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Furuhata M.,Japan Advanced Institute of Science and Technology | Mizuta T.,Sparx Asset Management Co. | So J.,Sparx Asset Management Co.
Proceedings - IEEE International Conference on Data Mining, ICDM | Year: 2010

In order to deal with sudden unexpected changes of circumstances, we propose a new forecast method based on paired evaluators, the stable evaluator and the reactive evaluator. These two evaluators are good at detecting consecutive concept drifts. We conduct a back-testing using financial data in order to demonstrate the performance of our proposing forecast method. The results of the back-testing show that our method is effective and robust even against the late-2000s recessions. © 2010 IEEE.


Yagi I.,Kanagawa Institute of Technology | Mizuta T.,SPARX Asset Management Co. | Izumi K.,University of Tokyo
2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2012 - Proceedings | Year: 2012

Deterioration in the fundamentals of firms due to scandals or disasters causes declines in their stock prices. We empirically know that stock prices rebound after they largely fall. In this paper, this trend is called the reversal phenomenon. There has been some preceding research on this issue; however, little has been explained about market mechanisms such as the market pricing mechanism responsible for the reversal in large declines in stock prices. We reproduced the reversal phenomenon in an artificial market with a degree of variation in expected prices, and not with the overreaction hypothesis, and found that a call market, which is a non-continuous double auction, imposes a condition where the market becomes non-efficient. © 2012 IEEE.


Kusada Y.,University of Tokyo | Mizuta T.,SPARX Asset Management Co. | Hayakawa S.,Osaka Exchange Inc | Izumi K.,University of Tokyo
Transactions of the Japanese Society for Artificial Intelligence | Year: 2015

We 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. © 2015, Japanese Society for Artificial Intelligence. All rights reserved.


Furuhata M.,University of Southern California | Mizuta T.,SPARX Asset Management Co. | So J.,SPARX Asset Management Co.
Studies in Computational Intelligence | Year: 2013

We consider the problem of forecasting under the environments of sudden unexpected changes. The objective of the forecasting is to detect several different types of changes and to be adaptive to these changes in the automated way. The main contribution of this paper is a development of a novel forecast method based on paired evaluators, the stable evaluator and the reactive evaluator, that are good at dealing with consecutive concept drifts. A potential application of such drifts is Finance. Our back-testing using financial data in US demonstrates that our forecasting method is effective and robust against several sudden changes in financial markets including the late-2000s recessions.


Yagi I.,Tokyo Institute of Technology | Mizuta T.,SPARX Asset Management Co. | Izumi K.,University of Tokyo
Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 | Year: 2010

Since the subprime mortgage crisis in the United Sates, stock markets around the world have crashed, revealing their instability. To stem the decline in stock prices, short-selling regulations have been implemented in many markets. However, their effectiveness remains unclear. In this paper, we discussed the effectiveness of short-selling regulation which was invoked temporarily after the market satisfies a regulation condition using an artificial market. First, we proposed an artificial market in which short-selling was regulated after the market satisfied the regulation condition. Next, we observed price variations in the markets in which the durations of short-selling regulation were different and discussed the bubble mechanism of them. Then, we observed the correlation between market stability and regulation periods and it was found that the longer the regulation period was, the more instable markets were. Therefore, we have found short-selling regulation had the property that it not only stemed the decline in the prices but also increased the prices excessively and market instability increased with increasing regulation period. Finally, we determined performances of each agent type in the market in which the regulation was invoked. © 2010 IEEE.


Yagi I.,Tokyo Institute of Technology | Mizuta T.,SPARX Asset Management Co. | Izumi K.,University of Tokyo
Transactions of the Japanese Society for Artificial Intelligence | Year: 2011

Since the subprime mortgage crisis in the United Sates, stock markets around the world have crashed, revealing their instability. To stem the decline in stock prices, short-selling regulations have been implemented in many markets. However, their effectiveness remains unclear. In this paper, we discuss the effectiveness of short-selling regulation using artificial markets. An artificial market that is an agent-based model of financial markets is useful to observe the market mechanism. That is, it is effective for analyzing causal relationship between the behaviors of market participants and the transition of market price. We constructed an artificial market that allows short-selling and an artificial market with short-selling regulation and have observed the stock prices in both of these markets. We have demonstrated that our artificial market had some properties of actual markets. We found that the market in which short-selling was allowed was more stable than the market with short-selling regulation, and a bubble emerged in the regulated market. We evaluated the values of assets of agents who used three trading strategies, specifically, these agents were fundamentalists, chartists, and noise traders. The fundamentalists had the best performance among the three types of agents.


Mizuta T.,SPARX Asset Management Co. | Mizuta T.,University of Tokyo | Izumi K.,University of Tokyo | Yoshimura S.,University of Tokyo
Proceedings of the 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 | Year: 2013

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. © 2013 IEEE.


Yagi I.,Tokyo Institute of Technology | Mizuta T.,SPARX Asset Management Co. | Izumi K.,Japan National Institute of Advanced Industrial Science and Technology
Studies in Computational Intelligence | Year: 2010

Since the subprime mortgage crisis in the United Sates, stock markets around the world have crashed, revealing their instability. To stem the decline in stock prices, short-selling regulations have been implemented in many markets. However, their effectiveness remains unclear. In this paper, we discuss the effectiveness of short-selling regulation using artificial markets. An artificial market is an agent-based model of financial markets. We constructed an artificial market that allows short-selling and an artificial market with short-selling regulation and have observed the stock prices in both of these markets. We found that the market in which short-selling was allowed was more stable than the market with short-selling regulation, and a bubble emerged in the regulated market. We evaluated the values of assets of agents who used three trading strategies, specifically, these agents were fundamentalists, chartists, and noise traders. The fundamentalists had the best performance among the three types of agents. Finally, we observe the price variations when the market price affects the theoretical price. © 2010 Springer-Verlag Berlin Heidelberg.

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