TOKYO—Toshiba Corporation has developed three innovative systems (two high-speed real-time trading systems and one asset management system) that, using quantum-inspired optimization computers, Toshiba’s simulated bifurcation machines (SBMs) (*1-6), detect ever-untargeted trading opportunities through quickly analyzing the complicated correlations between many stock prices in Japanese stock market, and demonstrated the execution capability and effectiveness of those systems.
Analyzing the complicated relations (or collective structure) between many stocks as a core part of new trading/asset management strategies is often mathematically formulated to a quadratic discrete optimization problem classified to be the computationally-hard problem (*7,8), which is difficult to solve in short times with conventional computers. The first and second high-speed real-time trading systems, which detect the trading opportunities by quickly solving quadratic discrete optimization problems with SBMs and then issue buying/selling orders in the Tokyo Stock Exchange, demonstrated that the orders issued are filled at the best bid and ask prices (intended prices) used for the decision-making. This is the world’s first demonstration that the orders based on the quadratic discrete optimization-based decision-making can be filled at the intended prices in a rapidly changing actual market (*9). The third asset management system, thanks to the high acceleration capability of the SBM, evaluated a diversified-correlation portfolio strategy based on a quadratic discrete optimization for an ever-larger-scale universe (tradable stocks) covering approximately 2,000 Japanese stocks and for an ever-longer period (10 years) and eventually demonstrated that the strategy outperforms the major indices such TOPIX and MSCI Japan Min. Vol.
The SBMs are remarkable in terms of being suitable/applicable to real-time, edge/embedded, and/or mission-critical systems (*4,5) in addition to the high acceleration capability. Those pioneering achievements obtained would lead to developing of various derivational systems executing similar strategies but defined by different return/risk measures. The detection and execution of trading opportunities based on the undiscovered correlations amongst many electronically-tradable financial products will contribute to enhance the efficiently (for realizing fundamental/fair prices) and liquidity of the financial markets as a liquidity transfer process to liquidity-depletion products (*10,11,12). The details of the systems were published as three referred journal papers (*13,14,15) in IEEE Access, an open-access journal of the IEEE, on Sep. 18, Oct. 23 and Dec. 12 in Eastern Standard Time (EST), the United States.
A financial market with high efficiency and high liquidity (an ideal market) is where investors can execute high-volume trading at fair values, at any time without significantly impacting the market prices. Actual prices of individual products (ex. stocks) can be deviated significantly from their fair values (hereinafter "mispricing"), which may force investors to trade at unfavorable prices (Figure 1). The mispricing is caused by some factors such as demand shocks (a sudden unexpected event that dramatically increases or decreases demand for a product) or excessive responses to the news by the media. It is convinced to deduce the fair values and detect the occurrence of mispricing as the deviations of actual prices from the deduced fair values, by analyzing the complicated relationships or collective dynamics among/of the instantaneous prices of many products (*7,8). Those problems to analyze the prices of many correlated products in finance are, when considering minimum transaction lots or other discretenesses of decision variables as realistic constraints, often formulated to a quadratic discrete optimization problem known to be nondeterministic polynomial (NP)-hard in computer science (Figure 1). The computational complexity to solve an NP-hard problem increases exponentially with the problem size (the number of decision variables), making large-scale quadratic discrete optimization challenging.
Toshiba’s simulated bifurcation machines (SBMs), derived from Toshiba’s original quantum computer called quantum bifurcation machines, are quantum-inspired special-purpose digital computers that enable solving quadratic discrete optimization problems at the world highest speed and largest scale (*1,2,3). The SBMs envision the new concept of financial systems featuring quadratic discrete optimization-based decision-making (*4), but those systems need to be validated in the actual market or using the actual historical market data.