We built an artificial market model and investigated the impact of large erroneous orders on price formations. Comparing the case of consented large erroneous orders in the short term with that of continuous small erroneous orders in the long term, if amounts of orders are the same, we found that the orders induced almost the same price fall range. We also analyzed effects of price variation limits for erroneous orders and found that price variation limits that employ a limitation term shorter than the time erroneous orders exist effectively prevent large price fluctuations. We also investigated effects of up-tick rules adopting the trigger method that the Japan Financial Services Agency adopted on November 2013.

In financial stock markets, dark pools, which never provide any order books and quotes, are becoming widely used. It is said that dark pools may lead to stabilization of markets. However, an increased use of dark pools does raise regulatory concerns as it may ultimately affect the quality of the price discovery mechanism. In this study, we built an artificial market model, multi-agent simulation, including one lit market, which provides all order books to investors, and one dark pool to investigate whether dark pools stabilize markets or not. We found that as the dark pool is increasingly used, markets become more stable. We also found that using the dark pool more reduces the market impacts. However, if other investors' usage rates of dark pools become too large, investors must use the dark pool more than other investors to avoid market impacts. When tick size of a lit market is large, dark pools are more useful to avoid market impacts. These results suggest that dark pools stabilize markets when the usage rate is under some threshold and negatively affect the market when the usage rate is over that threshold. Our simulation results suggested the threshold might be much lager than the usage rate in present real financial markets.

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.

Existing commercial finite element analysis (FEA) codes do not exhibit the performance necessary for large scale analysis on parallel computer systems. In this paper, we demonstrate the performance characteristics of a commercial parallel structural analysis code, ADVC, on Blue Gene/L (BG/L). The numerical algorithm of ADVC is described, tuned, and optimized on BG/L, and then a large scale drop impact analysis of a mobile phone is performed. The model of the mobile phone is a nearly-full assembly that includes inner structures. The size of the model we have analyzed has 47 million nodal points and 142 million DOFs. This does not seem exceptionally large, but the dynamic impact analysis of a product model, with the contact condition on the entire surface of the outer case under this size, cannot be handled by other CAE systems. Our analysis is an unprecedented attempt in the electronics industry. It took only half a day, 12.1 hours, for the analysis of about 2.4 milliseconds. The floating point operation performance obtained has been 538 GFLOPS on 4096 node of BG/L.