论文标题
高频欧洲价值交易的深度学习预测不确定性的投资规模
Investment sizing with deep learning prediction uncertainties for high-frequency Eurodollar futures trading
论文作者
论文摘要
在这项工作中，我们表明，从深度学习模型中收集的预测不确定性估计对于影响跨行业风险资本的相对分配是有用的投入。这样，考虑不确定性很重要，因为它允许以原则性和数据驱动的方式跨越贸易机会的投资规模扩展。我们通过预测模型展示了这种见解，并根据不考虑不确定性的交易策略找到了基于Sharpe比率的明显超越性能，或者利用基于市场的替代基于市场的统计量来代表不确定性。新颖性是我们在2018年每个交易日的欧洲价值期货限制订单账单上最高级别的高频数据的建模，我们可以预测小时时间范围内利率曲线的变化。我们有动力研究这些泛滥的利率衍生品的市场，因为它是深度且液体的，并有助于全球金融的有效运作 - 尽管通过其在学术文献中所包含的建模相对较少。因此，我们验证了这个复杂和多维资产价格空间中的交易应用程序的预测模型和不确定性估计的效用。
In this work we show that prediction uncertainty estimates gleaned from deep learning models can be useful inputs for influencing the relative allocation of risk capital across trades. In this way, consideration of uncertainty is important because it permits the scaling of investment size across trade opportunities in a principled and data-driven way. We showcase this insight with a prediction model and find clear outperformance based on a Sharpe ratio metric, relative to trading strategies that either do not take uncertainty into account, or that utilize an alternative market-based statistic as a proxy for uncertainty. Of added novelty is our modelling of high-frequency data at the top level of the Eurodollar Futures limit order book for each trading day of 2018, whereby we predict interest rate curve changes on small time horizons. We are motivated to study the market for these popularly-traded interest rate derivatives since it is deep and liquid, and contributes to the efficient functioning of global finance -- though there is relatively little by way of its modelling contained in the academic literature. Hence, we verify the utility of prediction models and uncertainty estimates for trading applications in this complex and multi-dimensional asset price space.
