investment viewpoints

How can big data be used to benefit investors?

How can big data be used to benefit investors?
Laurent Joué - Head of Systematic Alternatives and Lead Portfolio Manager

Laurent Joué

Head of Systematic Alternatives and Lead Portfolio Manager
Marc Pellaud, PhD - Lead Portfolio Manager

Marc Pellaud, PhD

Lead Portfolio Manager

The growth of big data  is a powerful development that could reshape investment approaches. Alternative data – from credit-card transactions, geolocation and insurance coverage and other sources – reflects real world activity, unlike traditional market data. Equity investors can use it to gain insights and differentiated perspectives, opening up exciting new ways to generate returns.  

That said, the ecosystem is growing ever more complex. Translating the almost boundless amount of information into actionable investment insights requires specific skillsets, creating significant barriers to entry. LOIM’s DataEdge strategy employs data science to gain an advantage, helping our investment team predict earnings revisions and build a more accurate information edge. 


Need to know:

  • Our DataEdge strategy combines alternative data and data science insights to forecast earnings revisions and business inflection points
  • Advances in data science help us seek an information edge by structuring the data, and identifying consumer behavior and business trend inflections 
  • We expect to expand our investment universe into new sectors and regions over time, as companies in more industries invest in data and logistics


A world transformed 

Most people could not have predicted how quickly world culture would be transformed by internet connectivity. For evidence of the impact, one need only compare photos of the last two papal inaugurations, just eight years apart (see Vatican Square: 2005 and 2013 (   The images show the stark difference in the crowds: a smattering of handheld devices in 2005 and a then a sea of them less than a decade later. 

Smartphones collect data on an extremely granular level. To many people, all of the streaming, liking, posting and purchasing probably seems like random information. However, when the digital footprint of a single individual is anonymised and amalgamated with those of millions of other people, we can draw connections and identify patterns. 

That, in essence, is big data. It is the incomprehensibly  vast amount of information that individuals, businesses, governments, machines and connected devices create every minute – and the attempts by data scientists to make sense of it all, using new technologies and statistical approaches. 

Through the DataEdge strategy, we source, test and clean the data so our investment teams can identify consumer trends that will impact companies. This can help improve our forecasting of company results and our stock picking. 

Big data is…

What is big data? To find out, click each tab below.

  • The investment community has been talking more and more about big data amid growing recognition of what it can reveal about business trends. Finding a differentiated edge in fundamental analysis has become more difficult using traditional sources of information – i.e., sell-side research, investor conferences, management team interviews and regulatory filings.  

    Data can tell help us learn about consumer behavior in real time, understand competitive issues and identify intra-quarter trends to help predict revenues.

    Is it any surprise that we hear more hedge funds talking about purchasing big data or speaking about a more ‘quantamental’  approach ? The data track earnings and reflect fundamental trends, but the skills needed to process the information are a subset of data science techniques and therefore inherently quantitative. 

  • The data ecosystem is growing exponentially in sheer volume, infrastructure and complexity. 

    At the moment, the consumer space offers the largest breadth and available history of alternative data – providing insights about key revenue trends within retail businesses, grocers, restaurants, hotels, leisure and entertainment providers.

    The next largest segment is technology, media and telecommunications – particularly internet, software and hardware companies. The industrials space has been slower to adopt data analytics outside of the airline and auto segments, although initiatives in manufacturing, shipping, and supply-chain management are increasing. 

    We expect to expand our investment universe into new sectors and regions over time, as companies in areas like healthcare continue to invest heavily in data and logistics.

    FIG 1.    Sample of the volumes of data created every minute


    Image from Domo, “Data never sleeps 10.0”, April 2022. Data from: Global Media Insight, Oberio, Hootsuite, Earthweb, Matthew, Web Tribunal,, Local IQ, Business of Apps, Query Sprout, Young and the Invested, Dating Zest, IBIS World, DoorDash, TechCrunch, Statistia, Data Never Sleeps 1.0.


    FIG 2.    Sources of alternative data

    source: LOIM. For illustrative purposes only.

    Figure 2 shows the broad categories of alternative data that we tend to focus on. While there are numerous other alternative data sources, such as social media, we generally find them to be less reliable or relevant for our purposes.

  • More data does not necessarily mean better knowledge. The reality is that much  of the available data can be worthless, prone to giving users false or misleading signals. The challenge of extracting meaningful investment insights from all the noise is an arduous task – akin to consistently finding needles in haystacks. 

    One vital development has been the emergence in recent years of software and applications that sort, summarise and present terabytes of data in a concise format. Advancements in data analysis and affordable access to external vendors are making it easier to extract value. 

    At LOIM, we partner with a pioneering data aggregator to structure and filter the data to make it useable for our systematic and quantitative investment teams. They then use the data to predict trends and improve their earnings forecasts for listed companies.

    FIG 3.    The four data quadrants of any investment framework 
    Our blend of data sources lead to differentiated conclusions

    Source: LOIM, as at October 2023. For illustrative purposes only.

    With alternative data, new information that can offer insights about business trends is available continuously, in real time. This makes it possible to assess companies during shorter and more frequent cycles from quarter to quarter. If modelled well, recent data trends can be pieced together and extrapolated to predict future inflections. Traditional fundamental analysis, by contrast, relies largely on longer-term company forecasts and guesswork. 

    Knowing the limitations of alternative data is crucial to building a robust portfolio. The limitation occurs when using an earnings-centric approach that focuses mainly on top-line performance indicators (i.e., sales, revenues). At times, investors focus less on these indicators and more on forward guidance that can be influenced by factors such as macro and geopolitical developments. We therefore rely on additional data sets, mostly fundamental, in order to cross-validate signals and adapt the portfolio accordingly.

  • Experience in evaluating datasets, and an understanding of their biases and limitations are imperative. There will always be a need for a human to provide guidance as a co-pilot. Technical proficiency must be balanced with good judgement. In fact, the degree of expertise and commitment required for success will limit this field to fewer participants than investors might realise.  

    The biggest hurdle that legacy investment managers may face when trying to extract alpha from big data is their culture. Humans are slow to evolve by nature and often reluctant to change ways of thinking that served them well for decades. 

    While there will certainly be increased adoption of big data within the investment community, and our information edge in some of the more mainstream data sources may narrow somewhat, investment habits change slowly. Adoption will not occur overnight.

    Furthermore, acquiring datasets and the necessary talent is costly. Funds using alternative data will certainly have higher thresholds for assets under management to break even relative to traditional equity long/short strategies. That said, if done effectively, this number can be much lower than investors anticipate. 

    Growth of the ecosystem continues to tilt the scales, and we argue that the investment edge in big data is linked more to an investment manager’s expertise and creative application of the information, rather than the size of their data science budget.

Expanding opportunistically  

We expect that continuing innovation will bring access to more alternative data that will allow us to improve our forecasting accuracy. As data adoption spreads to new sectors and regions, we will also continue to expand our investment universe and identify new opportunities.

A head start in this space is critical, and we believe the key to maintaining a lead is constant refinement of the analytical systems  and a focus on scalability. We are excited to see what this continued growth will mean for our space. 

To learn more about our DataEdge strategy, please click here.

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