英文标题：
《BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books》
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作者：
Zihao Zhang, Stefan Zohren, Stephen Roberts
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最新提交年份：
2018
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英文摘要：
  We showcase how dropout variational inference can be applied to a large-scale deep learning model that predicts price movements from limit order books (LOBs), the canonical data source representing trading and pricing movements. We demonstrate that uncertainty information derived from posterior predictive distributions can be utilised for position sizing, avoiding unnecessary trades and improving profits. Further, we test our models by using millions of observations across several instruments and markets from the London Stock Exchange. Our results suggest that those Bayesian techniques not only deliver uncertainty information that can be used for trading but also improve predictive performance as stochastic regularisers. To the best of our knowledge, we are the first to apply Bayesian networks to LOBs.
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中文摘要：
我们展示了如何将辍学变分推理应用于一个大规模的深度学习模型，该模型通过极限订单簿（LOB）预测价格变动，LOB是代表交易和定价变动的规范数据源。我们证明，从后验预测分布中获得的不确定性信息可用于头寸调整、避免不必要的交易和提高利润。此外，我们还使用伦敦证券交易所的多个工具和市场的数百万次观测数据来测试我们的模型。我们的结果表明，这些贝叶斯技术不仅提供了可用于交易的不确定性信息，而且还提高了作为随机正则器的预测性能。据我们所知，我们是第一个将贝叶斯网络应用于LOB的公司。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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