Thanks to the massive increase in available data, we are seeing major advances in cognitive computing in areas such as machine learning, natural language processing, and predictive data analytics. A number of firms in financial services are taking advantage of this to cut costs and to find new, profitable strategies.
While prop trading firms and hedge funds remain the biggest adopters, there are a few robo-advisors utilizing AI methods. But it’s getting harder to identify the fakers from the makers in such an overcrowded market. As competition heats up, firms are cavalierly tossing out buzzwords: risk management, artificial intelligence, tax loss harvesting, re-balancing.
But one robo-advisor is ready to put its money where its mouth is. I’m talking about qplum, the brainchild of Wall Street veterans Mansi Singhal and Gaurav Chakravorty. I recently had the opportunity to sit down with Gaurav and learn a bit more about the robo-advisor that’s making it possible for anybody to invest like the big players.
Tell us a little bit about qplum. What do you guys do?
qplum is an online investment advisory firm. We manage clients’ money using a systematic, quantitative approach.
We offer customized investment solutions for individuals and institutions. Right now, we’re one of the few robo-advisors using AI, more specifically deep learning (a machine learning methodology) to build, monitor, and optimize portfolios for our clients.
Interesting. So, how did the idea for this company arise?
I guess it all started when Mansi and I were still students at UPenn back in 2004. We were both taking a few courses on the application of machine learning on Financial Time Series at Wharton where we were introduced to the works of Benjamin Graham, also known as the father of value investing.
Back in the 1950s, Graham spoke about a future where investing would no longer be a game or competition. He envisioned a time when everybody would have access to a transparent, affordable tool with the ability to process all the data, and retain it in a collective data bank along with everything it had learned so far. Mansi and I could see the writing on the wall, that AI is the tool to which Graham referred.
Though excited with this notion, it would be some time before qplum could launch. For more than a decade before qplum started, we worked at major banks and hedge funds.
There are quite a few Robo-advisors. The space is very crowded. What sets qplum apart from others?
That’s a great question. Two main differences set us apart from the quick, DIY robo-advisory services: our approach to asset management and our focus on risk management.
We implement dynamic portfolios for our clients. What this means is that clients don’t have to decide how much to invest in say, stocks and bonds. Should it be 70/30 today? Or 80/20? The algos make that decision for them. Also, we overlay all of our portfolios with systematic drawdown controls. This means that if the next financial crisis happens, we have a plan in place for our clients; they don’t have to worry about reacting to market swings.
Our onboarding process is very holistic. We’re not trying to provide a passive, set-it and forget-it service. All of our clients are offered unlimited consultations with our Investments Team. We realize that deciding where to invest your hard-earned savings isn’t easy, and our relationship is based on mutual trust. We pride ourselves in building long-term relationships with our clients.
How much does the service cost?
Our service is a completely flat fee. We charge 0.50% of the managed assets. Put simply, it’s $50 per year for every $10,000 you invest. There are no trading fees or hidden costs.
During turbulent times, people tend to panic. What should we do?
When markets begin to drop, emotions take over for most of us. Some might become fearless and think “Prices are down, should I use this as a buying opportunity?” Others might feel a sense of panic and think “Should I cut my losses?”
At qplum, we don’t underestimate uncertainty, and therefore utilize risk management as well as dynamic asset allocation to tackle this. Dynamic asset allocation changes with market conditions, and attempts to find opportunities in changing market regimes.
Volatility targeting ensures that your portfolio hovers around a specific risk level irrespective of market conditions. Systematic risk management makes sure that there is a plan to exit and re-enter if another financial crisis happens.
Doing all of this manually would be practically impossible. This is where AI comes in. Using some of the best data-science methods, we’re able to offer our service in a transparent, efficient manner at a very low cost.
There’s a lot of buzz around AI these days. How can you tell what’s real and what’s hype?
There is no doubt that AI is changing our lives already. AI has huge potential to bring greater efficiency, higher returns, and more transparency for everyone.
While it’s a common concern that machines are taking the place of people, it will be a long time before technology can stand alone without constant monitoring. Behind the scenes, people are building and monitoring everything. And humans should always be available for client support.
There are many ways that AI can help you today, right now. Whether you’re using technology to obtain a better financial assessment, make better allocation decisions, initiate tax loss harvesting, or utilize risk management, it’s important to take advantage of these lower-cost services, otherwise you’ll get left behind.