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)

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.

言及状況

外部データベース (DOI)

Twitter (11 users, 13 posts, 52 favorites)

A short paper showing how you can use new type of modelization (#LSTM) for better predictions of the behavior of financial market traders than conventional deterministic models --> https://t.co/uAAJ4l0S21
Modeling stock bond correlations across frequencies using DCC-MIDAS - paper https://t.co/R3jvC2IMXK https://t.co/daaoZ9cfL5
もう,JSAIの論文ってonlineで出てるんですね.自分のも見つけられた https://t.co/FyDtPX4W78

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