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A new neural network approach for predicting the volatility of stock market

Koo, Eunho; Kim, Geonwoo - In: Computational economics 61 (2023) 4, pp. 1665-1679

Persistent link: https://www.econbiz.de/10014327101

An application of machine learning to logistics performance prediction : an economics attribute-based of collective instance

Suriyan Jomthanachai; Wong, Wai Peng; Khai Wah Khaw - In: Computational economics 63 (2024) 2, pp. 741-792

Persistent link: https://www.econbiz.de/10014472556

Modeling duration of building renovation projects

Lawal, Haruna Sa'idu; Ahmadu, Hassan Adaviriku; … - In: Journal of financial management of property and construction 28 (2023) 3, pp. 423-438

Persistent link: https://www.econbiz.de/10014494723

A high-frequency trading volume prediction model using neural networks

Xu, Xiaojie; Zhang, Yun - In: Decision analytics journal 7 (2023), pp. 1-8

considered to tackle the prediction problem. Several questions are pursued, including whether the trading volume could be … relatively simple model based upon ten hidden neurons and thirty lags leads to stable and accurate prediction results based on …

Persistent link: https://www.econbiz.de/10014497231

Stock market prediction, COVID-19 pandemic and neural networks : an SCG algorithm application

Goel, Himanshu; Som, Bhupender Kumar - In: Economia : revista da ANPEC 24 (2023) 1, pp. 134-146

Purpose This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011-February 2020) and during the COVID-19 (March 2020-June...

Persistent link: https://www.econbiz.de/10014418102

Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Li, Bo; Liao, Mengjie; Yuan, Junjing; Zhang, Jian - In: Journal of retailing and consumer services 75 (2023), pp. 1-12

Persistent link: https://www.econbiz.de/10014372532

Unveiling the diverse efficacy of artificial neural networks and logistic regression : a comparative analysis in predicting financial distress

Sabek, Amine - In: Croatian review of economic, business and social … 9 (2023) 1, pp. 16-32

The prediction of financial distress has emerged as a significant concern over a prolonged period spanning more than …

Persistent link: https://www.econbiz.de/10014372938

Stock price crashes in China : an artificial neural network approach

Wang, Le; Zou, Liping; Wu, Ji - In: Pacific accounting review 35 (2023) 4, pp. 645-669

Persistent link: https://www.econbiz.de/10014429780

CDS risk premia forecasting with multi-featured deep RNNs : an application on BR[I]CS countries

Kutuk, Yasin - In: Borsa Istanbul Review 23 (2023) 6, pp. 1380-1398

Using state-of-the-art recurrent neural network architectures, this study attempts to predict credit default swap risk premia for BR[I]CS countries as accurately as possible. In the time series setting, these recurrent neural networks are ELMAN, NARX, GRU, and LSTM RNNs, considering local and...

Persistent link: https://www.econbiz.de/10014447473

Product backorder prediction using deep neural network on imbalanced data

Shajalal, Md; Hájek, Petr; Abedin, Mohammad Zoynul - In: International journal of production research 61 (2023) 1, pp. 302-319

Persistent link: https://www.econbiz.de/10013530905