Abstract
This is an extension from a selected paper from JSAI2020. In this study, we propose a stochastic model for predicting the behavior of financial market traders. First, using real ordering data that includes traders’ information, 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 actions 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.
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Notes
- 1.
https://www.jpx.co.jp/english/systems/equities-trading/. This data is available for those who have a contract for the disclosure system.
- 2.
This data is generally not available. It requires a special agreement with Japan Exchange Group.
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Acknowledgement
We thank the Japan Exchange Group, Inc. for providing the data.This research was supported by MEXT via Exploratory Challenges on Post-K computer (study on multilayered multiscale space-time simulations for social and economic phenomena) and the computational resources of the HPCI system provided by the Information Technology Center at The University of Tokyo, and the Joint Center for Advanced High Performance Computing through the HPCI System Research Project (Project ID: hp190150).
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Hirano, M., Matsushima, H., Izumi, K., Sakaji, H. (2021). STBM: Stochastic Trading Behavior Model for Financial Markets. In: Yada, K., et al. Advances in Artificial Intelligence. JSAI 2020. Advances in Intelligent Systems and Computing, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-73113-7_14
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DOI: https://doi.org/10.1007/978-3-030-73113-7_14
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