Founder Spotlight: Chad Langager, CEO of AlphaLayer  

In a series of interviews, each of the founders in the AltaML Venture Studio will be introduced. First up is Chad Langager, CEO of AlphaLayer, the first startup incubated in the Studio. AlphaLayer, headquartered in Edmonton, AB, specializes in uncovering investment opportunities at scale through a repeatable research process that integrates core technology, data, and artificial intelligence (AI) to develop differentiated investment strategies.

Before establishing AlphaLayer, Langager served as the VP of Product at AltaML and was previously a co-founder of Leadoff Digital. He also led the product team at Investopedia until 2011. With over a decade of firsthand experience navigating the tech industry’s ebbs and flow, Langager offers insights into his journey to becoming a founder and what the future holds for AlphaLayer.

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What’s the Elevator Pitch for AlphaLayer?

The simplest way to understand our business is this: AlphaLayer is an investment research platform that utilizes AI and machine learning (ML) to create innovative strategies and signals for institutional investors. If I were explaining it to my grandmother, it’s essentially about predicting movements in the stock market, which in turn enables us to generate better risk-adjusted returns for our clients. Primarily, we work with institutional investors who seek new sources for trading revenue through innovative strategies, aim to streamline their processes, or require predictive signals for their investment research. This may involve forecasting credit rating changes of publicly traded companies, empowering them to make proactive decisions based on timely information rather than waiting for events to unfold.

What’s the Long-Term Vision for the Company?

When we originally started back in 2019, it was under the partnership model with AltaML and AIMCo, and there was a very specific mandate to explore broadly within that firm. Since then, we’ve narrowed our focus primarily to signal development and strategy development, essentially concentrating on investment research. Looking ahead, our goal remains to be a world-class investment research platform. This means generating the most value when our signals and strategies directly impact real investment dollars. Our long-term vision comes down to the number of decisions we influence each year and the corresponding financial impact they carry. We’re aggressive  in our aspirations, aiming for impacts in the billions or even hundreds of billions of dollars. It’s certainly a long road, but that’s our perspective: the true value lies in pairing our signals with assets deployed within the market.

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What Changes Have You Witnessed in the Tech Industry, and How Have You Adapted? 

There’s been far greater adoption and investment in leveraging AI/ML technologies in the firms that we talk to, and in what’s put out there publicly. The big thing is we’ve really just changed from being this ‘vision’ or this ‘idea’ to do something, to people who are actually taking action. For us, it has really opened up the door to more conversations as people are now ready for this. They’ve embarked on their AI journey. It’s not merely a concept they’re contemplating; it’s very tangible. What they’re seeking is expertise or accelerants to enhance their competitiveness as they progress down this path of AI/ML. You’re not encountering these ‘I’ve never heard of AI/ML, teach me’ conversations. It’s more about, ‘What have you done? And how do you see it impacting our business?’

What’s One Misconception of AI in the Investment Industry? 

One of the biggest misconceptions is AI’s ability to actually find signals within the market and what this technology is trying to do. One thing we’ll discuss with asset managers is demystifying our approach and clarifying what we bring to the table. Among the misconceptions we address is the idea that simply unleashing ML on financial markets solves all problems and leads to infinite dollars. That couldn’t be farther from the truth. Really what it is, is an additive layer to your investment research process so you still have a human-in-the-loop, you still have a portfolio manager involved, you’re still leveraging financial intuition as you build these types of models. But it’s not this one model that just solves the market.

Can You Take Us Through a Current Case Study You’re Working On?

We recently built a dividend yield strategy tailored to North American investors seeking recurring income from public company dividends. By utilizing ML, we focused on key factors such as dividend payouts, business quality, and positive momentum to build a portfolio of worthwhile investments. Moving forward, our challenge is determining how to integrate ML into our strategy effectively.

One issue we’re investigating with dividend-paying companies is the impact of dividend cuts on investors. Such cuts are concerning because investors rely on dividends for income. Additionally, dividend reductions often signal underlying problems within the company, indicating a decline in earnings. To address this, we developed an ML model to predict dividend cuts within the next 90 days. By analyzing various data, including price and fundamental data, our model accurately identified over 50% of companies that eventually cut dividends. This approach enhances our strategies, demonstrating the power of taking a common strategy then boosting it with ML.

What Advice Would You Give to Investment Firms Beginning Their AI Journey Today? 

With anything, it’s just taking that first small step. We can always find an excuse to not get started—maybe your data isn’t perfect or we don’t necessarily have the talent internally to do these types of things—but you don’t really know what you don’t know until you get started. That’s the biggest advice that I would give to any firm that’s looking to go down this journey.

What Advice Would You Give to Aspiring Founders?

Massive challenge is in front of you as you’re going down this path. It’s obvious you have to have an eye on the technology that you’re developing and make sure that it does what it says it can do. Not only do you need to build technology, but you need to validate that the technology is solving a core problem. It will make a lot of your sales a lot easier in the future if you’ve identified the right problem and begin tackling it in an innovative and unique type of way. 

On the more personal side, just be prepared. There’s a lot of ups and downs in everything that you do. It’s one of those things to try and regulate yourself where you never get too high, you never get too low. You’re going to get a lot of rejection as you’re going down this path, but that doesn’t necessarily mean that you’re not going to break through and find an obscene amount of success later. Make sure to find ways to navigate through those ups and downs, have a personal life, do other things, make sure that it’s not all business all the time. As long as you’re aggressively pursuing a problem that people care about, you’re going to find some success.

Learn more about AlphaLayer and the Portfolio of Ventures in the AltaML Venture Studio here. 

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