著者

Masanori HIRANO Hiroyasu MATSUSHIMA Kiyoshi IZUMI Hiroki SAKAJI

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34回全国大会(2020)

巻号頁・発行日

pp.1K4ES204, 2020 (Released:2020-06-19)

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In this study, we propose a stochastic model for predicting the behavior of financial market traders. First, using real ordering data that includes masked traders' IDs, we cluster the traders and select a recognizable cluster that appears to employ a high-frequency traders' market-making (HFT-MM) strategy. Then, we use an LSTM-based stochastic prediction model to predict the traders' behavior. This model takes the market order book state and a trader's ordering state as input and probabilistically predicts the trader's actions over the next one minute. The results show that our model can outperform both a model that randomly takes action and a conventional deterministic model. Herein, we only analyze limited trader type but, if our model is implemented to all trader types, this will increase the accuracy of predictions for the entire market.

著者

Masanori HIRANO Kiyoshi IZUMI Hiroki SAKAJI

雑誌

2022年度 人工知能学会全国大会(第36回)

巻号頁・発行日

2022-04-07

著者

Kohei NISHIMURA Hiroki SAKAJI Kiyoshi IZUMI

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32回 (2018)

巻号頁・発行日

pp.1P104, 2018 (Released:2018-07-30)

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Visualizing chains of Economic events and Financial matters including background eventsfrom text data is useful for investors and manager to understand Economic events and Financial matters properly. However, extracting chains of Economic events and Financial matters manually takes a lot of time. Therefore, we treat chains of Economic events and Financial matters as chains of causal relations (we call them Causal Network) and propose the procedure creating causal network using vectors similarity which represent semantic similarities between expressions of Economic events and Financial matters.

著者

Masanori HIRANO Kiyoshi IZUMI Hiroki SAKAJI

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35回全国大会(2021)

巻号頁・発行日

pp.2N1IS2a03, 2021 (Released:2021-06-14)

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This paper proposes a new model to reverse engineer and predict traders' behaviors for financial market. In this model, we used an architecture based on the transformer and residual block, and a loss function based on Kullback-Leibler divergence. In addition, we established a new evaluation metric, and consequently, succeeded in constructing a model that outperforms conventional methods and has an efficient architecture. In the future, we will build a model with higher performance and versatility. Moreover, we will introduce this model to financial simulations.

著者

Tomoki ITO Hiroki SAKAJI Kiyoshi IZUMI

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33回全国大会(2019)

巻号頁・発行日

pp.4Rin125, 2019 (Released:2019-06-01)

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To extract business contents automatically from financial reports is an important problem in the financial industry. Especially, segment names and their explanations are important contents to be extracted. However, the methods for extracting these types of information from financial reports have not been established. In this study, we aim to develop a practical solution for extracting these types of information. To solve this problem, we developed a manually annotated dataset for the task of extracting the segment names and their explanations of each company from financial reports and then developed a recurrent neural network model to solve this task. Our developed method using the manually annotated dataset outperformed the baseline methods without the dataset in the task of extracting segment names and their explanations of each company. This results demonstrated that our approach is useful for extracting the business contents of each company. This work is the first work for applying a machine learning method to the task of extracting segment names and their explanations. Insights from this work should be useful in the industrial area.

著者

Masanori HIRANO Hiroto YONENOH Kiyoshi IZUMI

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32回全国大会(2018)

巻号頁・発行日

pp.2P205, 2018 (Released:2018-07-30)

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Basel regulatory framework, one of CAR (capital adequacy ratio) regulations, is said to make markets destabilized in a previous study. But the previous study included some inappropriate assumptions. So, this study assessed this destabilizing effects with a new model. In my model, FCN agents and 2 kinds of portfolio agents, CAR regulated ones and not regulated ones, were included. Using this model, some simulations were run. As results, the simulations revealed some facts: 1. Asset management using portfolio stabilizes markets and the stabilizing effect are significant if there are a lot of markets included in the portfolio; 2. CAR regulation destabilizes markets and vanish the stabilizing effects of portfolio. In addition, the results of my simulations suggest that CAR regulation does not only raise the chance of price crashes but also depress whole price.

著者

Masanori HIRANO Hiroto YONENOH Kiyoshi IZUMI

出版者

人工知能学会

雑誌

2018年度人工知能学会全国大会(第32回)

巻号頁・発行日

2018-04-12

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Basel regulatory framework, one of CAR (capital adequacy ratio) regulations, is said to make markets destabilized in a previous study. But the previous study included some inappropriate assumptions. So, this study assessed this destabilizing effects with a new model. In my model, FCN agents and 2 kinds of portfolio agents, CAR regulated ones and not regulated ones, were included. Using this model, some simulations were run. As results, the simulations revealed some facts: 1. Asset management using portfolio stabilizes markets and the stabilizing effect are significant if there are a lot of markets included in the portfolio; 2. CAR regulation destabilizes markets and vanish the stabilizing effects of portfolio. In addition, the results of my simulations suggest that CAR regulation does not only raise the chance of price crashes but also depress whole price.