Search Results - subject_exact:"Machine learning"

Machine learning style rotation – evidence from the Johannesburg Stock Exchange

Page, Daniel; McClelland, David; Auret, Christo - 2024

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015426112

Prognose der Abgabequote von Einkommensteuererklärungen bei Rentnerinnen und Rentnern: Machbarkeitsstudie zur Beschleunigung der Veröffentlichung von statistischen Ergebnissen mittels Machine Learning

Moritz, Steffen; Wiynck, Frederik; Wiebels, Johannes - 2024

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014526875

Prognose der Abgabequote von Einkommensteuererklärungen bei Rentnerinnen und Rentnern : Machbarkeitsstudie zur Beschleunigung der Veröffentlichung von statistischen Ergebnissen mittels Machine Learning

Moritz, Steffen; Wiynck, Frederik; Wiebels, Johannes - 2024

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014526618

Machine learning style rotation : evidence from the Johannesburg Stock Exchange

Page, Daniel; McClelland, David E.; Auret, C. - 2024

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),...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015326694

Earning While Learning: How to Run Batched Bandit Experiments

Kemper, Jan; Rostam-Afschar, Davud - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015608049

Machine learning mutual fund flows

Fausch, Jürg; Frigg, Moreno; Ruenzi, Stefan; Weigert, … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015608830

Hard to process : atypical firms and the cross-section of expected stock returns

Weibels, Sebastian - 2026 - Current version: January 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605627

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Hard to process: Atypical firms and the cross-section of expected stock returns

Weibels, Sebastian - 2026

Book / Working Paper

Cropping history, agronomic rules, and commodity prices shape crop rotations across Central Europe

Palka, Marlene; Nendel, Claas; Weiß, Lucas; Schiller, … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015609076

Mapping mobility and opportunity: How place, gender, and ethnicity shape economic outcomes in Ecuador

Brunori, Paolo; del Pozo, Diego; Jara, H. Xavier; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015619123

Quantifying Minsky cycles

Ristolainen, Kim - 2026

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,...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015595844

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Quantifying Minsky cycles

Ristolainen, Kim - 2026

Book / Working Paper

Quantifying Minsky cycles

Ristolainen, Kim - 2026

Book / Working Paper

Quantifying Minsky cycles

Ristolainen, Kim - 2026

Book / Working Paper

Temporal and spatial crop diversity are related and affected by farm and landscape configurations

Schiller, Josepha; Jänicke, Clemens; Reckling, Moritz; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015619710

Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods

Helal, Al Mansor; Hiraki, Ryotaro; Patrinos, Harry Anthony - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015625136

Predicting option prices from their price history via machine learning

Fritzsch, Simon; Irresberger, Felix; Weiß, Gregor - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015626831

Earning while learning : how to run batched bandit experiments

Kemper, Jan; Rostam-Afschar, Davud - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015615820

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Earning while learning : how to run batched bandit experiments

Kemper, Jan; Rostam-Afschar, Davud - 2026

Book / Working Paper

An introduction to double/debiased machine learning

Ahrens, Achim; Chernozhukov, Victor; Hansen, Christian; … - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015616936

Predicting university dropouts : evidence on the value of student expectations and motivation

Epper, Thomas; Ibsen, Kristoffer Holst Kroustrup; Koch, … - 2026

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....

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015616999

Energy shocks and coffee market resilience under a machine learning framework with the SDI+Index

Suárez-Rodríguez, Carlos Hernán; Manotas-Duque, … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015617246

A bibliometric analysis and research landscape of machine learning applications in greenhouse gas emissions

Ajibade, Samuel-Soma M.; Adediran, Anthonia Oluwatosin; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015617389

Machine learning for estimating catastrophic health spending in disaster-affected, data-scarce settings

Himaz, Rozana; Salmanidou, Dimitra; Ghaffarian, Saman - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015618216

An analytical framework for causal decision-making in international trade

Vanegas-López, Juan Gabriel; Pérez-Aguirre, Carlos Andrés - 2026

In international trade, selecting an Incoterm is a critical decision impacting costs and risks. Previous quantitative studies relied on traditional econometric models imposing strict linearity, limiting causal interpretation. This study addresses this gap by applying a causal inference framework...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015654282

An adaptive graph-based method for structured learning and decision analysis

Samanta, Sovan; Allahviranloo, Tofigh; Mrsic, Leo; … - 2026

Many decision-support systems operate as networks of interacting units (e.g., hospital departments, transportation hubs, or organizational teams). In such systems, different units often use different data representations and model parameters, so direct comparison of local model states across...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015654360

Credit risk assessment with stacked machine learning

Columba, Francesco; Cugliari, Manuel; Di Virgilio, Stefano - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015562268

From shipments to supply chains : mining input-output links from firm-level trade flows

Mau, Karsten; Vicencio, Antonio; Xu, Mingzhi; Zheng, Yawen - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015562828

Deep learning projects jurisdiction of new and proposed Clean Water Act regulation

Greenhill, Simon; Walker, Brant J.; Shapiro, Joseph S. - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015608751

Can satellites predict oil demand?

Bricongne, Jean-Charles; Macalos, Joao; Meunier, Baptiste; … - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015610289

Legal dimensions of global AML risk assessment : a machine learning approach

Kovalchuk, Olha; Shevchuk, Ruslan; Banakh, Serhiy; … - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015611262

A framework for interpreting machine learning models in bond default risk prediction using LIME and SHAP

Zhang, Yan; Chen, Lin; Tian, YiXiang - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015611531

Pathways of climate variability, agricultural performance, and conflict : a machine learning approach to complex dependencies

Gattone, Tulia; Romano, Donato; Tiberti, Luca - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015611909

Open dumps and the global trade in garbage

Gordon, Matthew; Papp, Anna - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015614028

Lobbying for regulations : when big business says yes

Macedoni, Luca; Weinberger, Ariel - 2026

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....

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015614393

Exploring the potential of machine learning to reduce administrative burden in participatory budgeting : a case study of Seoul

Shin, Bokyong - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015615043

Global estimates of opportunity and mobility : a database

Ferreira, Francisco H. G.; Peragine, Vitorocco; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015580367

Show one more version 1

Global estimates of opportunity and mobility : a database

Ferreira, Francisco H. G.; Peragine, Vitorocco; … - 2026

Book / Working Paper

Randomized algorithms and neural networks for communication-free multiagent singleton set cover

He, Guanchu; Hill, Colton; Seaton, Joshua H.; Brown, … - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015615288

Mapping mobility and opportunity : how place, gender, and ethnicity shape economic outcomes in Ecuador

Brunori, Paolo; Pozo, Diego del; Jara, H. Xavier; … - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015615879

Forecasting stock market behavior in BRICS economies using artificial neural machine learning models

Panigrahi, Shrikant; Kukreja, Gagan; Kumaraswamy, Sumathi - 2026

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 -...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015592502

Hybrid modelling using simulation and machine learning in healthcare

Ahmadi, Ali; Fakhimi, Masoud; Magnusson, Carin - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015552381

Towards an unsupervised learning scheme for efficiently solving parameterized mixed-integer programs

Qu, Shiyuan; Dong, Fenglian; Wei, Zhiwei; Shang, Chao - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015552389

The Financial Lobster Bias

Reyes Marín, Óscar De los; Paz Gil, Iria; … - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015591400

A systematic review on AI-driven computational strategies for sustainable power systems

Ahmed, Ijaz; Rehan, Muhammad; Alqahtani, Mohammed; … - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605325

Do anecdotes matter? : exploring the Beige Book through textual analysis from 1970 to 2025

Du, Shengwu; Haberkorn, Flora; Kitschelt, Isabel; Lee, … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605570

Machine learning mutual fund flows

Fausch, Jürg; Frigg, Moreno; Ruenzi, Stefan; Weigert, … - 2026 - This draft: May 03, 2025

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605608

Dynamic investment in teamwork skill : theory and experimental evidence

Gill, David; Prowse, Victoria; Reddinger, J. Lucas - 2026 - This version: February 9, 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015606350

Identifying weak signals in the labor market : a machine learning approach for strategic policymaking

Kanzola, Anna-Maria; Papaioannou, Konstantina; … - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015607728

Optimal audit targeting with machine learning : evidence from Pakistan

Lacoste, Nicholas; Farooq, Zehra - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015619681

Forecasting out-of-time credit scoring model risk

Yoshida Jr., Valter T.; Schiozer, Rafael Felipe; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015625453

Returns to education in the United States : a comparison of OLS and Double Machine Learning methods

Helal, Al Mansor; Hiraki, Ryotaro; Patrinos, Harry Anthony - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015626592

Clustering-based column generation and heuristic methods for the container loading problem with practical constraints : a case study

Tekil-Ergün, Sezgi; Çebi, Ferhan - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015627288

In data we trust? : emerging policy and supervisory approaches to AI data use in financial services

Crisanto, Juan Carlos; Currat, Adrien; Ehrentraud, Johannes - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015628198

Detecting cyber fraud in banking transactions via machine learning techniques : implications for financial stability

Konsta, Lamprini; Dimitriou, Dimitrios; Papathanasiou, … - 2026

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,...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015628409

Hybrid machine learning-econometric framework for financial distress scoring : evidence from German manufacturing firms

Farag, Karim; Ali, Loubna; Hamada, Mohamed Ahmed - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015628559