A Q&A With The Co-Founder Of Quantamize

Stephen Mathai-Davis is the co-founder, chief investment officer and managing member of Quantamize, an investing research platform that utilizes AI and multi-factor models to generate investment ideas.

We caught up with him to find out how the platform works, what makes their ideas unique, and how investors should use it.

What is your goal with Quantamize?

Stephen Mathai-Davis: We want to create a large decentralized online community of investors and traders interested in talking about investing and big data. Our long-term goal is to make the Quantamize platform a place where individuals feel like they can come and engage with each other about investing with the trust that there is great data and a powerful analytics system to back it up.

This future is already here with decentralized communities on Reddit using bots to engage users and help disseminate information. We are super excited to bring this reality to the mainstream investing market, albeit with a Quantamize twist. From my perspective, this is a great way to democratize AI where people can leverage different elements of the Quantamize platform in a personalized way.

On a high level, could you briefly explain what you feel Quantamize as a platform offers that sets it apart from other investment resources available to traders?

SMD: I don’t like to negative sell so let me tell you why I am super psyched about what we are doing and where I see us going in the future. Quantamize is using advanced Factor Research and cutting-edge AI investment analytics to create interesting and outperforming investment ideas that stretch from a basic stock buy idea to advanced options trades and ETF portfolios. At the moment, we are the only investment platform employing these techniques on a large scale—we apply our models to over 10,000 stocks and 3,000 ETFs. We even model cryptocurrencies.

Never before has all this data been available to the individual investor in one place. The point is that finally, thanks to our partners like S&P Global and ETF Global, all this data is available in one place. That is the value proposition Quantamize offers—a one-stop shop to do detailed stocks, ETFs, options and cryptos research.

Can you offer your perspective on how you feel those tools fit into the investment process, from idea generation to the execution of a trade?

SMD: Quantamize covers the entire investment decision process until the execution of the trade. Our Global Top Stock Ideas, ETF Trading Signals, Daily Top Buy & Short Ideas, and ETF Smart Beta Ratings are meant to help our users to come up with interesting ideas that they may want to introduce into their portfolios.

Obviously, the core value proposition of Quantamize is the performance of our trades and portfolios. This year our options trades are up 433 percent on average while our Top Buy ideas are up north of 23 percent through the end of April. There are many other examples of great performance, but the key here is to highlight that we help the individual on his or her investment journey from the beginning, through the idea generation process, up until the point when they actually enter the trade into their brokerage account.

Among the main capabilities that appear throughout Quantamize is the integration of multi-factor modeling. Can you explain what makes multi-factor such a powerful tool for average traders and investors?

SMD: Multi-Factor investing is the core of what we do here at Quantamize. Unlike a singular focus on “cheap” stocks which are commonly called value stocks, or those that look like they are “hot” which the industry calls momentum stocks, we focus on a basket of factors that help explain the price movement of stocks and ETFs. Now, why do we do that? Well in certain environments, a specific factor may underperform. However, a multi-factor framework is better equipped to manage through an entire market cycle. In other words, when using multiple factors, you reduce your risk of underperforming.

Quantamize’s Q-factor score is one extension of this multi-factor integration. Elaborate a little on the thought process behind the scoring system and the approach you took in illustrating multi-factor in an easily understandable and actionable way.

SMD: Our Q-Factor system is based on the multi-factor models we just discussed. Rather than get into nerdy quant data points, we thought it would be better to distill the concepts down to a transparent and actionable framework. Remember, our goal is to democratize access to this research and we wouldn’t be doing that if we made everything unnecessarily complicated. Once stocks are ranked and scored, we bucket them into groups where we are trying to best maximize performance.

As an example, the stocks in our Top Buy category, on average outperform those in our Attractive category. Likewise, stocks in our Top Shorts category typically are worse performers than those in our Unattractive category. Our goal with the Q-Factor system is to make trading and understanding stocks simple and intuitive.

Why did you choose to make Quantamize free to access?

SMD: Well, let me pose a question to you. Why should people be forced to pay fees to simply save their money? Are we arguing that it should be that way simply because it has always been that way? I think it is safe to argue that the rise of ETFs and the race to zero fees has torn apart that paradigm.

We decided to make Quantamize free because we are committed to helping individuals invest using better data and analytics. Free access to Quantamize is part of our mission to democratize access to Big Data and AI Quant research, but that is just part of our bigger mission. We are daring to challenge the way people are forced to invest and asking is there a better way?

Quantamize is a content partner of Benzinga