دانلود رایگان مقاله انگلیسی پیش بینی مبتنی بر اخبار شاخص های اقتصاد کلان: مدل پیش بینی قابل تفسیر - الزویر 2019

abstract

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.

Conclusion

The volatile nature of the economy affects the operation of firms in key dimensions, such as prices for goods and services, financial risk, utilization of supply chains and customer demand. Hence, it is important for managers to obtain an accurate prognosis of developments in the economy in order to direct operations accordingly. Hitherto, macroeconomic forecasts have been dominated by expert opinions and rather simple time series models, while the big data era has created new opportunities to enhance the predictive power of macroeconomic forecasts based on unstructured data sources. This work contributes a news-based methodology for predicting macroeconomic indicators. First, we experiment with conventional models from machine learning in order to predict macroeconomic outcomes on the basis of word occurrences and historic lags. Back-testing shows that this method outperforms the benchmark models in an out-of-sample evaluation for various prediction horizons and macroeconomic indicators. Yet text-based predictions represent a challenging undertaking due to potential overfitting for a high-dimensional input. Instead, we suggest an alternative approach whereby the words from different semantic categories are mapped onto latent structures as a form of feature engineering. This shrinks the feature space considerably, thereby achieving superior out-of-sample performance in multiple experiments. Beyond that, we propose semantically-structured variant of partial least squares that reaches a comparable accuracy, but while fulfilling the demand of practitioners in being fully interpretable. This contributes to a greater understanding of how qualitative information can be used to make long-term predictions for key macroeconomic indicators.