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...

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),...

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 examines the economic returns to education in the U.S. using 2024 CPS data and compares Ordinary Least Squares (OLS) regression with a Double Machine Learning (DML) framework incorporating models such as random forests, boosted trees, lasso, GAMs, and neural networks (MLP). Results...

Purpose: This study addresses a real-world container loading problem (CLP) encountered in a logistics company in Turkey, filling a gap in the literature by solving practical constraints using a state-of-the-art algorithm. The problem involves constraints such as rotations, stackability, loading...

We develop a novel sentiment measure from survey forecasts that captures the component of beliefs arising from the systematic misaggregation of public information relative to a machine benchmark based on the same information set. We extend this sentiment measure historically for a panel of 78...

This study empirically investigates the performance of Elastic Machine Learning, an industrial, unsupervised anomaly detection tool, in the identification of fraudulent behavior in banking transactions. Using AI-generated datasets that were designed to simulate realistic banking environments,...

Nowadays, the European economy faces significant global challenges that threaten the continuity of economic growth, especially in the German manufacturing sector, which is under strain from financial turmoil, resulting in numerous layoffs and firm closures. In this respect, FinTech significantly...

This study examines the economic returns to education in the U.S. using 2024 CPS data and compares Ordinary Least Squares (OLS) regression with a Double Machine Learning (DML) framework incorporating models such as random forests, boosted trees, lasso, GAMs, and neural networks (MLP). Results...

This paper presents a novel method for estimating treatment effects in cases where prior knowledge of the exogeneity of the treatment variable is limited. We employ a machine learning technique, double selection via Lasso, to identify a robust set of control variables without requiring prior...

The continued digitalisation of banking systems has raised a demand for more reliable data-based decision-making, in particular when referring to financial forecasts as covered by e-banking applications. This research also investigates the usage of AI-based decision-making systems to facilitate...

This study forecasts inbound tourism demand for the United Kingdom, using monthly data from February 1989 to February 2020. In the empirical analysis, we evaluate and compare the performance of five machine learning models (decision trees, random forests, XGBoost, and support vector regression...

Bankruptcy prediction is currently a widely researched topic, as it typically results from a chain of negative events. Logistic Regression (LR) is one of the standard prediction tools; however, with advances in technology, machine learning (ML) methods are gaining prominence and demonstrating...

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...

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...

Integrating administrative data from the civil registry, social security, and national censuses, we provide novel evidence on intergenerational income mobility and equality of opportunity among 514,890 formal workers in Ecuador. Our results show substantial intergenerational mobility (rank-rank...

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

This paper provides an introduction to Double/Debiased Machine Learning (DML). DML is a general approach to performing inference about a target parameter in the presence of nuisance functions: objects that are needed to identify the target parameter but are not of primary interest. Nuisance...

University dropout is costly, making it a policy priority to identify factors that predict dropout. Using a survey experiment with incoming first-year students linked to long-run administrative outcomes, we assess which information improves dropout prediction beyond standard university records....

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...

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...

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...

Integrating administrative data from the civil registry, social security, and national censuses, we provide novel evidence on intergenerational income mobility and equality of opportunity among 514,890 formal workers in Ecuador. Our results show substantial intergenerational mobility (rank-rank...

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,...

CONTEXT: Numerous studies underscore the importance of temporal and spatial diversification in cropping systems for enhancing agricultural resilience under growing uncertainty. OBJECTIVE: Although positive effects of crop diversification have been widely reported, the factors influencing...

This study examines the economic returns to education in the U.S. using 2024 CPS data and compares Ordinary Least Squares (OLS) regression with a Double Machine Learning (DML) framework incorporating models such as random forests, boosted trees, lasso, GAMs, and neural networks (MLP). Results...

We benchmark the performance of widely used long short-term memory (LSTM) models in predicting standardized implied volatility (IV) of equity options against a range of alternative time series models. We forecast option prices over the period 2018-2023 and find universal models that are trained...

We develop a novel sentiment measure from survey forecasts that captures the component of beliefs arising from the systematic misaggregation of public information relative to a machine benchmark based on the same information set. We extend this sentiment measure historically for a panel of 78...

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...

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...

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...

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 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...