{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:25:55Z","timestamp":1754155555507,"version":"3.41.2"},"reference-count":24,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2024,1,2]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The stock indexes are an important issue for investors, and in this paper good trading strategies will be aimed to be adopted according to the accurate prediction of the stock indexes to chase high returns.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>To avoid the problem of insufficient financial data for daily stock indexes prediction during modeling, a data augmentation method based on time scale transformation (DATT) was introduced. After that, a new deep learning model which combined DATT and NGRU (DATT-nested gated recurrent units (NGRU)) was proposed for stock indexes prediction. The proposed models and their competitive models were used to test the stock indexes prediction and simulated trading in five stock markets of China and the United States.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The experimental results demonstrated that both NGRU and DATT-NGRU outperformed the other recurrent neural network (RNN) models in the daily stock indexes prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>A novel RNN with NGRU and data augmentation is proposed. It uses the nested structure to increase the depth of the deep learning model.<\/jats:p><\/jats:sec>","DOI":"10.1108\/k-04-2022-0629","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T22:46:06Z","timestamp":1665441966000},"page":"58-82","source":"Crossref","is-referenced-by-count":2,"title":["DATT-NGRU: a novel deep learning model with data augmentation for daily stock indexes prediction"],"prefix":"10.1108","volume":"53","author":[{"given":"Yuefeng","family":"Cen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4002-5662","authenticated-orcid":false,"given":"Minglu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Cen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongping","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"issue":"1","key":"key2023122105454536600_ref001","first-page":"1","article-title":"Improving stock price prediction with GAN-based data augmentation","volume":"4","year":"2021","journal-title":"Indonesian Journal of Artificial Intelligence and Data Mining"},{"first-page":"424","article-title":"High-frequency trading strategy based on deep neural networks","year":"2016","key":"key2023122105454536600_ref002"},{"key":"key2023122105454536600_ref003","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.eswa.2018.07.019","article-title":"ModAugNet: a new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module","volume":"113","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"key2023122105454536600_ref004","article-title":"Stock market movement forecast: a systematic review","volume":"156","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"key2023122105454536600_ref005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/jrfm7010001","article-title":"Revisiting the performance of MACD and RSI oscillators","volume":"7","year":"2014","journal-title":"Journal of Risk and Financial Management"},{"key":"key2023122105454536600_ref006","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1002\/jae.3950030404","article-title":"Volatility persistence and stock valuations: some empirical evidence using GARCH","volume":"3","year":"1988","journal-title":"Journal of Applied Econometrics"},{"key":"key2023122105454536600_ref007","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.eswa.2015.07.063","article-title":"An intelligent short term stock trading fuzzy system for assisting investors in portfolio management","volume":"43","year":"2016","journal-title":"Expert Systems with Applications"},{"key":"key2023122105454536600_ref008","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.econmod.2017.10.004","article-title":"Systemic risk in the US: interconnectedness as a circuit breaker","volume":"71","year":"2018","journal-title":"Economic Modelling"},{"issue":"4","key":"key2023122105454536600_ref009","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1002\/isaf.1358","article-title":"Predicting next trading day closing price of Qatar exchange index using technical indicators and artificial neural networks","volume":"21","year":"2014","journal-title":"Intelligent Systems in Accounting, Finance and Management"},{"issue":"8","key":"key2023122105454536600_ref010","doi-asserted-by":"crossref","first-page":"10389","DOI":"10.1016\/j.eswa.2011.02.068","article-title":"Using artificial neural network models in stock market index prediction","volume":"38","year":"2011","journal-title":"Expert Systems with Applications"},{"key":"key2023122105454536600_ref011","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1080\/1540496X.2018.1553163","article-title":"Price limits and asymmetry of price dynamics\u2014high frequency evidence from the Chinese stock market","volume":"56","year":"2020","journal-title":"Emerging Markets Finance and Trade"},{"key":"key2023122105454536600_ref012","article-title":"Applications of deep learning in stock market prediction: recent progress","volume":"184","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"key2023122105454536600_ref013","article-title":"An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms","volume":"541","year":"2020","journal-title":"Physica A: Statistical Mechanics and Its Applications"},{"key":"key2023122105454536600_ref014","article-title":"Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation","volume":"161","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"key2023122105454536600_ref015","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1080\/00036846.2011.631894","article-title":"Technical analysis and the Spanish stock exchange: testing the RSI, MACD, momentum and stochastic rules using Spanish market companies","volume":"45","year":"2013","journal-title":"Applied Economics"},{"key":"key2023122105454536600_ref016","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1002\/fut.3990130304","article-title":"Circuit breakers and stock market volatility","volume":"13","year":"1993","journal-title":"Journal of Futures Markets"},{"key":"key2023122105454536600_ref017","first-page":"1","article-title":"A survey on image data augmentation for deep learning","volume":"6","year":"2019","journal-title":"Journal of Big Data"},{"key":"key2023122105454536600_ref018","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.inffus.2020.08.019","article-title":"Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions","volume":"65","year":"2021","journal-title":"Information Fusion"},{"first-page":"2160","article-title":"Stock market trend prediction using ARIMA-based neural networks","year":"1996","key":"key2023122105454536600_ref019"},{"key":"key2023122105454536600_ref020","article-title":"NGCU: a new RNN model for time-series data prediction","volume":"27","year":"2022","journal-title":"Big Data Research"},{"key":"key2023122105454536600_ref021","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.physa.2016.08.048","article-title":"Post-hit dynamics of price limit hits in the Chinese stock markets","volume":"465","year":"2017","journal-title":"Physica A: Statistical Mechanics and Its Applications"},{"key":"key2023122105454536600_ref022","first-page":"60","article-title":"Application of MEA-LSTM neural network in stock balance prediction[C]","volume-title":"The International Symposium on Computer Science, Digital Economy and Intelligent Systems","year":"2022"},{"first-page":"903","article-title":"Data augmentation based stock trend prediction using self-organising map","year":"2017","key":"key2023122105454536600_ref023"},{"key":"key2023122105454536600_ref024","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.1109\/TSP.2019.2907260","article-title":"Deeplob: deep convolutional neural networks for limit order books","volume":"67","year":"2019","journal-title":"IEEE Transactions on Signal Processing"}],"container-title":["Kybernetes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-04-2022-0629\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-04-2022-0629\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:47:59Z","timestamp":1753393679000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/k\/article\/53\/1\/58-82\/1236027"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,11]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,10,11]]},"published-print":{"date-parts":[[2024,1,2]]}},"alternative-id":["10.1108\/K-04-2022-0629"],"URL":"https:\/\/doi.org\/10.1108\/k-04-2022-0629","relation":{},"ISSN":["0368-492X"],"issn-type":[{"type":"print","value":"0368-492X"}],"subject":[],"published":{"date-parts":[[2022,10,11]]}}}