#AI #altdata #bigdata #machinelearning #quantamental
These terms have received a lot of attention from the financial press, and it seems every investing conference these days has a panel discussion about how great alternative data is. But should we care?
Many of alt data’s early adopters – and ExtractAlpha’s first clients – were quant hedge funds. This crowd learned the hard way that if they all traded on the same information, they not only were at a competitive disadvantage; they may even face an existential risk.
In early August 2007, massive drawdowns rippled through many of the factors then used by equity quants, forcing redemptions, fund closures, and a lot of soul-searching. We discussed this Quant Quake in some detail on its ten-year anniversary and provided some takeaways from it.
One key takeaway, which applies equally well to discretionary managers as to quants, is that sources of investing edge should be diversified, and in particular diversified away from what one’s competitors are doing. We need to diversify to provide an investing edge, but also to avoid being exposed to the same factors as our competitors, since their losses could then cascade through to our losses, as happened in 2007. And of course we need to diversify to tell a better story to our own investors as to why we are different and more innovative than other investment managers.
Diversification can be methodological (how to act on information) but can also be materials-based (what information to use). And alternative datasets are a great way to get at new perspectives on a company’s prospects.
But embracing alternative data is tricky in a few ways, which a some recent surveys have noted, and which we observe from our discussions with clients:
- There are too many alternative data providers out there – hundreds! – and it’s hard to know which ones to pay attention to
- The datasets are often large and complex
- Many managers don’t have the infrastructure to efficiently ingest and evaluate new datasets – especially fundamental managers
- The datasets can be expensive
- Turning the datasets into actionable intelligence is not always easy
And most importantly, after all that,
- The datasets often don’t deliver any value
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Insight by ExtractAlpha is designed to address these issues for fundamental portfolio managers, analysts, and traders. We’ve looked through dozens of datasets since our founding in 2013 and identified a curated, manageable set which have displayed proven investment value. We’ve then applied our proprietary technology to distill these large, complex datasets – as diverse as options implied volatilities, patent filings, government contracts, and consumer complaints – into easy-to-use tools, such as portfolio-level and stock-level scoring systems and ESG rankings, all within a single platform, ExtractAlpha Insight. The tools in Insight are designed to align with institutional investors’ workflows, such as custom watchlists and trade-timing signals. The scores are backed by research by our in-house quant research team as well as published academic studies, and are used by some of the top quant firms worldwide.
Insight makes valuable, unique data immediately actionable – without the buzzwords.
Currently, Insight covers 3000 US stocks and we continue to actively refine the platform and expand coverage. The Insights platform can be accessed in evaluation mode here.
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Founded in 2013, ExtractAlpha is a premiere provider of alternative data solutions. The company sorts through huge amounts of alternative data sets with proven value in forecasting fundamentals, returns and risk, and delivers that data in clean, intuitive ways. The rigorously researched data sets are designed for institutional investors to gain a measurable edge over their competitors. ExtractAlpha partners with some of the world’s leading FinTech data firms to identify investment value in big data sets, and therefore, help investors profit from unique sources of information.
