Search Results - subject:"Deep Learning"

Demand estimation with text and image data

Compiani, Giovanni; Morozov, Ilya; Seiler, Stephan - 2026

-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed …

Persistent link: https://www.econbiz.de/10015615907

An overview of short-term wind power forecasting : multi-scale decomposition and multi-model deep learning fusion

Mei, Yan; Che, Jinxing; Sun, Qian; Dong, Wei - In: Energy strategy reviews 63 (2026), pp. 1-18

methodologies have evolved from traditional statistical models to deep learning architectures. A dominant paradigm in the field has …

Persistent link: https://www.econbiz.de/10015605194

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

Kanzola, Anna-Maria; Papaioannou, Konstantina; … - In: Journal of innovation & knowledge : JIK 11 (2026), pp. 1-9

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/10015607728

Deep hybrid CNN-LSTM-GRU model for a financial risk early warning system

Muhammad Ali Chohan; Li, Teng; Abrar, Mohammad; … - In: Risks : open access journal 14 (2026) 1, pp. 1-20

emerging economies such as China. This study develops a hybrid deep learning model integrating Convolutional Neural Networks …

Persistent link: https://www.econbiz.de/10015611314

Credit risk assessment with stacked machine learning

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

Persistent link: https://www.econbiz.de/10015562268

Merger and acquisition prediction based on deep learning with attention mechanism

Wan, Liangyong; Zheng, Yongjia; Xu, Xiaoying; Wang, Rui - In: China journal of accounting research : CJAR 19 (2026) 1, pp. 1-20

mergers and acquisitions (M&A). The model extends existing deep learning frameworks by incorporating M&A-specific features … forecasting M&A outcomes, achieving a 29.2% improvement in predictive accuracy over conventional deep learning methods. This study …

Persistent link: https://www.econbiz.de/10015596393

Generating synthetic stock return distributions with diffusion models

Fukunishi, Yosuke; Qiu, Haorong; Takahashi, Akihiko - 2026

Persistent link: https://www.econbiz.de/10015641268

Accounting scripts and the politics of compliance : understanding accountants’ roles in anti-money laundering through sentiment and script theory

Lokanan, Mark E. - In: Journal of economic criminology 11 (2026), pp. 1-15

combination of machine learning (ML) and deep learning (DL) algorithms, along with natural language processing techniques, the …

Persistent link: https://www.econbiz.de/10015643939

Aligned explanations in neural networks

Lobet, Corentin; Chiaromonte, Francesca - 2026

: by reconciling the predictive power of deep learning with the interpretability of linear models, PiNets provide a …

Persistent link: https://www.econbiz.de/10015644225

A novel AI-based trading framework for futures markets : evidence from the MTX case study

Hsieh, Yu-Heng; Lai, Chiung-Han; Yuan, Shyan-Ming - In: International Journal of Financial Studies : open … 14 (2026) 3, pp. 1-22

This study develops a novel AI-based trading framework designed to consistently generate profits across cyclical bullish and bearish futures markets. Unlike conventional strategies that rely on static rules or a single predictive model, the proposed framework introduces a dual-agent deep...

Persistent link: https://www.econbiz.de/10015644217