Long-Short Minimum Risk Parity Optimization Using a Quantum or Digital Annealer
By Gili Rosenberg & Maxwell Rounds
In portfolio optimization, many weight allocation strategies result in long-only positions. We show how it is possible to formulate and solve an optimization problem that assigns a direction (long or short) to each weight allocation, such that the variance is minimized or maximized. This optimization problem can then be solved by many solvers, including D-Wave Systems’ quantum annealer, the Fujitsu Digital Annealer, and others. We present backtested results for three datasets solved using a tabu solver run on a CPU. Our results suggest that by utilizing intelligent shorting, this method is able to reduce the volatility of long-only strategies, leading to shorter maximum drawdowns and higher Sharpe ratios, albeit with a higher turnover. We also discuss possible extensions of this model such that it attempts to achieve market neutrality, sector neutrality, or takes into account a shorting aversion.
Most Recent Papers
A Comparison of Text Sentiment and Market Sentiment: US Treasury 10-Year Note Futures and Changes to Cash in Circulation using Sentiment Analysis and the CME Market Sentiment Meter
By Pazinski Hong & Anish R. Verma
The CME Market Sentiment Meter (MSM) calculates market sentiment states based on a novel mixture distribution, taking input from options and futures settlement data. We compare market sentiment from financial data to text sentiment from sentiment analysis as an indicator for market trends due to external events. Both types of sentiment were explored in a case study of the year 2020 about the US Treasury 10-Year Note futures (TYF). The year brought large fluctuations in the US economy due to the COVID-19 pandemic and other major events…
CME Market Sentiment Meter Historical Market Analyses: Natural Gas 2014 North American Cold Wave
By Aaron He & Anish R. Verma
From late 2013 through early 2014 there were severe cold fronts across North America, during which time natural gas futures (NG) prices spiked, peaking in February of 2014. The Market Sentiment Meter (MSM) indicated Complacent and Balanced states before the cold period. As the cold wave became more severe, the MSM indicated Anxious states, which preceded an upward movement in settlement price…
Trading Algorithm Navigation Using a Mixture Distribution Risk Model
By Andrew Milne, Anish R. Verma, Phil Goddard, & Clemens Adolphs
The CME Market Sentiment Meter (MSM) provides a daily risk–return estimate for eight products traded on CME Group exchanges: corn (C), crude oil (CL), euro/USD FX (EC), S&P 500 index e-minis (ES), gold (GC), natural gas (NG), soybeans (S), and 10-year treasury notes (TYF). The Market Sentiment Meter is computed by 1QBit using end-of-day settlement data published by CME Group. It is available as a subscription product through CME DataMine…
CME Market Sentiment Meter Historical Market Analyses – Gold – 2019 Federal Funds Rate Cuts
By Anish R. Verma, & Andrew Milne
Periods of Anxious market states for COMEX Gold futures (GC1) tended to be either short-lived or long-lived in the eight-year period ending in December 2019.
In 2018, the U.S. saw economic growth and the Federal Reserve hiked rates four times during the year. The year was dominated primarily by Balanced market states, and GC consistently fell once the rate hikes were announced…
Market Reactions to COVID-19
By Anish R. Verma, & Andrew Milne
The COVID-19 pandemic had a notable effect on the eight futures and options products tracked by 1QBit’s CME Market Sentiment Meter. In some markets, such as U.S. equity index futures and U.S. interest rate futures, there were rapid increases in daily futures volumes as prices changed and traders managed their evolving risk…