Realizing financial markets in which all investors can trade at fair values requires improvements in market efficiency*2 and liquidity*?. High frequency traders, who continue to become more important as trading entities in financial markets*4, are characterized by repeating buying and selling of financial products at high-speed and high-frequency using trading algorithms that enable them to make high-speed decisions. It is believed that such HFT activities contribute to improving market efficiency and liquidity*5.
For example, in the stock market, such factors as excessive responses to the news by the media can cause prices of individual stocks to deviate significantly from their fair values (hereinafter 'mispricing'), which may cause investors to trade at unfavorable prices. High frequency traders have the ability to quickly eliminate these mispricings and improve market efficiency and liquidity by executing transactions that match overvalued/undervalued financial products even when markets are in such volatile situations*5.
Conventional HFTs tend to focus on self-evident arbitrage*6 opportunities where high speed is the main source of competitiveness. An integration with mathematical models that require high-precision wide-area search*7 using sophisticated evaluation functions to detect mispricings has not necessarily progressed over the years. On the other hand, in recent years, there has been rapid progress in the development of quasi-quantum computers that can solve large-scale combinatorial optimization problems at high speeds and low latency*8,9, which was previously difficult.
By combining this quasi-quantum technology with conventional HFT technology, it is now possible to search a wider area, which includes statistical arbitration opportunities that have not been targeted before, with a sufficient level of low latency against market price fluctuations. Establishing trading systems that can quickly detect ever-untargeted mispricings and eliminate them is expected to further improve market efficiency and liquidity. The arrival of quasi-quantum technology envisions the new concept of algorithmic trading, but due to the nature of finance and especially of HFT, it needs to be validated in the actual market first.
In this joint experiment,
In this joint experiment, Toshiba will provide
As explained earlier, Simulated Bifurcation Machine will be utilized to detect mispricings (Figure 3a) in a wide area. As shown in Figure. 3b, it detects mispricings that can only be reached by solving combinatorial optimization problems, for example, by solving the shortest path search problem in the whole investment universe.
As its applications in the stock market, it has been planned to conduct statistical arbitrage strategies and optimal portfolio constructions based on the fast detection of ever-untargeted mispricings by solving combinatorial optimization problems. These strategies exploiting such quasi-quantum computers are considered to conceptually differ from existing HFT strategies and are expected to find new investment opportunities that existing HFT systems are unable to target.
Combining Dharma Capital's HFT technologies with Toshiba's Simulated Bifurcation Machine will find undiscovered opportunities by exploring all possibilities while maintaining sufficient speed. The parties will examine the effectiveness of the investment strategies exploiting the quasi-quantum computer in the financial market, and verify its contributions to efficient price formation and liquidity improvement in the market.
This joint experiment will be conducted under the following structure:
Toshiba: Provides Toshiba's Simulated Bifurcation Machine customized for financial trading to
The methodology examined in this experiment should apply not only to high speed trading of stocks listed on the
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