STBM+: Advanced Stochastic Trading Behavior Model for Financial Markets using Residual Blocks or Transformers
STBM+: Advanced Stochastic Trading Behavior Model for Financial Markets using Residual Blocks or Transformers
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DOI：
10.1007/s00354-021-00145-z
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发表时间：
2021-11
影响因子：
2.6
通讯作者：
Masanori Hirano;K. Izumi;Hiroki Sakaji
中科院分区：
文献类型：
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作者：
Masanori Hirano;K. Izumi;Hiroki Sakaji
本研究提出一个新的模型，以反向工程和预测交易者的行为，为金融市场。这项试验对于建立更可靠的模拟至关重要，因为模型的可靠性是越来越多地使用模拟的基本问题。因此，我们试图通过交易者的未来行为预测，利用实际订单数据建立一个金融交易者的行为模型。本研究的重点是一类交易者采用高频做市（HFT-MM）在金融市场的交易。在我们的实验中，我们建立了预测每个交易者下一步行动的模型，并评估这些模型在未来一分钟内成功预测交易者未来行动的准确性。虽然任务与以前的工作相同，但本研究新使用了基于Transformer和残差块的架构，以及基于Kullback-Leibler散度（KLD）的损失函数。此外，我们建立了一个新的评价指标。因此，我们的新模型，无论是基于transformer还是基于剩余块的模型，在新旧评估指标方面都优于之前基于LSTM的模型。这些结果表明，Transformer和剩余块可以有效地捕捉交易者的行为。此外，基于KLD的新损失函数也表现出比以前的基于MSE的损失函数更好的结果。我们假设这是因为基于KLD的损失函数由于其数学形式而更适合此任务。
This study proposes a new model to reverse engineer and predict traders’ behavior for the financial market. This trial is essential to build a more reliable simulation because the reliability of models is a fundamental issue in the increasing use of simulations. Thus, we tried to build a behavior model of financial traders through the traders’ future action predicting using the actual order data. This study focused on one category of traders employing high-frequency market-making (HFT-MM) trading in financial markets. In our experiments, we build models for predicting the next actions of each trader and evaluate how correctly these models successfully predict trades’ future actions in the next one minutes. Although the task is the same as previous work, this study newly used an architecture based on the transformer and residual block, and a loss function based on the Kullback-Leibler divergence (KLD). In addition, we established a new evaluation metric. Consequently, our new models, both transformer-based and residual-block-based models, outperformed the previous model based on LSTM in terms of both old and new evaluation metrics. These results suggested that transformer and residual block are effective in capturing traders’ behaviors. In addition, the KLD-based new loss function also showed better results than the previous MSE-based loss function. We assumed it is because the KLD-based loss function has a better fitting to this task due to its mathematical form.