Technology Tailwind Built for Fundamental Investors

The Challenge:

 

“I have come to believe that the hedge fund industry requires far greater scale than in the past, and that excellent fundamental investing requires quantamental sophistication and analytics,” – Samantha Greenberg on her decision to close Margate and join Citadel.​ 

 

The sophistication Samantha is talking about is straightforward – roughly 85% of global stored data is unstructured and growing at 30% per year (faster than any other investment input), while developed market assets with systematic strategies (fundamental and quant) account for nearly 80% of total volumes. As information efficiency climbs, discretionary managers require a more repeatable, and scalable framework to succeed and leverage their strengths in predicting stock returns.

 

Current Solutions Strained: but Technology Tailwind Suggests Opportunity

 

When Excel revolutionized graphing paper and T-accounts, it brought dramatic efficiency gains to arithmetic calculations. However, as significantly more data has become accessible, actionable insight is increasingly available – but it is buried in millions of rows and flowing by in a real time river – well outside the intended parameters of Excel. As a result managers like Samantha Greenberg are increasingly seeking out larger institutions, to digest the torrent.

 

Thankfully technology has a history of leveling the playing field, as vendors such as Salesforce.com, ServiceNow, and public cloud vendors have demonstrated the ability to democratize the power of scale for organizations of all sizes. The resulting reduction in startup and maintenance cost revolutionized the innovation world and allowed new companies to challenge the power of incumbents - and we believe this opportunity exists in active management as well.

 

Truly great technology platforms are built with common component elements enabling each customer to configure their "secret sauce" in scalable, consistent, disciplined processes. As opposed to internal purpose-built bridges to nowhere - costs and complexity is minimized, freeing users to focus on creativity and excellence.

 

Doing it Your Way: Technology Configured For You

 

We believe Equity Data Science (EDS) represents the similar scale and configurability of great technology platforms – but specifically with active management use cases in mind. The processes are far more mature than its recent formal introduction, as the culmination of over 5 years inside of multiple funds building a library of inputs, under the guidance of performance proven professionals.

 

The team at EDS spent 5 years, inside of two funds, and under the guidance of two leading CIOs: a former CIO at Morgan Stanley and Partner at Maverick Capital, the second from another large Tiger Cub, developing the active management platform before beginning to ramp the effort externally in 2017. EDS is the only scalable platform purpose-built for fundamental investors, covering the entire Investment Life-Cycle (Pre & Post Trade), solving the pain of aggregating and optimizing your unique intelligence. The software solution is a decision weighting tool, able to reflect your process in fundamentals, conviction, valuation, real time data inputs, and risk factor analysis in precise proactive stack ranks. This can be visualized for Idea Generation, Portfolio Construction and Sizing, and Risk Management. This disruptive Platform-as-a-Service (PAAS) is delivered at a fraction of cost of current fragmented solutions, includes leading data-sets from FactSet and MSCI Factor Data, and is secure, robust, delivered quickly and configured to client’s exact needs. Further, it brings the entire team together around one straightforward, objective, and consistent process: fundamental analysts, data scientists, risk officers, portfolio managers, and client product managers.

 

Equity Data Science (EDS) is revolutionizing proactive, thoughtful investment managers looking to leverage technology as a scalable tailwind in their proprietary process, leveling the playing field relative to large incumbent vendors. 

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