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Quant roadshows shine light on buy-side data science

Austin Burkett

20 Jul 2017

Quant roadshows shine light on buy-side data science
Thousands of offroaders light up the night at Oldsmobile Hill at the Imperial Sand Dunes Recreation Area during Thanksgiving weekend in Glamis, California. Photographer: Gene Blevins

Entering the world of quants, more than 100 buy-side professionals recently attended one of our roadshows to discover more about how data science is transforming financial markets.

The numbers speak for themselves, with investments held by quant hedge funds more than doubling in the past three years.

Find out more about Thomson Reuters QA point

More firms are now willing to adopt scientific approaches to algorithms so they can parse data and decide what to buy and sell.

Incorporating these quantitative trading methods into fund management satisfies both an increased desire for scalable investment processes and a need to operate efficiently in a cost constrained environment.

Teams that use old-school research methods are working alongside data scientists and quantitative modeling in order to find ways to optimize the risk/return profile of their portfolios.

At our recent “Work Like a Quant” events in New York and Chicago, we aimed to  show buy-side guests the best ways to incorporate quant workflows into the investment process.

These roadshows addressed the fears of many firms that they lack the in-house infrastructure and desire to perform complicated content integration work to enable quant workflow.

Watch video — Introducing Thomson Reuters QA Point

Alpha generation

In his keynote presentation, Paul Rowady, Director of Research at Alphacution Research Conservatory, illustrated the concept of ‘technical leverage’.

He noted that the increasing use of quantitative tools for asset management is becoming a competitive imperative for success in the current market landscape.

Find out more about how QA Point makes it easy to deploy quantitative investment strategies

Tim Gaumer, Director of Fundamental Research, Thomson Reuters, showed the benefits of diversifying through market factors such as value, momentum, quality and others, rather than tilting a portfolio toward just one.

Gaumer also showed which factors worked best for a specific region and how best to combine them into a single, alpha-generating, multi-factor model.

Director of Fundamental Research at Thomson Reuters
Tim Gaumer, Director of Fundamental Research at Thomson Reuters, presents the benefits of multi-factor models

Backtesting

For decades, asset managers tended to be in one of two camps — fundamental or quantitative.

Many traditional asset managers were deterred by the requirements of technical quantitative analysis or “quant” professionals, particularly for the backtesting of new models, the most time-consuming part of the process.

Now the “quantamental” approach is gaining traction, and it’s already redefining how asset managers handle their portfolios.

The sheer quantity of data available to investors today is making it possible to gain greater insight on investment strategies than ever before.

Find out more about Thomson Reuters QA point

Cloud-based platform

In order to stay ahead of the curve, asset managers will need to find ways to harness and use vast quantities of data efficiently.

Zac Sheffer, CEO at Elsen
Zac Sheffer, CEO at Elsen, demonstrates the value of Thomson Reuters QA point powered by Elsen

In his presentation, Zac Sheffer, CEO at Elsen, showcased Thomson Reuters QA Point powered by Elsen.

This is a cloud-based platform that revolutionizes the backtesting of systematic investment models, allowing professional investors to significantly improve productivity.

QA Point summary screen

It simplifies workflows, making it easy to construct, validate, and deploy multi-factor quantitative investment strategies — in a fraction of the time it takes using traditional quant tools.

Read more on quants – Quant investing: rocket science or accessible tool?

QA Point removes the need to be a highly skilled developer or ‘quant’ to take advantage of advanced quantitative investment strategies and data science.

QA point

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