How our algorithm is different
What if you could play the market with cheat codes enabled?
Humans have been on a quest to know the future for eons. Delphia’s algorithm is fascinated by it, too.
And while we don’t know if the future is 100% predictable, we do know that it is 68% predictable.
Why 68%? That’s our best Hit Rate to date – the percentage of bets our algorithm gets right when it sees the potential for surprise in the markets.
We're not sure if perfect foresight is possible in our lifetime, but we do know the only way to find out is with your help.
If you’ve ever wanted to train an algorithm, today is your lucky day.
Every day we're running:
- active predictions
- new predictions
- daily features
- data points
We use ML to predict, not react
Some dance to predict, some dance to react.
In markets, there are Prediction Players and there are Reaction Players.
Prediction Players (often Fundamental investors) study companies intimately and make predictions about their fundamentals in order to place bets on which companies will win or lose. They are limited in the quantity of information they can process and the number of bets they can take. The human brain only has so much capacity.
Reaction Players (known as Systematic investors) care less about seeing the future, and more about reacting to new information before anyone else has the chance. They make money by placing thousands of smaller bets which helps lower their risk across the entire portfolio.
Delphia is the best of both worlds. We take the technology of a reactive investor — terabytes of data, computational power, and machine learning — and use it to make predictions across thousands of companies at once.
THE HORIZON MATTERS MOST
There’s the reality of the economy, and the noise of the market.
In the short run, the market is a voting machine.
Stocks go up. Stocks go down. And lately these moves seem more pronounced than ever. When you’re placing a bet in the short run, you’re at the mercy of the speed and diversity of human thought.
Every time another (big) player’s expectations change, prices do too.
That’s why Delphia places its bets out beyond the noise. Because eventually, markets become a weighing machine.
Delphia succeeds by placing bets in moments where the voting has strayed too far from the weighing – where human expectations have diverged from economic reality.
In Machine Learning, the target you choose decides your fate.
Wall Street’s no stranger to lots of data, computation and AI, but like when humans discovered fire, they’ve found themselves getting burnt by things the machine could never have predicted, like a pandemic.
Delphia doesn’t use AI to predict a stock’s price – that’s a fool’s errand. Instead, we use it to predict fundamentals – things like sales or profit – not just today, but quarters ahead.
Armed with these predictions, our algorithm goes hunting for where the market is primed for surprise.
For illustrative purposes, here’s what the fundamentals might look like for Netflix. Keep in mind, we don’t just make our own predictions – we predict what everyone else is predicting as well.
OUR PEOPLE ARE ON A MISSION
An algorithm is only as good as the people behind it.
Our research team, engineers and senior leaders are exceptional. With 6 PhDs, 43 publications, and hundreds of citations between them, they have all the accolades you’d expect. But they could work at any hedge fund.
Instead, they took a career risk, left Wall Street, and chose Delphia because of the mission.
Revolutionizing the world of investing and levelling the playing field brings an energy and purpose you can’t find anywhere else — and that’s worth the gamble.
PhD, Machine Learning, Carnegie Mellon University
Senior Data Scientist
A physics PhD with a specialization in Climate Dynamics.
PhD Physics, University of Toronto
Head of Quantitative Research
PhD, Finance, UC Irvine Merage School of Business
Chief Technology Officer
Previous startup was acquired by Autodesk. Led a global team at AutoCAD.
Chief Investment Officer
Previously an MD at the Canada Pension Plan Investment Board.
PhD in Mechanical Engineering & Applied Mechanics, University of Pennsylvania