论文标题
稳定和易变的市场环境中的预测盘中相关性：深度学习的证据
Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning
论文作者
论文摘要
财务中的标准方法和理论可能不足以捕获基于大规模数据集的财务预测问题中高度非线性的相互作用，深入的学习提供了一种能够洞悉作为复杂系统的市场相关性的方法。在本文中，我们将深度学习应用于计量经济学构造的梯度，以学习和利用标准普尔500种股票之间的滞后相关性，以比较稳定和易变的市场环境中的模型行为，在排除目标股票信息以进行预测中。为了衡量时间范围的效果，我们以不同的间隔长度预测日内和每日股票价格移动，并通过修改模型体系结构来衡量手头问题的复杂性。我们的发现表明，准确性虽然保持着重要意义，并证明了股票市场中滞后相关性的可利用性，但随着预测范围的较短而降低。我们讨论对现代金融理论的影响，以及我们的工作作为投资组合经理的调查工具的适用性。最后，我们表明，通过将模型的绩效暴露于2007/2008年最近的金融危机的环境中，我们的模型的性能是一致的。
Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile market environments, and under the exclusion of target stock information for predictions. In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our model architecture. Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers. Lastly, we show that our model's performance is consistent in volatile markets by exposing it to the environment of the recent financial crisis of 2007/2008.
