We analyze the relation between digitalization and the market value of US insurance companies. To create a text‐based measure that captures the extent to which insurers digitalize, we apply an unsupervised machine learning algorithm—Latent Dirichlet Allocation—to their annual reports. We...

We decompose the textual data in a daily Norwegian business newspaper into news topics and investigate their predictive and causal role for asset prices. Our three main findings are: (1) a one unit innovation in the news topics predict roughly a 1 percentage point increase in close-to-open...

Purpose: In recent years, a number of practical logistic applications of machine learning (ML) have emerged, especially in Supply Chain Management (SCM). By linking applied ML methods to the SCM task model, the paper indicates the current applications in SCM and visualises potential research...

Latin America and the Caribbean must respond to the challenge of climate change while making progress with other sustainable development goals. How much will it cost to meet climate change goals in this context? This work reviews the evidence on the costs of meeting the goals the goals of the...

We analyze a corpus of 564 business cycle forecast reports for the German economy. The dataset covers nine institutions and 27 years. From the entire reports we select the parts that refer exclusively to the forecast of the German economy. Sentiment and frequency analysis confirm that the mode...

Digitization and computer science have established a whole new set of methods to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, statistical algorithms that automatically infer themes from large collections of texts. In...

News-based indicators are in vogue in economics. But they tend to be applied with little consideration for the properties of news itself. In this paper, we try to shed light on the nature of this type of data. Drawing from established findings in communication science and journalism studies we...

This paper deals with the problem of deriving consistent time-series from newspaper contentbased topic models. In the first part, we recapitulate a few our own failed attempts, in the second one, we show some results using a twin strategy, that we call prototyping and seeding. Given the...