Consistently outperforming an index
Index investing is perceived as a safe strategy. Your wins and losses are aligned with the market, so when things go bad you just do not feel you are the dumber in the room. And they typically win, since index investing proved to be a solid strategy in the past few decades.
Index investing is all about trust. You trust that whoever creates NASDAQ 100 and S&P 500 have correctly selected and tracked the right companies. Solid companies with a proven track record.
But building a portfolio that tracks an index without fully replicating it is an immense challenge. That is precisely what we are able to do. Instead of creating a groundbreaking new strategy, our technology allows asset managers to keep investing in their trusted indices as they would normally do, but obtaining an extra alpha. NASDAQ 100 plus a consistent 2%? That's what our product is all about.
Quantum inspired algorithms
Our team started in quantum computing at PhD level. At a certain point we realized that it was possible to run some of the algorithms we used there on conventional hardware.
We all agree quantum computing will be a leap forward, but at this point it is not widely available to perform complex computations. It is simply not stable enough for that.
Running quantum inspired algorithms on computers nowadays is what motivated us to start a company to make this technology available today to users instead of waiting for true quantum computers to arrive.
Index tracking at a glance
Our algorithms excel at complex combinatorial problems. Where other approaches just bring a simulation, we come with the true exact answer. It is like simulating car dynamics with some multiplications or with a real physics engine.
You can track an index by buying all of its constituents in the exact proportions defined by the index provider. However, this approach is expensive, operationally complex, and difficult to maintain. It also leaves no room for flexibility—no ability to adapt the portfolio to market conditions or to introduce additional constraints, as we'll discuss later.
Tracking an index using a smaller number of constituents is a computationally formidable challenge. To give a sense of scale: if we want to build a basket of 80 stocks that behaves like the S&P 500, using only companies from the index itself, the number of possible combinations exceeds the number of atoms in the observable universe. How can we know which of those combinations truly tracks the index—or whether some of them not only track it, but also deliver higher returns? With such an overwhelming number of possibilities, evaluating them all is simply impossible. This is where our quantum-inspired algorithms represent a dramatic leap forward compared to traditional approaches, allowing us to identify those specific portfolios hidden within this vast combinatorial space.
Beyond the usual index tracking
Index tracking is already a challenging and fascinating problem in its own right. But our technology goes even further. We can define the universe of assets from which the tracking portfolio is built. For instance, when tracking the S&P 500, we might choose to construct an 80-asset portfolio using only a selected subset of 450 preferred stocks. Beyond that, we can impose global constraints on the portfolio—designing portfolios that, for example, maximize dividend yield or ESG scores, while still meeting strict index-tracking requirements.
