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Data Engineering is a Game-Changer for Quantamental Investing

Traders Magazine Online News, April 12, 2018

Cody Robertson

Data science has been attracting a lot of attention during the last few years and FOMO is high. Not surprisingly, clients often tell me they are interested in using alternative data to augment their fundamental investment process and are planning to hire a data scientist, but before doing so, there is much more to consider than simply hiring someone to fill that role internally.  

To provide some context, fundamental managers today see how artificial intelligence and machine learning have been producing great returns for quant funds and are interested in layering those techniques into their own investing strategies.  This is known as a “quantamental” approach.  From Point 72 to Blackrock, some of the biggest names in the industry have been adopting quantamental techniques.

Practical applications include credit card transaction data to help predict retail sales, geolocation data to spot trends in foot traffic and even private plane manifest data to see where and when company executives are travelling to predict M&A activity. It seems like the possibilities are limitless and it is easy to see why the level of buzz and hype are so high.  

To optimize the power of data, companies do not necessarily need to hire a full-time staffer. In fact, implementing a software platform that is supported by a team of engineers with specialized skills that can hit the ground running is a cost-effective and impactful alternative to the expense and time-consuming process of bringing someone in-house. When it comes to considering that software platform, buyers should think about these four important considerations:  

Make Sure the Platform Can Handle Complex Data Flows

Data scientists require reliable pipelines of information to perform their analysis properly.  Data engineers have expertise in areas like data warehousing, ETL processing and data governance.  All of these skills are crucial to produce the flows of managed data.  It is also important for a platform to be able to adapt quickly to changes in data sources and formats.  Data sources that are relevant today may not be relevant tomorrow.  For this reason, configuration-driven systems are preferable to those that require custom code development to adapt to changes.

Bigger Means Better

With more data to leverage, data scientists can make more accurate predictions.  Engineers help merge large amounts of disparate data allowing for richer, more thorough analysis.  A great example of this is private company data.  The vast majority of US companies are privately held and as a result, performance data is typically not disclosed and other important pieces of information that are useful for investment purposes are difficult to come by. The good news is that by leveraging the power of data engineering to bring together the bits and pieces of alternative data spread across the various sources enhances analysts’  research to positively impact investment decision-making for portfolio managers. 

Great Strategies are Built on Solid Footing

Data engineers create the foundations that good analysis is based on.  Having solid data infrastructure in place allows data scientists to spend more time using their specialized skills in statistics and machine learning to operate at their highest potential. 

Industry Knowledge is Key   

There are hundreds of software platforms to choose from when implementing a quantamental strategy.  Choosing a platform that is designed specifically for the alternative asset industry can provide a leg up.  Asset management and data engineering are both inherently complex.   Working with a team that understands both areas can help ensure a timely and successfully rollout.

We are in a transformative time in our industry with traditional firms adapting their investment process to harness the potential of alternative sources of data.  Clients are using data engineering to build new information pipelines that scale easily and minimize costs by using cloud technologies.  Together, data engineers and data scientists can disrupt investment research as we used to know it and find entirely new and innovative pipelines of alpha. 


Cody Robertson is Head of Product for LUX Fund Technology and Solutions (LUX FTS).

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