著者

西岡 寛兼 鳥海 不二夫 石井 健一郎

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会全国大会論文集 第23回 (2009)

巻号頁・発行日

pp.3G3OS127, 2009 (Released:2018-07-30)

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信頼性の高い人工市場シミュレーションを行うために,人工市場が十分に実市場を近似しているか評価する必要がある.そこで本研究では,板情報に基づいて市場の時系列分析を行う手法を考案した.本手法では,板情報から注文の規模を推測し,注文規模の分布がどのように変化するかという点に注目して時系列分析を行う.時系列データの類似度に基づき市場の時刻識別を行ったところ,十分な精度で識別可能であることが確認できた.

著者

鳥海 不二夫 西岡 寛兼 梅岡 利光 石井 健一郎

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.27, no.3, pp.143-150, 2012 (Released:2012-03-27)

参考文献数

14

被引用文献数

1 2

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The financial markets are fluctuating consistently. Therefore, it is difficult to analyze the financial market based on the same theory, without depending on the state of the market. So we use the concept ofmarket condition change. To estimate the points when the market change occurred in a real market is effective for market analysis. Thus, in this paper, we propose a method to detect the changes in market conditions. In the proposed method, we focuse on the stock board instead of the price data. From the stock board data, we classify short time series data to clusters by using k-means clustering method. Then, we generate Hidden Markov Model(HMM) from the transition probability of each clusters. By using the likelihood of HMM, we analyze the similarities of each time series data. We performed an experiment to evaluate the effectiveness of the method by discriminant analysis of time series data which created from opening session and continuous session. As a result, two time series data are discriminated with high accuracy. Finally, we compared the discriminate performance of proposed method with another discriminant analysis methods. We used three types of time series data of stock board and price data, before the Lehman's fall financial crisis. From the result, the proposed method shows the best performance in discriminating each financial data.