Jährlich veröffentlicht das Statistische Bundesamt Statistiken über die Besteuerung von Rentnerinnen und Rentnern, wegen langer Abgabe- und Einspruchsfristen für Einkommensteuererklärungen allerdings erst etwa 3,5 Jahre nach Ablauf des betref- fenden Statistikjahres. Jedoch liegt ein Teil...

This study evaluates näive and advanced prediction models when applied to style rotation strategies on the Johannesburg Stock Exchange ('JSE'). We apply 1- and 3-month style momentum as näive predictors against three tree-based machine learning ('ML') algorithms (advanced predictors),...

This study evaluates naïve and advanced prediction models when applied to style rotation strategies on the Johannesburg Stock Exchange (‘JSE’). We apply 1- and 3-month style momentum as naïve predictors against three tree-based machine learning (‘ML’) algorithms (advanced predictors),...

Jährlich veröffentlicht das Statistische Bundesamt Statistiken über die Besteuerung von Rentnerinnen und Rentnern, wegen langer Abgabe- und Einspruchsfristen für Einkommensteuererklärungen allerdings erst etwa 3,5 Jahre nach Ablauf des betref- fenden Statistikjahres. Jedoch liegt ein Teil...

We develop a novel sentiment measure derived from survey data to empirically vali date the Minsky-Kindleberger view on financial crises. Using survey data from multiple countries, we decompose beliefs into components explained by public information that are orthogonal to optimal machine beliefs,...

This study introduces a novel machine learning-based methodology for detecting and forecasting the strength of weak signals in the labor market, using Greece as a case study and utilizing Eurostat time series data (2000-2023). Weak signals, conceptualized as subtle anomalies within otherwise...

The magnitude and scope of the application of artificial intelligence (AI) and information-based computing methods to green and sustainable power generation systems have been significantly expanded to include research, initial development, implementation, and deployment. Over the past five...

Purpose - This study aims to forecast the stock market behavior of BRICS nations (Brazil, Russia, India, China and South Africa) using advanced machine learning models. The focus is on identifying market trends, predicting future index prices and analyzing returns. Design/methodology/approach -...

The Financial Lobster Bias describes how SMEs, driven by distorted liquidity perceptions, engage in aggressive expansion until financial breakdown occurs. Using data from 10,412 Spanish SMEs (2000-2024), this study shows that liquidity misperception-measured through two versions of the Liquidity...

We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using interpretable ML methods, we identify past flows and the...

Theories of limited attention predict that investors rely on typical patterns to navigate high-dimensional firm characteristics, making atypical firms hard to process. To quantify this difficulty, we propose a data-driven measure of firm atypicality using an autoencoder (ATYP). The model learns...

Projecting the effects of proposed policy reforms is challenging because no outcome data exist for regulations that governments have not yet implemented. We propose an ex ante deep learning framework that can project effects of proposed reforms by mapping outcomes observed under past regulations...

We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using interpretable ML methods, we identify past flows and the...

Theories of limited attention predict that investors rely on typical patterns to navigate high-dimensional firm characteristics, making atypical firms hard to process. To quantify this difficulty, we propose a data-driven measure of firm atypicality using an autoencoder (ATYP). The model learns...

Money laundering poses a serious threat to financial stability and requires effective national frameworks for prevention. This study investigates how the quality of legal and institutional frameworks affects the effectiveness of national anti-money laundering (AML) systems and their implications...

Context: Crop rotations provide agronomic benefits over monocropping, such as enhanced nitrogen supply, improved weed and pest control, and higher yields. Although the theoretical understanding of optimal rotations has advanced, little is known about their real-world implementation and the...

We investigate whether satellite observations of nitrogen dioxide (NO₂) - a short-lived pollutant primarily emitted by fossil fuel combustion - can improve the forecasting of oil demand. After retrieving, cleaning, and aggregating daily satellite data, we integrate NO₂ into a range of...

Researchers typically collect experimental data sequentially, allowing early outcome observations and adaptive treatment assignment to reduce exposure to inferior treatments. This article reviews multi-armed-bandit adaptive experimental designs that balance exploration and exploitation. Because...

Interpretability analysis methods, such as LIME and SHAP, are widely employed to explain the predictions of artificial intelligence models; however, they primarily function as post hoc tools and do not directly quantify the intrinsic interpretability of the models. Although it is commonly...

Do firms uniformly oppose regulations that increase production costs, or might industry leaders strategically support stricter standards as a competitive tool? We identify a specific mechanism through which large firms strategically support regulations to enhance their competitive position....

This paper describes a new public-access online database containing internationally comparable estimates of inequality of opportunity for seventy-two countries, covering two-thirds of the world's population. The estimates were computed directly from the unit-record microdata for 196 household...

This paper considers how a system designer can program a team of autonomous agents to coordinate with one another such that each agent selects (or covers) an individual resource with the goal that all agents collectively cover the maximum number of resources. Specifically, we study how agents...

We consider parametric portfolio policies of any complexity using deep neural networks to optimize investor utility. Risk aversion acts as an economic regularization mechanism, with higher risk aversion constraining model complexity. Empirically, Deep Parametric Portfolio Policies (DPPP)...

This paper explores the application of machine learning (ML) techniques to nowcast the monthly year-over-year growth rate of both total and non-primary GDP in Peru. Using a comprehensive dataset that includes over 170 domestic and international predictors, we assess the predictive performance of...

Die Nutzung von Scannerdaten in der Verbraucherpreisstatistik hat viele Vorteile, bringt allerdings auch einige Herausforderungen mit sich. Eine der Herausforderungen ist die Klassifizierung der Artikel nach dem vom Verbraucherpreisindex verwendeten Klassifikationssystem COICOP. Aufgrund der...

Auch die amtliche Statistik nutzt Methoden der Künstlichen Intelligenz (KI) und ihres Teilbereichs Maschinelles Lernen (ML). Dies wirft verschiedene ethische Fragestellungen auf, die im ersten Teil des Artikels ("Identifikation") mithilfe von Vorarbeiten aus anderen Staaten und von...

Künstliche Intelligenz (KI) hat mit ihrem Teilgebiet Maschinelles Lernen (ML) Einzug gehalten in die Verwaltung im Allgemeinen sowie in die amtliche Statistik in Deutschland im Speziellen. Welche ethischen Fragen sind jedoch beim Einsatz von KI/ML in der Produktion amtlicher Statistiken zu...

Inequality is a dynamic phenomenon, and the relative and absolute positions of individuals are subject to frequent shocks. It is important to know if preventive interventions mitigate adverse inequality effects of labor market shocks. We consider individuals up to three months before the...

We study how the effects of exports on earnings vary across individual workers, depending on a wide range of worker, firm, and job characteristics. To this end, we combine a generalized random forest with an instrumental variable strategy. Analyzing Germany's exports to China and Eastern Europe,...

We examine predictive machine learning studies from 50 top business and economic journals published between 2010 and 2023. We investigate their transparency regarding the predictive performance of machine learning models compared to less complex traditional statistical models that require fewer...

Können Methoden der Künstlichen Intelligenz helfen, Baustellenaktivitäten in Nordrhein-Westfalen zu erfassen und somit mehr Informationen über das dortige Baugeschehen zu erhalten? Der Artikel untersucht ob es möglich ist, einen Datensatz zu erstellen, um einen Algorithmus zu trainieren,...

In this study, the authors propose an advanced strategy to analyze thermal images for breast cancer detection employing machine learning techniques. By focusing on critical features that capture geometric and structural information in thermal images, the aim is to elevate the precision and...

Accurate macroeconomic forecasts are essential for effective policy decisions, yet their precision depends on the accuracy of the underlying assumptions. This paper examines the extent to which assumption errors affect forecast accuracy, introducing the average squared assumption error (ASAE) as...

We introduce a novel, non-parametric approach for estimating house price indices that capture heterogeneous price developments independently of strict functional form assumptions. Utilizing the potential outcomes framework, our approach employs causal forests to effectively address changes in...

This study presents an exploratory analysis of the key factors contributing to fatal and severe crashes on German motorways. We employ Poisson and Negative Binomial regression models, combined with Lasso regularization and stability selection, to explore model specifications incorporating...