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Turning ideas into quantitative strategies

Zachary R. Sheffer
Zachary R. Sheffer
CEO & Founder of Elsen

As quants and fundamental investors learn to communicate and share ideas more freely, how is technology helping asset management firms to harness this collaboration for their data-driven quantitative strategies?


  1. Sharing quantitative strategies is not an easy task, with the use of complex equations and algorithms making communication difficult.
  2. When an asset or portfolio manager hatches an idea, it can sometimes take weeks for quants to prepare the data, perform the analysis and return the results.
  3. The use of technology, such as QA Point Powered by Elsen, boosts collaboration so the power to identify and execute on ideas expands exponentially.

Portfolio and asset managers have never been known for their openness.

Sharing and collaborating on new strategies or ideas simply hasn’t been part of the job, and for good reason.

There’s been a culture of secrecy in the asset management industry because fundamental managers don’t want to give their best ideas away to anyone.

They’ve spent too much time researching and identifying investment strategies, seeing signals where others haven’t. After all this work, they want and deserve due credit — and the subsequent returns.

Like the “great and powerful Wizard of Oz”, letting anyone behind the curtain to see the levers they’re pulling to make moves that deliver results has just been perceived as too risky.

But this culture, that’s nearly as old as Wall Street itself, is starting to change, driven by the rise of quantitative investing.

QA Point is a cloud-based platform for backtesting systematic investment models. Turning ideas into quantitative strategies
QA Point is a cloud-based platform for backtesting systematic investment models

Sharing quantitative strategies

The data sources that investors are beginning to use to develop ideas and quantitative strategies is essentially infinite. As a result, managers are becoming more willing to share ideas so they can iterate off each other.

New data sources can be developed almost anywhere — from mining social media content to observing activity in satellite photos.

And when these new alternative data sources are leveraged alongside historical investment data from high quality sources, the opportunities for identifying new signals are endless.

This new world of infinite data resources brings with it new problems that need to be addressed. Namely, the fact that sharing quantitative strategies is not an easy task.

In the past, it was simple enough to write down or talk about a strategy because you could do it in plain English.

In a quantitative world, that’s not the case anymore. Complex equations and algorithms are the new language and they’re not as easy to share.

QA Point. Turning ideas into quantitative strategies

Communication challenges

This challenge is especially acute at large firms where quant and fundamental teams rely on each other, but essentially work independently.

I’ve seen this first hand at nearly every large firm I’ve ever spoken to about implementing data-driven strategies. Collaboration between these two groups is especially hard because basic communication practices between them are broken.

In fact, the quant groups at these firms are so new — and not yet well integrated into big, traditional financial institutions — that proper communication with them may have never even existed in the first place.

Watch: Evidence for tilting towards the quality during market downturns

Time-consuming process

When an asset or portfolio manager at these firms hatches an idea they’d like to test, they talk to an analyst, who then brings the request to an engineer or programmer in the quant group.

It often takes weeks for quants to prepare the data, perform the analysis and return the results.

This takes so much time.

One manager I talked to even shared an experience about how he forgot what his original idea was by the time he received the analysis back.

The thought of iterating on ideas brings even more frustrations. If you want to build on or tweak something after observing the original analysis, you’re basically starting back at square one. Get ready to wait.

This is no way to operate in a world where news cycles – and markets – change at a moment’s notice. Forget about using any element of timeliness as an advantage.

Collaboration through technology

In order to embrace the new culture of collaboration that quantitative strategies have sparked, firms need the technology to support it.

There are a few key attributes that any technology capable of supporting better collaboration should possess. These include:

  • One version of the truth. It’s critical for everyone in an organization to be working off the same data. This sounds simple, but with financial data, there are so many ways to cleanse it and prepare it for use. It doesn’t always come down to right and wrong methods, sometimes it’s just user preference. But if groups in the same organization are using different methods, they might as well be talking different languages. This is why a central and standardized data repository is key.
  • Intuitive interface. Creating an interface that works for everyone is tricky because it needs to be sophisticated enough to meet the needs of quants who want flexibility in the ways they control and manipulate data, yet approachable enough for fundamentals to use without frustration. Addressing the needs of both these groups is an essential component in getting them to work together and better understand each other.
  • Cloud based. In order for technology to enable better communication, it has to be easily accessible for everyone. Cloud-based tools are uniquely suited in this respect because they don’t require any software to be downloaded or installed, and they can be accessed anywhere. There’s no need to be within the four walls of an organization or to install and educate users on using virtual private networks (VPNs) or other technology that might pose a roadblock to usage.
Take advantage of advanced quantitative investment strategies and data science. Turning ideas into quantitative strategies
Take advantage of advanced quantitative investment strategies and data science.

Armed with technology that checks all these boxes, asset management firms will be in a better position to embrace the new culture of collaboration.

And once quants and fundamentals are able to communicate and share ideas more freely, the power to identify and execute on new ideas will expand exponentially.

To see how this type of collaboration can be achieved today, take a closer look at QA Point Powered by Elsen, a Refinitiv product that’s already bringing firms from around the world closer together.

Read the case studies: Using AI and Factor Testing to Find Multiple Sources of Alpha

Discover how to introduce cutting edge quantitative research and analytics to fund management with QA Point.