论文标题
宏观经济学和金融中的学习概率分布
Learning Probability Distributions in Macroeconomics and Finance
论文作者
论文摘要
我们提出了一种深度学习方法，以预测宏观经济和财务时间序列。能够从数据丰富的环境中学习复杂的模式，我们的方法对于取决于大量经济成果的不确定性的决策很有用。具体而言，这对于面临非对称依赖对可能非高斯和非线性变量结果的不对称依赖性的代理人是有益的。我们在两个不同的数据集中显示了所提出的方法的有用性，在该数据集中，机器从数据中学习模式。首先，我们构建了宏观经济风扇图表，这些图表反映了高维数据集的信息。其次，我们说明了预测股票回报分布的收益，这些股票回报分布重尾，不对称且信噪比低。
We propose a deep learning approach to probabilistic forecasting of macroeconomic and financial time series. Being able to learn complex patterns from a data rich environment, our approach is useful for a decision making that depends on uncertainty of large number of economic outcomes. Specifically, it is informative to agents facing asymmetric dependence of their loss on outcomes from possibly non-Gaussian and non-linear variables. We show the usefulness of the proposed approach on the two distinct datasets where a machine learns the pattern from data. First, we construct macroeconomic fan charts that reflect information from high-dimensional data set. Second, we illustrate gains in prediction of stock return distributions which are heavy tailed, asymmetric and suffer from low signal-to-noise ratio.
