We pick 2 stocks this week with our quantum computing algorithm…how are they doing?
Updated October 20 & October 29 with updated performance charts.
Updated November 25, 2020 with 33 trading-day performance data. Our CQNS portfolio of AX and MCRB returned 13.3% against the benchmark SPY return of 2.9%, and QQQ of 0.7%. This is significantly better performance, and we see graphical evidence of the zig-zag effect of offsetting stock price moves.
On Friday, October 9, markets close, we download ~ 3,300 stock tickers from the NYSE, NASDAQ-Q and NYSE American stock exchanges. We pass 3,171 stocks through data validation and run them through a 2-step analysis to find the best Chicago Quantum Net Score.
We use the new D-Wave Systems Advantage (TM) 1.1 quantum annealing computer with 5,760 quantum qubits in step 2 with our custom classical solvers. These include a simulated bifurcator, simulated annealer, genetic algorithm and ‘fat-tailed’ Monte Carlo solver.
We select 2 stocks out of 3,171 as of Market Close, Friday, October 9, 2020. This gave us the weekend to crunch the numbers.
We selected AX and MCRB, two companies from very different industries. AX is a regional bank with most of its mortgage exposure in Southern California. MCRB is a drug development platform (‘restoring the function of a dysbiotic microbiome’) which placed stock with Nestle in August.
In the first two days, MCRB (grey line) rose significantly over the past 2+ days. It is up around 5%. AX (black line) is down slightly, 0.3%.
This compares to the market indices ^GSPC is the S&P 500, ^RUT is the Russell 2000, and ^IXIC is the Nasdaq Composite, which are up ~ 1%.
The technical charts below the main graph indicate a general lack of commitment this week. The first is the MACD, and the purple line just crosses under the orange line, indicating downward volume pressure. In the chart VOL ROC, you see a decline in volume for the S&P 500. All the more surprising to see a winner selected.
Thursday, the market starts down. Lots of macro-economic and earnings news. We see movement in the 2 stock portfolio in different directions. If a trader seeks offsetting volatility to trade, this would provide it. Not sure how long this will last, but it is an exciting week with these two stocks. These could turbo-charge a larger portfolio with a dose of Alpha.
For a more detailed look, you can see the S&P 500 is down, AX and MCRB are moving in opposite directions at times, and the major spike in MCRB. We run this algorithm each weekend as research. We can run this for clients over the weekend delivered Monday morning before market open, or we can adopt a new algorithm for a private client.
The method is to run all the US Common Stock tickers that pass data validation, select a 134 stock portfolio that is efficient and attractive based on our algorithm (CQNS). We then do a second deeper analysis and take 134 stocks down to (2,10) stocks.
October 20, 2020 update (1 week of data):
We picked two stocks that outperformed the benchmarks in the first week, moved in opposite directions throughout the week, and had enhanced volatility for institutional traders to profit on. Below see 2 charts.
On October 29, we check the performance of those two stocks against both $SPY and $QQQ (S&P 500 and Nasdaq Composite indices).
We check the performance of our two stocks against $SPY, the S&P 500 index ETF and the index we use to calculate BETA, and $QQQ. We start at the next day’s market open. $MCRB and $AX appear as blue lines, while the $SPY is yellow and $QQQ is orange. While the S&P 500 fell 4.9%, and the QQQ fell 3.3%, AX fell 0.2%, and MCRB fell 6.4%. On average, our portfolio would have fallen less than the S&P 500, and about the same as QQQ. Of note is that these stocks consistently moved in opposite directions over the period, and end straddling both indices. MCRB had significant volatility during the period, giving investors a chance to trade that stock.
Updated (11/25): Over the past 33 trading days, AX is up 34.0% and MCRB is down 7.4%, a portfolio return of 13.3%. This compares favorably to a SPY return of 2.9%, and QQQ of 0.7%. We see the behavior we are looking for. Uncorrelated moves, reversals, cross-overs, and one stock rising while the other falls. This demonstrates the behavior our CQNS model is looking for in the past 253 trading days (1 year), which is reduced volatility of the portfolio with enhanced return (from BETA).
Updated (11/25): We are uncertain how long our CQNS analysis will last for a portfolio. In this case, we see the effects are lasting 33 trading days, or 13% of the trading days in the underlying data. This is lasting longer than we expected.
Updated (11/25): We published our 3rd article pre-print to arXiv on November 4, 2020, found here. We discuss how we scaled up our analysis to ‘all’ US common stocks, and introduced two new solvers to our platform. We now use a custom developed simulated bifurcator (based on pressure), and the new D-Wave Advantage 1.1 (Pegasus P16 topology). We describe our various analysis methods used to pick the most efficient portfolios based on our Chicago Quantum Net Score, which compares variance to expected returns over the past year.
This is not investment advice. We do not have investments in these two stocks, nor do we have any business interests or arrangements with any companies listed in this article.
Jeffrey Cohen, Chicago Quantum (SM),
President, US Advanced Computing Infrastructure Inc.
For more information, please visit our website at www.chicagoquantum.com.
We offer this as a service via our website, here.
