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How does alternative data provide an equity investing edge?
Laurent Joué
Head of Systematic Alternatives and Lead Portfolio Manager
Marc Pellaud, PhD
Lead Portfolio Manager
key takeaways.
Our research into the performance of hundreds of companies shows that earnings surprises and equity returns are closely linked, both on the upside and downside1
Systematically analysing relevant real-time information on consumer behaviour provides an information edge, which can be used to forecast KPIs and predict such idiosyncratic events
Two recent real-world examples demonstrate the benefits of leveraging alternative data and systematic techniques in a ‘quantamental approach’ to identify opportunities and alpha potential.
Why use alternative data in equity investing?
Non-traditional company data, such as information relating to website traffic, retail footfall, credit-card transactions, mobile app usage and satellite imagery differs in key ways from traditional financial data. Crucially, such data is highly granular and available in real time (or near real time), capturing early, company-specific signals that are not always reflected in market prices. What’s more, while conventional data is widely available and used by most market participants, the cost and complexity of harnessing alternative data limits its use to investors with the financial and technical resources to effectively exploit it.
We recently explored these themes in a webinar. Click here to watch the replay
How can raw consumer data be turned into investment signals?
The first step is to clean and map the data onto company-specific KPIs. These are typically focused on metrics such as revenue, customer growth and profitability, which are identified as being most likely to influence the company’s share price. Forecasts for individual KPIs obtained in this way are then used to build a comprehensive picture of the company’s earnings momentum and outlook.
By comparing alternative data-driven KPI forecasts to the market consensus, it is possible to identify meaningful deviations and predict earnings surprises for companies with an accuracy level of 65-70%2. A long/short investment approach to such idiosyncratic events can take advantage of this, with average outperformance of around 2% on positive surprises and 4%1 on negative surprises.
In the case studies below, we illustrate the benefits of integrating alternative data into an equity research process.
Making a splash: retailer impact of a major consumer product launch
When the Nintendo3 Switch 2 launched in June 2025, alternative data accurately captured the consumer response in real time.
As the first major videogame console launched since Sony’s3 PS5 in 2020, the second generation of Nintendo’s handheld device was heavily promoted by major US retailers. Best Buy3 opened its store at 11pm on the eve of release to capitalise on excitement, while Target3 turned the launch into a multi-day event with giveaways, exclusive merchandise and themed displays.
Alternative data clearly evidenced a surge in consumer engagement around the launch. Foot traffic at Target stores on the 6 June was up 5% on the same date the previous year, while web traffic for both 6 and 7 June were over 40% higher than in 20244. Meanwhile, Best Buy’s Google3 search results spiked on launch day – and its year-on-year transaction sales more than doubled.
This high level of activity was reflected in first-week sales for the Switch 2 of over 1.1 million units, setting an all-time US launch-week sales record for videogame hardware. However, while the launch gave a short-term boost to the gaming segment, mapping the impact against key KPIs showed it failed to offset ongoing softness in both Target and Best Buy’s business activity. As a result, we maintained a neutral stance on both stocks throughout the period.
related content.
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media releasesalternativesData Edge - market neutralCorporate
Dough or no dough? Contrasting fortunes for hospitality names
Turning to the hospitality sector, the recent performance of two businesses which at first glance appear similar provides a strong example of how alternative data can identify and exploit specific deviations from general market expectations for a given sector.
Brinker International3 is a US multinational best known for its Chili’s chain of casual dining restaurants. Our company-specific KPIs for the firm concentrate on system-wide sales, focusing on gross profit, same-store sales growth and total revenue. Krispy Kreme3, meanwhile, needs little introduction, having built up a global presence as purveyors of doughnuts. Our alternative data modelling for the company focuses on its home market, considering total revenue and gross profit for the US side of the business.
By analysing alternative data, we were able to detect a clear, consistent shift in consumer behaviour for these two businesses across multiple data sets. Each company was deviating from the norm for the hospitality sector, but in different directions. From 31 October 2024 to 17 March, foot traffic for Brinker was up 18%, while Apple and Android app usage had increased by 19% and 17% respectively. In contrast, Krispy Kreme’s foot traffic showed 8.3% less activity, while Apple and Android app usage was down by 12% and 17% respectively.
Most importantly, the market consensus had yet to fully identify these shifts, underestimating Brinker’s accelerating growth while being slow to realise the slowdown for Krispy Kreme. We therefore decided to invest long on Brinker and short on Krispy Kreme, anticipating contrasting earnings surprises for each.
For Brinker, we remained long after the earnings results until the end of Q2, when we began reducing exposure towards a residual position at the end of July. For Krispy Kreme, in contrast, we closed the position shortly after the firm’s earnings call, since the subsequent market reaction fully priced in the decline in customer demand.
Data for the edge
By systematically employing this kind of carefully selected and interpreted alternative data for a diversified, market-neutral portfolio of global equities, we believe it is possible to generate significant long-term outperformance with low correlation to traditional asset classes. This is at the core of the investment philosophy for LOIM’s DataEdge Market Neutral equity strategy.
To learn more about our DataEdge strategy, please click here.
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[1] Please contact your sales representative to read the full paper and research findings.
[2] Source: LOIM. Past performance is not a guarantee of future returns.
[3] Any reference to a specific company or security does not constitute a recommendation to buy, sell, hold or directly invest in the company or securities. It should not be assumed that the recommendations made in the future will be profitable or will equal the performance of the securities discussed in this document.
[4] Source: LOIM alternative data indicators.
important information.
For professional investors use only
This document is a Corporate Communication for Professional Investors only and is not a marketing communication related to a fund, an investment product or investment services in your country. This document is not intended to provide investment, tax, accounting, professional or legal advice.
