Jeffrey Cohen, President, US Advanced Computing Infrastructure Inc. August 20, 2020
Good news. We submitted our article to arXiv yesterday. Should publish Friday.
arXiv:submit/3330970 [cs.DM] 19 Aug 2020
What did we learn?
1. Today's stock pickers can use either the D-Wave quantum annealer (2048 qubits), or classical methods to optimize a 60 stock universe (with 1 year of historical data). Our custom coded Genetic Algorithm found the 'ideal' portfolio in 7 seconds...and all take under a minute of computation time.
2. Our Chicago Quantum Net Score is a reformulation of the traditional Sharpe ratio and is used to pick efficient equity portfolios. It seems to work well. It quickly picks portfolios on the Efficient Frontier (return / risk). It runs well on the D-Wave quantum annealer and in classical solvers too.
An efficient frontier is the top of a set of portfolios (highest return for each level of risk). The upper points are either yellow, red or blue.
The yellow dots are the efficient frontier with our Monte Carlo analysis (fat-tailed, & discrete distribution around N/2). We ran over 200K samples, and picked from every asset size. It picked the 'ideal' portfolio.
The red dots are picked by the quantum computer.
The blue dots are picked by our genetic algorithm (random seed).
3. We can successfully analyze 60 stocks at a time. What might stop us from running 100 at once?
We find two challenges:
a) We are running out of room on the quantum computer (we use 1,700 qubits out of 2,048).
b) Our variance terms get very small when we choose larger portfolios (e.g., 55 stocks out of 60).
The good news is that D-Wave has a new quantum annealer with over 5,000 qubits (more room to grow), and we continue to research and learn new methods for larger portfolios.
We will let you know when our article is available on arXiv in a comment to this post.
Strategic IT Management Consultant with a strong interest in Quantum Computing. Consulting for 29 years and this looks as interesting as cloud computing was in 2010.