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.

Introduction

Macroeconomic developments, such as cyclic downturns or the economic circumstances associated with the U. S. subprime crisis, affect firm operations in multiple ways and represent direct challenges to management (e. g. Demyanyk & Hasan, 2010; Goudie & Meeks, 1982). Examples include changes in the price of goods and raw materials, as well as the impact on overall demand, supply chain utilization and even operational processes (Xu, Pinedo, & Xue, 2017). Therefore, firms are interested in foreseeing the future economic climate in order to manage operations accordingly and hedge potential risks. In this context, operational research (OR) has a long tradition of addressing such risks (Demyanyk & Hasan, 2010). Our discipline has thus contributed to anticipating a variety of developments at a macroeconomic level, including financial distress (Geng, Bose, & Chen, 2015), liquidity risks (Shaik, 2015), credit risks (Akkoç, ۲۰۱۲; Desai, Crook, & Overstreet, 1996), financial crises (Demyanyk & Hasan, 2010; Huang, Kou, & Peng, 2017), currency crises (Sevim, Oztekin, Bali, Gumus, & Guresen, 2014) and bankruptcy (Du Jardin, 2015; McKee & Lensberg, 2002; Sun & Shenoy, 2007), especially in the financial sector (Tam & Kiang, 1992) Future expectations regarding the macroeconomic environment play a critical role in the decision-making process for many organizations (Xu et al., 2017). Hence, decision-makers across all sectors must analyze the current economic environment and form accurate expectations about future economic trends in order to support the operational strategy of organization and long-term management. As a result, macroeconomic variables, and the accurate prediction thereof, form the basis for a wide array of OR models (e. g. Calabrese, Degl’Innocenti, & Osmetti, 2017; Fethi & Pasiouras, 2010; Gutiérrez & Lozano, 2012). The importance of accurate long-term forecasts for firm operations has driven the extensive amount of research conducted with respect to macroeconomic predictions. Specific examples from the OR domain include short-term predictions of asset-related values, including government bonds (Tay & Cao, 2001) and stock indices (e. g. Huang, Nakamori, & Wang, 2005; Kung & Yu, 2008; Oztekin, Kizilaslan, Freund, & Iseri, 2016). Further research focuses on forecasting macroeconomic indicators of single countries (e. g. Mahmoud, Motwani, & Rice, 1990) or the relationship between countries (Sermpinis, Theofilatos, Karathanasopoulos, Georgopoulos, & Dunis, 2013). Other works propose agent-based simulations to study the behavior of human forecasters (Bovi & Cerqueti, 2016). Previous efforts at forecasting macroeconomic indicators have made use of various input features and methodologies. Historic time series data is a staple input for macroeconomic forecasting, and has been applied to make both short- and long-term predictions (e. g. Jansen, Jin, & de Winter, 2016; Mahmoud et al., 1990; Sermpinis et al., 2013). A prevalent alternative is the subjective judgments of professional forecasters (Matsypura, Thompson, & Vasnev, 2018) such as those used by the European Central Bank.