Key Takeaways

• Alternative data provides non-traditional insights into market dynamics and company performance, complementing conventional financial information.
• It encompasses a wide array of sources, including transaction data, satellite imagery, social media activity, and web traffic.
• The use of alternative data is driven by the desire for a competitive edge and the increasing availability of digital information.
• Advanced analytical techniques, such as artificial intelligence and machine learning, are crucial for processing and extracting value from alternative data.
• Regulatory scrutiny and ethical considerations regarding data privacy and fair use are significant challenges in the alternative data ecosystem.

Interpreting the Alternative Data

Interpreting alternative data involves transforming raw, often unstructured, information into actionable insights that can influence investment decisions. This process typically requires robust data science capabilities to clean, normalize, and analyze the data. For instance, analyzing consumer behavior through credit card transaction data can provide a granular view of a company's sales performance before official earnings reports are released. ³³Similarly, tracking container ship movements via geospatial data can offer real-time indicators of global trade activity or a company's supply chain health.
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The interpretation of alternative data often aims to identify patterns, predict future trends, and corroborate or challenge existing assumptions derived from traditional sources. Investors evaluate the data's timeliness, relevance, and predictive power, frequently integrating it into their financial modeling and quantitative analysis frameworks to enhance forecasting accuracy.
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Hypothetical Example

Consider an investor analyzing a publicly traded fast-food chain. Traditionally, they might wait for quarterly earnings reports and analyst forecasts. However, using alternative data, they could gain a proactive view.

Scenario: The investor subscribes to a service providing anonymized mobile device location data for major retail establishments.

Step-by-step analysis:

1. Data Acquisition: The service provides daily aggregated foot traffic data for all locations of the fast-food chain in a specific region, updated with a 24-hour lag.
2. Trend Identification: Over a three-week period, the investor observes a consistent 15% increase in foot traffic at the fast-food chain's locations compared to the previous quarter's average. This rise in customer visits suggests potentially higher sales.
3. Cross-Verification: The investor might then cross-reference this with social media sentiment analysis related to the chain's recent promotional campaigns or new menu items. If sentiment is largely positive, it reinforces the foot traffic signal.
4. Informed Decision: Based on these real-time insights, the investor might anticipate stronger-than-expected revenue in the upcoming earnings report, potentially adjusting their portfolio construction by increasing their position in the company's stock before the official announcement.

This hypothetical example illustrates how alternative data can provide early signals, allowing investors to make more timely and informed decisions than those relying solely on lagging traditional data.

Practical Applications

Alternative data has numerous practical applications across various facets of finance, enabling professionals to gain a competitive advantage and make more informed decisions.

• Investment Management: Hedge funds and asset managers extensively use alternative data to develop sophisticated investment strategies. This includes anticipating earnings reports, identifying emerging market trends, and spotting unique investment opportunities. ²⁸, ²⁹For example, satellite imagery of parking lots can estimate sales at retail stores, or data on corporate jet movements can signal potential mergers and acquisitions.
  ²⁷* Credit Risk Assessment: Lenders can leverage alternative data, such as transactional behavior, employment history, and even social media activity, to more accurately assess an individual's or company's creditworthiness, particularly for those with limited traditional credit histories.
  ²⁶* Supply Chain Monitoring: Firms can use sensor data, shipping manifests, and even weather patterns to monitor global supply chains, anticipating potential disruptions or assessing the operational efficiency of companies.
  ²⁴, ²⁵* Private Equity Due Diligence: Private equity firms can employ alternative data to conduct more thorough due diligence on target companies, gaining insights into customer engagement, operational performance, and competitive landscapes.
  ²³* Algorithmic Trading: In quantitative analysis and algorithmic trading, alternative data provides fresh inputs for models designed to execute trades at high speeds, reacting to real-time market shifts before they are reflected in traditional financial indicators.
  ²²* ESG Investing: Environmental, Social, and Governance (ESG) data, a growing category of alternative data, helps investors assess a company's sustainability practices and ethical impact, aligning investments with responsible criteria.
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  The market for alternative data is projected to continue its rapid expansion, reflecting its increasing recognition within the Banking, Financial Services, and Insurance (BFSI) industry for enhancing investment strategies and risk management processes. As of 2023, the market was valued at USD 7.20 billion and is projected to grow at a compound annual growth rate (CAGR) of 50.6% through 2030.

²⁰## Limitations and Criticisms

Despite its growing adoption, alternative data comes with notable limitations and criticisms that financial professionals must address.

• Data Quality and Cleaning: Raw alternative data is often unstructured, messy, and incomplete, requiring significant effort in data cleansing and normalization. Ensuring the accuracy, consistency, and reliability of these vast datasets is a major challenge.
  ¹⁹* Bias and Representativeness: Alternative data sets may not always be representative of the broader market or population, potentially leading to skewed insights. For example, relying solely on social media sentiment might overlook the views of a significant segment of consumers who are not active on those platforms. Ethical concerns also arise regarding potential biases in algorithms that use alternative data, which could lead to unfair outcomes, particularly in areas like credit lending.
  ¹⁸* Regulatory and Ethical Risks: The collection and use of alternative data raise significant regulatory compliance and ethical questions, especially concerning data privacy and the potential for misuse of sensitive information. The Securities and Exchange Commission (SEC) has increased its scrutiny of alternative data, issuing risk alerts and bringing enforcement actions to ensure firms have robust policies to mitigate the risk of using Material Nonpublic Information (MNPI). ¹⁶, ¹⁷For instance, the SEC brought an enforcement action against alternative data provider App Annie for misrepresentations regarding its data collection and the underlying data provided to investment firms. F¹⁵inancial firms must conduct rigorous due diligence on alternative data providers to ensure data is legally and ethically sourced.
  ¹³, ¹⁴* Cost and Complexity: Acquiring, processing, and integrating alternative data can be expensive and resource-intensive, requiring specialized technical talent and infrastructure.
  ¹²* Over-reliance and Information Overload: While alternative data provides an advantage, an over-reliance on it without proper integration with traditional analysis can lead to information overload, potentially diverting attention from long-term strategic value.

Alternative Data vs. Traditional Data

The fundamental difference between alternative data and traditional data lies in their origin and nature.

While traditional data provides a stable, regulated foundation for financial analysis, alternative data offers a dynamic, often real-time, complementary layer that can reveal nuances and shifts not yet apparent in official disclosures. ¹¹The confusion between the two often arises because both aim to inform investment decisions, but they do so through different lenses and with varying degrees of immediacy and detail. Many successful firms integrate both types of data to gain deeper insights.

¹⁰## FAQs

What are common examples of alternative data?

Common examples include credit card transaction data, satellite imagery (e.g., counting cars in parking lots), social media sentiment, web traffic data, mobile app usage statistics, geolocation data, and supply chain tracking information.

⁸, ⁹### Why do investors use alternative data?
Investors use alternative data to gain a competitive edge by uncovering unique, real-time insights into companies, industries, and economies that are not available through traditional financial reporting. This helps them anticipate market movements, identify investment opportunities, and enhance their financial modeling.

⁶, ⁷### Is alternative data regulated?
The regulatory landscape for alternative data is evolving. While the data itself may not be directly regulated in the same way as financial filings, its collection and use are subject to various laws concerning data privacy and the prevention of insider trading. Regulators like the SEC have emphasized the importance of proper due diligence on data providers and robust internal compliance policies to avoid the misuse of Material Nonpublic Information (MNPI).

⁴, ⁵### How does alternative data impact small investors?
While historically primarily used by institutional investors, alternative data is becoming increasingly accessible to retail investors through new platforms and tools. This democratization of data allows individual investors to make more informed, data-driven decisions that were once exclusive to larger market participants.

³### What are the main challenges in using alternative data?
Key challenges include ensuring data quality and accuracy, addressing ethical and privacy concerns related to data sourcing, navigating an evolving regulatory compliance environment, and the significant costs and technical expertise required for data acquisition, processing, and analysis.¹, ²