Search Results - subject_exact:"Neuronale Netze"

Comparing GDP growth rates between countries with high and low renewable energy usage based on neural networks

Abdelsamiea, Abdelsamiea Tahsin; Abd El-Aal, Mohamed F. - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015616954

A computational approach to cognitive architecture with adaptive networks and entropy-driven dynamics

Arbaoui, Billel - 2026

This study presents a computational approach to cognitive architecture that integrates adaptive networks with entropy-driven dynamics to model the evolution and stabilization of mental states. The framework combines temporal-causal network modeling with entropy-based mechanisms inspired by...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015654358

Natural hazards and financial activity : evidence from solar storms impact on BTC Mining

Michaēlidēs, Panagiōtēs G.; Prelorentzos, … - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015609729

Large and deep factor models

Kelly, Bryan T.; Kuznetsov, Boris; Malamud, Semyon; Xu, … - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015609789

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Large (and deep) factor models

Kelly, Bryan T.; Kuznetsov, Boris; Malamud, Semyon; Xu, … - 2024

Book / Working Paper

Self-driving neural networks for term structure modeling

Kooiker, Sicco; Brummelen, Janneke van; Schaumburg, Julia; … - 2026

We propose a factor model with time-varying loadings for term structure modeling and fore casting. While maintaining the interpretation of the factors as level, slope, and curvature through explicit identification restrictions, we allow the loadings to take flexible shapes by specifying them as...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015611785

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://ebvufind01.dmz1.zbw.eu/10015611909

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://ebvufind01.dmz1.zbw.eu/10015615288

Measuring economic outlook in the news

Beck, Elliot; Eckert, Franziska; Kühne, Linus; … - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015615555

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://ebvufind01.dmz1.zbw.eu/10015592502

The impact of countries' artificial intelligence readiness levels on new business establishment : controlling for energy consumption, economic growth, inflation, and population

Zharkimbekova, Kamila S.; Niyazbekova, Roza K.; … - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015620788

Forecasting economic growth with traditional methods and a simple neural network model

Li, Shujie; Feng, Yuanhua - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015627073

Mood in the market : forecasting IPO activity with music sentiment and LSTM

Ding, Qinxu; Guan, Chong; Yu, Yinghui - 2026

We examine whether aggregate "music mood" derived from globally popular songs can help forecast primary equity issuance. We build a Friday-anchored weekly panel that merges SEC EDGAR counts of priced Initial Public Offerings (IPOs) with features from the Spotify Daily Top 200 (audio descriptors...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015628658

Critical regimes of systemic risk : flow network cascades in the U.S. banking system

Montañez Jacquez, Samuel; Quezada Téllez, Luis Alberto; … - 2026

Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015639141

Graph attention networks in exchange rate forecasting

Landmesser-Rusek, Joanna; Orłowski, Arkadiusz - 2026

Exchange rate forecasting is an important issue in financial market analysis. Currency rates form a dynamic network of connections that can be efficiently modeled using graph neural networks (GNNs). The key mechanism of GNNs is the message passing between nodes, allowing for better modeling of...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015640548

A Bayesian learning approach for predictive resilience in engineer-to-order supply chains

Alaoua, Aicha; Karim, Mohammed - 2026

Accurate supplier lead time prediction is critical for maintaining resilience in Engineer-to-Order (EtO) supply chains, characterized by high customization and uncertainty. This study develops a simulation-based predictive framework combining log-normal sensitivity analysis, Internet of Things...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015640886

Fine-Scale Spatial Disaggregation of Statistical Data via Graph Neural Networks

Lee, Kamwoo; Blankespoor, Brian; Newhouse, David - 2026

Fine-grained spatial data are critical for informed decision-making in domains ranging from economic planning to environmental management. However, many statistics are only available for coarse administrative units, necessitating techniques for fine-scale spatial disaggregation. This paper...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015641144

Exploring market efficiency with GRU-D neural networks : evidence from global stock markets

Ben Jbara, Abdelhamid; Rabah Gana, Marjene; Dakhlaoui, Mejda - 2026

This study revisits the Efficient Markets Hypothesis by employing a GRU-D neural network to predict stock return distributions across global equity markets, accounting for missing and irregular data. It examines whether stock returns exhibit statistically significant departures from purely...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015643213

Aligned explanations in neural networks

Lobet, Corentin; Chiaromonte, Francesca - 2026

As artificial intelligence increasingly drives critical decisions, the ability to genuinely explain how neural networks make predictions is essential for trust. Yet, most current explanation methods offer post-hoc rationalizations rather than guaranteeing a true reflection of the model's...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015644225

Predicting shock propagation and uncovering heterogeneity with graph neural networks

Arata, Yoshiyuki - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015655641

Siamese networks for AI-powered automated banknote quality control

Gentile, Salvatore; Luciani, Andrea; Marchetti, Sabina; … - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015655714

Sequential solution for DSGE models with deep neural networks

Ferrari, Massimo Minesso; Frenzel, Carla - 2026

This paper develops a sequential deep learning algorithm for solving dynamic stochastic general equilibrium (DSGE) models. The algorithm trains a deep neural network to approximate the model's policy functions across four progressive phases: steady-state anchoring, exploration around the steady...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015650845

Explainable neural algorithms for corporate sustainability forecasting : a layered predictive model anchored in executive awareness, green finance, and digital innovation

Ibrahim, Yara; Moubarak, Hosam; Badawy, Hebatallah Abd … - 2026

This study investigates how artificial intelligence (AI) capability drives sustainable performance through the mediating role of Digital Green Innovation (DGI). Grounded in the Resource-Based and Natural Resource-Based Views, survey data from 321 organizations are analyzed using a multi-method...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015651766

Predicting stock market trends using convolutional neural networks : a deep learning approach

Özkul, Ege - 2026

Technical analysis aims to predict stock returns based on price and volume patterns and has seen growing adoption of machine learning methods. However, most approaches rely on hand-crafted features. This paper investigates whether deep learning applied to stock chart images can predict future...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015652027

An expectile-based neural network approach for mixed-frequency economic forecasting

Saputra, Wisnowan Hendy; Prastyo, Dedy Dwi; … - 2026

Timely and accurate forecasting of Gross Domestic Product (GDP) is critical for economic policymaking, yet it is complicated by the need to integrate high-frequency financial indicators with low-frequency macroeconomic data and to capture complex nonlinear dynamics. This study introduces a novel...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015652276

Neural demand estimation with habit formation and rationality constraints

Grzeskiewicz, Marta - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015652614

AI-driven bankruptcy prediction in manufacturing SMEs : comparing machine learning techniques with logistic regression

Letkovský, Stanislav; Jenčová, Sylvia; … - 2026

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...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015634019

Regime-aware conditional neural processes with multi-criteria decision support for operational electricity price forecasting

Das, Abhinav; Schlüter, Stephan; Schneider, Lorenz - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015635119

Graph neural network model for cable tunnel cost prediction under high-dimensional construction data

Fang, Ming; Lu, Handong; Lai, Yifeng - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015635256

Empirical analysis of industry 4.0 determinants in Moroccan supply chains : a neural network approach

2026

This paper examines the factors shaping the adoption of Industry 4.0 (I4.0) technologies in Moroccan supply chains (SCs), with a focus on how digitalization, management practices, strategic planning, and financial resources contribute to SC optimization. The study aims to explore how these...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015636782

Simulation studies on the impact of using demand forecasts based on deep neural networks for inventory replenishment efficiency

Krzyżaniak, Stanisław; Cyplik, Piotr; Bartkowiak, Marcin - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015670479

Hierarchical neural additive models for interpretable demand forecasts

Feddersen, Leif; Cleophas, Catherine - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015668064

Recurrent neural networks for real estate evaluation in the Italian market

Basso, Antonella; Corazza, Marco; Tonon, Lorenzo - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015670866

Neural networks in economics : a selective review

Chen, Xiaohong; Hoderlein, Luis; Lieber, Jonas - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015670982

Benchmark of likelihood-free inference methods based on neural and optimal transport approaches

Aka, Samira; Kratz, Marie; Naveau, Philippe - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015677465

Implementing neural SDEs for data-driven dynamics of the Bitcoin option surface

Shah, Arjun; Schlögl, Erik - 2026

This paper presents a full implementation of data-driven modelling of the dynamics of the options on Bitcoin, using high-frequency data from the Deribit exchange. To this end, we provide a synthesis of methods established in prior papers, namely, the works involving "neural SDE market models,"...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10016059322

A natural copula

Lerner, Peter B. - 2026

Copulas are widely used in financial economics, as well as in other areas of applied mathematics. Yet, there is much arbitrariness in their choice. The author proposes a "natural copula" concept that minimizes the Wasserstein distance between distributions in a space in which both distributions...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10016059558

Hyperparameters over architecture : a controlled comparison of neural networks for aggregate loss reserving

Guo, Qiheng - 2026

We compare neural network architectures for aggregate loss triangle reserving under matched data, training, and evaluation protocols, separating architectural choice from hyperparameter configuration across hundreds of training runs. We compare the GRU Baseline of the DeepTriangle framework...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10016059201

Applications of econometrics and artificial intelligence in oil price prediction : a systematic review study

Razavi, Seyyed Abdollah; Kashani, Leila - 2026

Crude oil price forecasting, one of the global economy's strategic challenges, has always been an attractive topic for researchers, policymakers, and market participants. This study aims to systematically analyze and critically review econometric and artificial intelligence approaches to oil...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015677810

Financial fraud detection based on an explainable multi-layer framework

Xia, Hui; Huang, Yilong; Fang, Shanshan; Wang, Qin; … - 2026

Financial information plays a critical role in decision-making for stakeholders, including investors, regulators, and corporate managers. However, financial data is susceptible to deliberate manipulation, where some firms may distort disclosures to mislead stakeholders and potentially engage in...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10016061254

An enhanced ADMM-based distributed framework for coordinated wind power and electric vehicle scheduling optimization

Yang, Lihao; Zhang, Qi; Zhang, Xin - 2026

The rapid proliferation of electric vehicles and the increasing penetration of wind energy into modern power grids have introduced significant operational challenges, including load volatility, wind curtailment, and real time constraint management in large scale distributed systems. Existing...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015676319

Economic complexity and environmental impact using a neural-network embedded semiparametric mixture of experts model

Skhosana, Sphiwe B.; Olaniran, Abeeb O.; Rad, Najmeh Nakhaei - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015668862

ReLU-based and DNN-based generalized maximum score estimators

Chen, Xiaohong; Gao, Wayne Yuan; Wen, Likang - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015616706

The price of uniformity : welfare costs of one-size-fits-all investment plans under income risk

Rodrigues, Paulo Jorge Maurício; Schotman, Peter C. - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015617924

Predicting default risk bancassurance using GMDH and dce-GMDH neural network models

Jaber, Jamil; Alkhawaldeh, Rami S.; Khatatbeh, Ibrahim N. - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015618739

A transparent house price prediction framework using ensemble learning, genetic algorithm-based tuning, and ANOVA-based feature analysis

Hussain, Mohammed Ibrahim; Munir, Arslan; Mamun, Mohammad; … - 2025

House price prediction is crucial in real estate for informed decision-making. This paper presents an automated prediction system that combines genetic algorithms (GA) for feature optimization and Analysis of Variance (ANOVA) for statistical analysis. We apply and compare five ensemble machine...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015547349

An interpretable 1D-CNN framework for stock price forecasting : a comparative study with LSTM and ARIMA

Ranjan, Pallavi; Itani, Rania; Faccia, Alessio - 2025

Deep learning has transformed numerous areas of data science by achieving outstanding performance in tasks such as image recognition, speech processing, and natural language understanding. Recently, the challenges of financial forecasting-marked by nonlinear dynamics, volatility, and regime...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015547438

Stock market volatility forecasting : exploring the power of deep learning

Vo, Minh T. - 2025

This study provides a comprehensive evaluation of five deep learning (DL) architectures-TiDE, LSTM, DeepAR, TCN, and Transformer-against the extended Heterogeneous Autoregressive (HAR) model for stock market volatility forecasting. Utilizing 22.5 years of high-frequency data from the S&P 500,...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015547446

Leveraging financial interdependencies in emerging markets via graph neural networks

Jessen, Nicolai Bloch - 2025

This study examines the role of interdependencies in forecasting sovereign yield spreads in emerging markets using Graph Neural Networks (GNNs). Sovereign yield spreads reflect economic conditions and investor sentiment, making accurate predictions crucial for investors, policymakers, and...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015654816

Finding money launderers using heterogeneous graph neural networks

Johannessen, Fredrik; Jullum, Martin - 2025

The finance industry depends on effective anti-money laundering (AML) systems to ensure compliance and maintain operational efficiency. However, existing AML systems, which are predominantly rule-based, frequently struggle to detect money laundering accurately. In particular, their inability to...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015654865

Optimizing solar photovoltaic power forecasting via multi-architecture machine learning framework with multiple hyperparameter optimization techniques

Tahir, Muhammad Faizan; Tzes, Anthony; El-Fouly, Tarek H. M. - 2025

The integration of solar photovoltaic systems into power grids has become crucial for sustainable energy development, particularly in extreme climate conditions where accurate power forecasting remains challenging. This study presents a novel multi-architecture machine learning framework with...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015485825