基于新闻的宏观经济指标预测：可解释预测的语义路径模型
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions
摘要：
宏观经济环境影响到以下方面的业务：原材料价格、融资、供应链利用率和需求配额。为了适应经济环境，公共和私营部门的决策者需要对经济前景进行准确预测。现有预测框架的预测主要基于时间序列分析以及专家的判断。因此，目前的方法往往是有偏见的，容易出错。为了减少预测误差，本文提出了一种创新的方法，扩展滞后变量与非结构化数据的金融新闻的形式：（1）我们应用各种模型从机器学习字数作为高维输入。然而，这种方法遭受低可解释性和过度拟合，激发了以下补救措施。(2)我们遵循的直觉，经济气候是由一般的情绪，并建议投影的话到潜在的语义结构作为一种手段的功能工程。(3)我们提出了一个语义路径模型，连同基于正则化的估计技术，以产生充分的可解释性的预测。我们通过利用80，813个临时公告来展示我们方法的预测性能，以便对关键宏观经济指标进行长达24个月的长期预测。回溯测试显示，预测误差大大减少。
英文摘要：
The macroeconomic climate influences operations with regard to, e.g., raw material prices, financing, supply chain utilization and demand quotas. In order to adapt to the economic environment, decision-makers across the public and private sectors require accurate forecasts of the economic outlook. Existing predictive frameworks base their forecasts primarily on time series analysis, as well as the judgments of experts. As a consequence, current approaches are often biased and prone to error. In order to reduce forecast errors, this paper presents an innovative methodology that extends lag variables with unstructured data in the form of financial news: (1) we apply a variety of models from machine learning to word counts as a high-dimensional input. However, this approach suffers from low interpretability and overfitting, motivating the following remedies. (2) We follow the intuition that the economic climate is driven by general sentiments and suggest a projection of words onto latent semantic structures as a means of feature engineering. (3) We propose a semantic path model, together with estimation technique based on regularization, in order to yield full interpretability of the forecasts. We demonstrate the predictive performance of our approach by utilizing 80,813 ad hoc announcements in order to make long-term forecasts of up to 24 months ahead regarding key macroeconomic indicators. Back-testing reveals a considerable reduction in forecast errors.
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