Observational evidence is sought that the long‐term (104 yr) action of a mean motion resonance with Jupiter can produce structure in a meteoroid stream, concentrating meteoroids in a dense swarm. More specifically, predictions tabulated by Asher & Clube of enhanced meteor and fireball activity from a Taurid Complex swarm in the 7:2 resonance are compared with observational data collected in Japan over several decades. The swarm model was proposed for reasons independent of the observations analysed here, and these newly considered data are shown to be consistent with it. This allows increased confidence in the Taurid swarm theory, and more generally could mean that resonant trapping is a dynamical mechanism affecting a significant amount of meteoroidal material in the inner Solar system.
Financial exchanges sometimes employ a "price variation limit", which restrict trades out of certain price ranges within certain time spans to avoid sudden large price fluctuations.We built an artificial market model implementing a learning process to replicate bubbles that has the continues double auction mechanism and investigated price variation limits. We surveyed an adequate limitation price range and an adequate limitation time span for the price variation limit and found a parameters' condition of the price variation limit to prevent bubbles. The price variation limits are expected to be an especially effective way to prevent bubbles, so the model should be able to replicate bubbles. When we gave a bubble-inducing trigger, which is a rapid increment of the fundamental value, a bubble occurred in the case in which the model implemented the learning process and did not occur in the case without the process. We also showed that a hazard rate enables verification of whether the models can replicate a bubble process or not.
keywords: market maker, competition among markets, share of trading volume, high frequency trading, multi agent-based simulation
SummaryWe analyzed the impact of position-based market maker, which tries to maintain its neutral position, to the competition among stock exchanges by an artificial market simulation approach. In the previous study, we built an artificial market model and investigated for the impact of non-position-based market maker's spread to the markets' shares of trading volumes. However it had the serious problem that the non-position-based market maker is too simple to manage its own position properly and so we could not judge weather the result of previous study is correct or not. Thus in this study, we made a position-based market maker and explored the competition, in terms of taking markets' shares of trading volumes, between two artificial financial markets that have exactly the same specifications except existing a market maker, the non-position-based market maker or the position-based market maker. As a result, we found that the position-based market maker can acquire the share of trading volumes from the competitor even though its spread is bigger than bid-offer-spread of the competitor. Moreover, we revealed that position-based market maker can get a profit even in the situation that its spread or tick sizes of the stock exchanges are small. In addition to that, position-based market maker made a profit in almost all experiments which we conducted in this research by changing its spread and tick sizes of markets. At last, we confirmed that position-based market maker can manage its position properly compared to non-position-based market maker. In conclusion, the position-based market maker can not only supply liquidity to stock exchanges and contribute to acquire the share from the competitor as well as the non-position-based market maker does, but also manage its own position properly and make a profit.
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