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Deep speech embeddings of earning calls predict future stock returns

Goeij, Peter de; Liu, Zihao; Postma, Eric - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015417094

Large (and deep) factor models

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

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014483267

Transformers and tradition : using generative AI and Deep Learning for financial markets prediction

Wade, Toby J. - 2024

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015190353

Demand estimation with text and image data

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

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015615907

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

Özkul, Ege - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015654995

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

Özkul, Ege - 2026

Article

Runtime-sensitive learned operator selection in ALNS : testing improvements to adaptive operator selection while optimizing runtime

Dudel, Christopher - 2026

We propose and test two variations of the Adaptive Large Neighborhood Search (ALNS) meta-heuristic: First, we add time sensitivity to the operator selection scheme to optimize the ALNS for both solution quality and runtime. We reward comparatively slow operators with reduced rewards for finding...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015652053

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Runtime-sensitive learned operator selection in ALNS: Testing improvements to adaptive operator selection while optimizing runtime

Dudel, Christopher - 2026

Article

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

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

Muhammad Ali Chohan; Li, Teng; Abrar, Mohammad; … - 2026

Financial risk early warning systems are essential for proactive risk management in volatile markets, particularly for emerging economies such as China. This study develops a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015611314

Demand estimation with text and image data

Compiani, Giovanni; Morozov, Ilya; Seiler, Stephan - 2026 - Original Version: October 2024, This Version: March 2026

We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015614346

Show one more version 1

Demand estimation with text and image data

Compiani, Giovanni; Morozov, Ilya; Seiler, Stephan - 2025 - Original version: October 2024, this version: November 2025

Edition: Original version: October 2024, this version: November 2025

Book / Working Paper

Merger and acquisition prediction based on deep learning with attention mechanism

Wan, Liangyong; Zheng, Yongjia; Xu, Xiaoying; Wang, Rui - 2026

This study proposes a novel attention-based deep neural network (AttDNN) model specifically designed for predicting mergers and acquisitions (M&A). The model extends existing deep learning frameworks by incorporating M&A-specific features, regularization layers, and an attention mechanism that...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015596393

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 - 2026

Accurate wind power forecasting is crucial for the effective scheduling and scientific management of wind energy, enhancing the safety and reliability of power grids. To handle the inherent intermittency of wind energy, forecasting methodologies have evolved from traditional statistical models...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605194

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

How effective is mamba-augmented transformer for stock market price forecasting?

Shuvo, Md Shahria Sarker; Adib, Awsaf Tausif; Emon, Md … - 2026

Stock price forecasting remains challenging due to the non-linear, noisy, and non-stationary nature of financial time series. Although LSTMs and Transformer-based models have improved sequential modeling, their ability to scale efficiently to long financial sequences remains limited. Recently,...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015628622

An analytics-driven hybrid method for multi-item demand forecasting in supply chains

Lingkon, Md. Limonur Rahman; Hossain, Md. Sanowar; … - 2026

This study proposes a new deep learning (DL)-based approach for multi-item demand forecasting in multi-wave distribution networks. In modern merchandising supply systems, traditional forecasting techniques such as moving averages and autoregressive integrated moving average (ARIMA) models are...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015640893

Generating synthetic stock return distributions with diffusion models

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

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015641268

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

Lokanan, Mark E. - 2026

This study explores the role of accountants in anti-money laundering (AML) compliance within Canada's non-banking financial institutions, specifically focusing on the real estate, luxury vehicles, and gaming sectors. By utilizing a unique combination of machine learning (ML) and deep learning...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015643939

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

Hsieh, Yu-Heng; Lai, Chiung-Han; Yuan, Shyan-Ming - 2026

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, ebvufind01.dmz1.zbw.eu/10015644217

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://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015644225

Dismantling binary opposition in fraud detection : a fuzzy deep learning framework for imbalanced transaction data

Essa, Reham M.; El-Kassrawy, Yasser Ahmed; Alaya, Amer; … - 2026

In the context of behavioral finance, detecting credit card fraud remains a critical challenge, particularly when dealing with highly imbalanced datasets and ambiguous transaction patterns. This complexity highlights the limitations of traditional fraud detection models, which rely on a rigid...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015656143

Modelling and diagnosis method of power transformer sound characteristics by integrating sparse representation and deep autoencoder network

Li, Dong; Liu, Kuo; Guo, Congcong; Gong, Qingqing - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015635232

The high-quality development of China's green energy economy for promotion of digital finance under deep learning technology

Li, Ximeng; Sun, Qiaomeng - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015635234

Wavelet-enhanced multimodel framework for stock market forecasting : a comprehensive analysis across market regimes

Okşak, Yüksel; Büyükkör, Yasin; Sarıtaş, Tufan - 2026

In this study, we develop a hybrid forecasting framework that integrates discrete wavelet transform with multiple machine and deep learning architectures to address nonlinearity and regime-dependent dynamics in financial markets. Log-return series using daily data from the BIST 100, S&P 500, and...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015664858

Deep learning models for economic research

Dudek, Andrzej - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015582419

AI and data engineering for healthcare : real-world applications and case studies

2026 - First edition

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015582842

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/10015626276

Hidden value : provenance as a source for economic and social history

Rother, Lynn; Mariani, Fabio; Koss, Max - 2023

Building on the extensive production of provenance data recently, this article explains how we can expand the purview of computational analysis in humanistic and social sciences by exploring how digital methods can be applied to provenances. Provenances document chains of events of ownership and...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014292980

Three theories of natural rate dynamics

Nuño, Galo - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015451180

Show 2 more versions 2

Three Theories of Natural Rate Dynamics

Nuño, Galo - 2025

Book / Working Paper

Three theories of natural rate dynamics

Nuño, Galo - 2025 - This version: April 2025

Edition: This version: April 2025

Book / Working Paper

The environmental, social, and governance emphasis of leading companies in East Asia and Southeast Asia unveiled by deep learning

Li, Chao; Keeley, Alexander; Managi, Shunsuke; … - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015433776

A data-driven deep learning approach incorporating investor sentiment and government interventions to predict post-crash stock return in China's A-share market

Lin, Weiran; Yu, Haijing; Wang, Liugen - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015461214

Operationalization of the construct “Business model of a Bank”: clustering analyses with deep neural networks

Herdt, Manfred; Schulte-Mattler, Hermann - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015484554

PALSYN: a method for synthetic multi-perspective event log generation with differential private guarantees

Kuhn, Martin; Grüger, Joscha; Bergmann, Ralph - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015564736

Artificial Intelligence–Based Forecasting of Oil Prices: Evidence from Neural Network Models

Ficura, Milan; Ibragimov, Rustam; Janda, Karel - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015583445

Simulation of semiconductor wafer dicing induced faults on chips and their application as augmentation method for a deep learning based visual inspection system

Friedrich, Michael; Schlosser, Tobias; Kowerko, Danny - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015604483

Handling data drift in deep learning-based quality monitoring: evaluating calibration methods using the example of friction stir welding

Bauer, Johannes C.; Trattnig, Stephan; Vieltorf, Fabian; … - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015604494

Deep Learning and Econometric Time Series Analysis: An Assessment of Daily Return Forecasts

Berger, Theo - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605214

Sheet metal localization using deep learning and synthetic data

Behnen, Hannes; Boada-Gardenyes, Guillem; Schmitt, Robert H. - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605241

Generative AI in industrial machine vision: a review

Zhou, Hans Aoyang; Wolfschläger, Dominik; Florides, … - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015627053

Revisiting the macroeconomic determinants of non-performing loans with a deep learning technique with causal inference: Evidence from Türkiye

Raşid Bakır, Muhammed; Atalay Çetin, Mümin; … - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015635743

Voice versus text? : an empirical analysis on the impact of physicians' voice messages in online medical consultations

Ma, Yuanhong; Zhang, Alan Ran; Li, Xitong; Mao, Shengjun - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015618148

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://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015547446

Symbolic Modeling for financial asset pricing

Zuo, Xiangwu; Jiang, Anxiao (Andrew) - 2025

Symbolic Regression is a machine learning technique that discovers an unknown function from its samples. Compared to conventional regression techniques (e.g., linear regression, polynomial regression, etc.), Symbolic Regression does not limit the discovered function to specific forms (e.g.,...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015654750

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://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015654816