英文标题：
《Universal features of price formation in financial markets: perspectives
from Deep Learning》
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作者：
Justin Sirignano and Rama Cont
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最新提交年份：
2018
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英文摘要：
Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a stock, as revealed through the order book, to subsequent variations in its market price. We assess the model by testing its out-of-sample predictions for the direction of price moves given the history of price and order flow, across a wide range of stocks and time periods. The universal price formation model is shown to exhibit a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific. The universal model --- trained on data from all stocks --- outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock, showing that the universal nature of price formation weighs in favour of pooling together financial data from various stocks, rather than designing asset- or sector-specific models as commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations is shown to improve forecasting performance, showing evidence of path-dependence in price dynamics.
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中文摘要：
通过将大规模深度学习方法应用于包含数十亿美国股票电子市场报价和交易的高频数据库，我们发现了非参数证据，证明存在一种与股票供求动态相关的普遍且稳定的价格形成机制，如订单所示，其市场价格的后续变化。我们通过测试其对价格变动方向的样本外预测来评估该模型，考虑到价格和订单流的历史，跨越广泛的股票和时间段。通用价格形成模型显示，对于不同部门的各种股票，跨时间的样本外预测精度非常稳定。有趣的是，这些结果也适用于不属于训练样本的股票，表明该模型捕获的关系是普遍的，而不是特定于资产的。基于所有股票数据训练的通用模型在样本外预测精度方面优于基于任何给定股票时间序列训练的特定资产线性和非线性模型，表明价格形成的普遍性有利于汇集各种股票的财务数据，而不是像通常那样设计特定于资产或部门的模型。基于波动性、价格水平或平均价差的标准数据规范化，或将培训数据划分为部门或类别，如大型/小型股票，不会改善培训结果。另一方面，在过去的许多观察中纳入价格和订单流历史可以提高预测性能，显示价格动态中存在路径依赖的证据。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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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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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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