发帖
雷达卡
《Extending Deep Learning Models for Limit Order Books to Quantile
Regression》
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作者：
Zihao Zhang, Stefan Zohren, Stephen Roberts
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最新提交年份：
2019
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英文摘要：
We showcase how Quantile Regression (QR) can be applied to forecast financial returns using Limit Order Books (LOBs), the canonical data source of high-frequency financial time-series. We develop a deep learning architecture that simultaneously models the return quantiles for both buy and sell positions. We test our model over millions of LOB updates across multiple different instruments on the London Stock Exchange. Our results suggest that the proposed network not only delivers excellent performance but also provides improved prediction robustness by combining quantile estimates.
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中文摘要：
我们展示了分位数回归（QR）如何应用于使用限额订单簿（LOB）预测财务回报，LOB是高频金融时间序列的规范数据源。我们开发了一个深度学习架构，可以同时为买入和卖出头寸的回报分位数建模。我们在伦敦证券交易所的多个不同工具上对我们的模型进行了数百万次LOB更新测试。我们的结果表明，所提出的网络不仅提供了优异的性能，而且通过结合分位数估计提供了改进的预测鲁棒性。
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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Extending_Deep_Learning_Models_for_Limit_Order_Books_to_Quantile_Regression.pdf
(596.45 KB)
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