AGFiQ: Disciplined Intellect

Data is everywhere.

But as volume explodes exponentially, we’ve become inundated. We know more than ever before, but without understanding, data is meaningless. The true potential of data lies in how it’s deciphered and how it’s applied. It needs to be understood, but it takes intelligence to interpret intelligence. We believe in bringing together a group of minds – our unartificial intelligence – to uncover the real insights behind the numbers. A human touch, one that demands discipline. Our process is predicated on the belief that to think differently, we must be built differently. A model designed to be multidisciplinary, transparent and creative. A disciplined approach guided by three principles – shared intelligence, measured approach, and active accountability. We champion shared intelligence. That’s why we employ a diverse group of specialists with Ph.D.’s in everything from Finance and Economics to Astrophysics. We understand process and take a measured approach. That’s why we created a unique systematic approach to factor research for our quantitative solutions. We believe in active accountability. That’s why we developed our own state of the art proprietary global database. In an ever-evolving and increasingly complex market environment, discipline matters more now than ever before. Through AGFiQ, our unartificial intelligence delivers stability for your investments, whatever tomorrow may bring.


Big data: How to get quality from quantity


The ins and outs of utilizing data to improve investment outcomes of both quantitative and fundamental managers in the digital age


For many in the financial community, big data has become synonymous with quantitative investing and quantitative investing alone. Who, after all, is better equipped to handle the growing reams of information now available at the push of a button or swipe of a screen than those who base their investment decisions on facts and figures, selected and sorted by powerful computer models?

But as the influence of big data on “quants” has continued to grow, so too has its pull on fundamental managers seeking new ways of assessing opportunities and mitigating risk. 

As a result, the silos that once separated and created friction between these two styles of investment management are now being torn down to create a shared intelligence, utilizing the proliferation of data more efficiently in all of its various forms.

Members of AGF’s quantitative and fundamental investment teams sat down recently to discuss these collaborative efforts and also how they use and share data to improve their own unique investment processes.


Why is data becoming more important to investment management?

Stephen Way: Data simplifies our investable universe and has helped us create a more disciplined, systematic process over the years. The key is how you analyse the data and the judgement you apply to it.

Mark Stacey: What’s most important is the story within the data that tells us how companies are doing. To make thoughtful decisions, you need to be able to harness this information.

Stephen Duench: I think you could make a strong case that there is nothing more important in investment analysis than data. In the last few decades the breadth of data in investment management has gone parabolic and only in the last few years have we reasonably started to understand its importance to a more robust investment process.

mark stacey quote

How has data changed your job?

Rune Sollihaug: Data has become more granular, and there is now more focus on shorter term data. In the past, there was very little focus on factor exposures, for instance. Systems have become more sophisticated as well, resulting in the benefit of more detailed reports.

MS: More and better data now provides so many different perspectives on the market beyond traditional financial statement analysis.

SW: We’ve been using data related to factors to inform our country allocation since 1995, but back then we relied solely on a third party provider. Now we get a lot of it from Mark’s quantitative team and we have more ways to slice it to come up with new approaches.

SD: Having built my entire career on the quantitative side of investment management, I’ve always had a strong respect for the power of data, and also the importance of having quality data. The better it is the more trust there is in our analysis.


Does utilizing data represent any specific challenges for asset managers?

RS: Both accuracy and getting timely data are crucial. Without a proper quality assurance process in place there is a risk that the data quality could end up being poor.

MS: Whether it’s financial statements or an earnings call transcript, it’s crucial to get the right data source. You also have to be able to understand the data, “scrub” it, organize it and maintain it. It’s easy to become overloaded with too much data if you don’t have a proper process for handling it in place.

SW: Figuring out what is not worth knowing is just as critical. Otherwise, you can spend months crunching data in a so-called analysis paralysis.

SD: For those managers utilizing third-party sources of data, the biggest challenge is trust, and I’m unsure one can fully trust a source they don’t initially create. As the industry becomes more and more quantitatively inclined, sourcing and building the data internally is the only way to achieve legitimate trust with the data.

What data sources do you use/trust?

RS: There are no data sources that I trust 100%. Those I trust most have a solid quality assurance process in place in order to scrub and validate the data.

SD: For our proprietary investment process and analysis, I only trust data that has been vetted, researched, and implemented internally. But even then, quality is not guaranteed if you don’t have processes in place to monitor efficacy.

MS: Trust can become a bit of an issue when you get into Artificial Intelligence (AI) and unstructured data that is being scrapped from the Internet. As data moves away from just financial statement analysis, it’s important to note whether the information is audited or self-reported as it tends to be like a lot of environmental, social and governance (ESG) factor data.

SW: Because ESG scores are put together by third parties from a culmination of data, you never know if they are misinterpreting the information unless you do the fundamental analysis and identify the key issues yourself. So, it’s important to triangulate and validate through multiple sources.


Is harnessing proprietary data important?

MS: It’s more the way you use the data that’s important. For the quant team, that means making sure all the inputs for the models we build are proprietary as well as the models themselves. That way, we understand them and are not relying on someone else if something goes wrong.

RS: If you have data or models that give you a competitive advantage, it would certainly be beneficial.


How does a quant use data differently than a fundamental manager? How can they learn from each other?

SW: From a fundamental perspective, we use the quantitative data to help us narrow the universe of stocks down from around 600 names to something more manageable. We can also use data to help capture “red flags” in our investment thesis, allowing us to prioritize our attention to potential risks that fundamental analysis, by itself, might have been slow to recognize.

MS: As quants, we’re analyzing many of the same things that Steve does, like discounted cash flow, for example. But we do it differently. Our heavy lifting is up front when we are creating our models based on the data. Steve’s heavy lifting is after the data has helped him narrow down his universe.

RS: I believe both should be used in both processes, the question is to what degree.


How does data help from a risk management perspective?

RS: It’s become crucial for someone like me who is helping manage risk across a number of strategies. But it has to be accurate. Even small variances in input data can cause large variances in any report.

SW: Data is becoming a key enabler of risk management. It provides key insights in terms of active risk contribution, correlations with the rest of the portfolio and scenario analysis.

MS: Data is helping us learn far more about the contributors to risk in a portfolio. Because of that, we are able to identify and get the exposures we want, but also control the exposures we don’t want.

SW: I’m not sure terms like active risk and tracking error were even in my parlance 10 years ago. The bar has been raised and it’s now expected you’ll use this data to help understand, manage and communicate the various risk exposures in your portfolio.

SD: I believe they are under the same umbrella. However, risk management is often many fundamental manager’s first foray into data and quantitative analysis. As the industry uses and trusts risk management processes more, the same demand and trust may be applied towards other data analytical methods.

Mark Stacey

Mark Stacey, MBA, CBA

Senior Vice-President & Head of Portfolio Management and Co-Chief Investment Officer1
Industry Exp: since 2002
Firm Exp: since 2011

Stephen Way

Stephen Way, CFA

Senior Vice-President and Portfolio Manager
Industry Exp: since 1987
Firm Exp: since 1987

Stephen Duench

Stephen Duench, CFA

Vice-President and Portfolio Manager
Industry Exp: since 2007
Firm Exp: since 2007

Rune Sollihaug

Rune Sollihaug, CFA, CIPM

Vice-President, Risk & Portfolio Analytics
Industry Exp: since 2000
Firm Exp: since 2011

Commentaries contained herein are provided as a general source of information based on information available as of May 1, 2019 and should not be considered as personal investment advice or an offer or solicitation to buy and/or sell securities. Every effort has been made to ensure accuracy in these commentaries at the time of publication; however, accuracy cannot be guaranteed. Market conditions may change and the manager accepts no responsibility for individual investment decisions arising from the use of or reliance on the information contained herein. Investors are expected to obtain professional investment advice.
AGFiQ is a collaboration of investment professionals from Highstreet Asset Management Inc. (a Canadian registered portfolio manager) and AGF Investments LLC (formerly FFCM, LLC). This collaboration makes-up the quantitative investment team.
AGF Investments is a group of wholly owned subsidiaries of AGF Management Limited, a Canadian reporting issuer. The subsidiaries included in AGF Investments are AGF Investments Inc. (AGFI), Highstreet Asset Management Inc. (Highstreet), AGF Investments LLC (formerly FFCM, LLC), AGF Investments America Inc. (AGFA), AGF Asset Management (Asia) Limited (AGF AM Asia) and AGF International Advisors Company Limited (AGFIA). AGFA is a registered advisor in the U.S. AGFI and Highstreet are registered as portfolio managers across Canadian securities commissions. AGFIA is regulated by the Central Bank of Ireland and registered with the Australian Securities & Investments
Publication date: May 13, 2018

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