Four categories, illustrated.
i.Supply-chain data
Supply-chain relationships are increasingly used in investment management to identify and assess risk. By understanding the dynamics of a supply chain, investors can detect areas of potential disruption and the room for mitigating them — for example, whether a supplier’s stability puts downstream companies in question.
A close reading of the chain can also gauge a company’s business robustness (a diversified customer portfolio, for instance) and identify companies able to exercise market power over competitors.
ii.Consumer transaction data
Transaction data — from aggregated credit-card panels, for example — can reveal patterns in consumer spending that inform investment decisions, and can surface signs of potential fraud, so that decisions do not rest on flawed inputs.
Emerging trends and structural breaks in consumer behaviour also show up in transaction data early, and combining it with other sources can improve the precision of existing predictive models.
iii.Job-listing data
Job listings offer insights into the labour market: at the company level, hints about new business lines, hiring freezes, or the ability to fill open roles.
In aggregate, they help identify industries and regions with many openings — potential growth — and sectors with declining openings, where returns have become less likely. They also show which skills are in high demand, and where chronic gaps point to structural rigidity.
iv.Social sentiment data
Sentiment data is derived from social media, news outlets, discussion forums and blogs. It reads public opinion about a company, an industry, an asset class or the macro environment.
It is a fast-growing resource: which emerging trends are most discussed, how investors react to news and events — and, read in real time, a possible leading indicator of market movements and opportunities.
Frequently asked questions.
What is alternative data?
Alternative data refers to non-traditional, non-financial data that can be used by investors to obtain a competitive advantage in securities trading. This type of data includes consumer online activity, social media posts, satellite imagery, and many other sources.
Why is alternative data important?
It provides investors with insights that can help identify potential trading opportunities not available from traditional financial data sources. This can help with portfolio construction, risk management, and improving predictive analytics.
What are the main types?
The main categories include web data, satellite imagery, consumer behaviour data, location data, social media data, and market data.
How is alternative data used?
In conjunction with traditional data sources to create predictive models for securities trading. This type of data can help identify potential opportunities and patterns not detectable from traditional sources.
What are the ethical considerations?
Businesses must ensure that they follow ethical guidelines to protect consumer privacy: using only data obtained legally, processing it in a secure and private manner, and providing consumers the ability to opt out of sharing their data.
What challenges are associated with it?
Obtaining quality data, the cost associated with obtaining and processing the data, and the continuous process of staying compliant with data privacy regulations.
What is the best way to process it?
A combination of AI, machine learning, and data mining techniques — allowing potential opportunities to be identified quickly and accurately.
Are there regulations around its use?
Yes, there are regulations and guidelines that businesses must follow, including compliance with data protection regulations such as GDPR.
Myths, debunked.
Myth: Alternative data only applies to large institutional investors
Fact: Alternative data can be used by individual investors as well. It can be used to identify small-cap stocks, identify potential investments, and to stay up to date with the market.
Myth: Alternative data is too costly
Fact: While some alternative data sources can be expensive, there are many cost-effective options available. The value gained from insights often outweighs the initial investment, and costs continue to decrease as the market matures.
Myth: Alternative data is difficult to understand
Fact: Alternative data can take some time to understand, but there are many resources available to help you understand and utilise the data effectively.
Myth: Alternative data has no predictive power
Fact: Alternative data can be used to identify potential trends and patterns, thereby giving it significant predictive power when properly analysed and integrated with traditional data sources.
Myth: Alternative data isn’t reliable
Fact: Many alternative data sources are verified and validated, ensuring their accuracy and reliability. Professional data providers implement rigorous quality-control measures.
You don't have to call the fog. You have to read what moves inside it.
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