The 34th Annual Conference of the Japanese Society for Artificial Intelligence, p. 1K4-ES-2-04, June 9, 2020
The 34th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2020)
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.
Deep Learning; Data Mining; High-frequency Trade; Market-making; Clustering;
10.11517/pjsai.JSAI2020.0_1K4ES204
@inproceedings{Hirano2020-jsai34,
title={{STBM: Stochastic Trading Behavior Model for Financial Markets Based on Long Short-Term Memory}},
author={Masanori HIRANO and Hiroyasu MATSUSHIMA and Kiyoshi IZUMI and Hiroki SAKAJI},
booktitle={The 34th Annual Conference of the Japanese Society for Artificial Intelligence},
pages={1K4-ES-2-04},
doi={10.11517/pjsai.JSAI2020.0_1K4ES204},
year={2020}
}