{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T03:36:52Z","timestamp":1780630612650,"version":"3.54.1"},"reference-count":40,"publisher":"Emerald","issue":"12","license":[{"start":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T00:00:00Z","timestamp":1694476800000},"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":[[2024,12,9]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The authors found a long memory in the stock price index series using modified R\/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A\u00a0de-noising module\u2014Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module\u2014BiGRU model, and (3) an optimization module\u2014Grid Search Cross-validation (GSCV) algorithm.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Social implications<\/jats:title><jats:p>If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic\u2019s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.<\/jats:p><\/jats:sec>","DOI":"10.1108\/k-02-2023-0286","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T21:50:40Z","timestamp":1694469040000},"page":"5905-5931","source":"Crossref","is-referenced-by-count":3,"title":["Stock price index prediction based on SSA-BiGRU-GSCV model from the perspective of long memory"],"prefix":"10.1108","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1017-4496","authenticated-orcid":false,"given":"Zengli","family":"Mao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7816-6660","authenticated-orcid":false,"given":"Chong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2023,9,12]]},"reference":[{"key":"key2024120604514067700_ref001","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04420-6","article-title":"Deep learning-based exchange rate prediction during the COVID-19 pandemic","year":"2021","journal-title":"Annals of Operations Research"},{"key":"key2024120604514067700_ref002","article-title":"COVID-19 and stock returns: evidence from the Markov switching dependence approach","volume":"64","year":"2023","journal-title":"Research in International Business and Finance"},{"key":"key2024120604514067700_ref003","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04452-y","article-title":"Dynamic nonlinear connectedness between the green bonds, clean energy, and stock price: the impact of the COVID-19 pandemic","year":"2022","journal-title":"Annals of Operations Research"},{"key":"key2024120604514067700_ref011","first-page":"7","article-title":"Grid search parametric optimization for FT-NIR quantitative analysis of solid soluble content in strawberry samples","volume":"94","year":"2017","journal-title":"Vibrational Spectroscopy"},{"key":"key2024120604514067700_ref004","first-page":"328","article-title":"Long memory in returns of Slovenian stock market index and major stocks listed on Ljubljana stock exchange","volume":"138","year":"2013","journal-title":"Actual Problems of Economics"},{"key":"key2024120604514067700_ref006","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-04838-6","article-title":"Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales","year":"2022","journal-title":"Annals of Operations Research"},{"issue":"6","key":"key2024120604514067700_ref007","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/0375-9601(89)90630-0","article-title":"Singularity spectrum of generalized energy integrals","volume":"140","year":"1989","journal-title":"Physics Letters A"},{"key":"key2024120604514067700_ref008","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1016\/j.physa.2018.06.092","article-title":"On a class of estimation and test for long memory","volume":"509","year":"2018","journal-title":"Physica A-Statistical Mechanics and Its Applications"},{"issue":"5-6","key":"key2024120604514067700_ref041","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1016\/j.physa.2007.08.061","article-title":"Long-term memory and volatility clustering in high-frequency price changes","volume":"387","year":"2008","journal-title":"Physica A-Statistical Mechanics and its Applications"},{"key":"key2024120604514067700_ref009","doi-asserted-by":"publisher","DOI":"10.1108\/IJCHM-05-2022-0562","article-title":"An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features","year":"2023","journal-title":"International Journal of Contemporary Hospitality Management"},{"issue":"1","key":"key2024120604514067700_ref010","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1002\/for.2616","article-title":"Using social media mining technology to improve stock price forecast accuracy","volume":"39","year":"2020","journal-title":"Journal of Forecasting"},{"key":"key2024120604514067700_ref012","article-title":"CodeGRU: context-aware deep learning with gated recurrent unit for source code modeling","volume":"125","year":"2020","journal-title":"Information and Software Technology"},{"issue":"1","key":"key2024120604514067700_ref013","doi-asserted-by":"crossref","first-page":"29","DOI":"10.21307\/stattrans-2021-002","article-title":"Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach","volume":"22","year":"2021","journal-title":"Statistics in Transition New Series"},{"issue":"8","key":"key2024120604514067700_ref040","first-page":"1431","article-title":"Short-term stock price prediction by supervised learning of rapid volume decrease patterns","volume":"E105D","year":"2022","journal-title":"IEICE Transactions on Information and Systems"},{"issue":"3","key":"key2024120604514067700_ref014","first-page":"1751","article-title":"A graph-based CNN-LSTM stock price prediction algorithm with leading indicators","volume":"29","year":"2021","journal-title":"Multimedia Systems"},{"issue":"7","key":"key2024120604514067700_ref015","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1016\/j.physa.2009.12.004","article-title":"Long memory volatility in Chinese stock markets","volume":"389","year":"2010","journal-title":"Physica A-Statistical Mechanics and Its Applications"},{"issue":"2","key":"key2024120604514067700_ref016","first-page":"467","article-title":"Investigation of fractal market hypothesis in emerging markets: evidence from the MINT stock markets","volume":"13","year":"2023","journal-title":"Organizations and Markets in Emerging Economies"},{"issue":"2","key":"key2024120604514067700_ref017","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.32604\/iasc.2022.024311","article-title":"Stock price prediction using optimal network based Twitter sentiment analysis","volume":"33","year":"2022","journal-title":"Intelligent Automation and Soft Computing"},{"key":"key2024120604514067700_ref018","article-title":"The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets","volume":"151","year":"2021","journal-title":"Chaos Solitons and Fractals"},{"key":"key2024120604514067700_ref019","article-title":"An improved elman network for stock price prediction service","volume":"2020","year":"2020","journal-title":"Security and Communication Networks"},{"issue":"8","key":"key2024120604514067700_ref020","doi-asserted-by":"crossref","first-page":"181","DOI":"10.3390\/jrfm13080181","article-title":"Comparison of financial models for stock price prediction","volume":"13","year":"2020","journal-title":"Journal of Risk and Financial Management"},{"key":"key2024120604514067700_ref021","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1016\/j.csda.2013.04.009","article-title":"Basic singular spectrum analysis and forecasting with R","volume":"71","year":"2014","journal-title":"Computational Statistics and Data Analysis"},{"key":"key2024120604514067700_ref022","doi-asserted-by":"publisher","DOI":"10.1080\/15228916.2023.2172990","article-title":"Network granger causality linkages in Nigeria and developed stock markets: bayesian graphical analysis","year":"2023","journal-title":"Journal of African Business"},{"issue":"1","key":"key2024120604514067700_ref023","article-title":"Sustainable stock market prediction framework using machine learning models","volume":"14","year":"2023","journal-title":"International Journal of Software Science and Computational Intelligence-Ijssci"},{"issue":"2","key":"key2024120604514067700_ref024","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.2991\/ijcis.d.191219.001","article-title":"A statistical approach to provide explainable convolutional neural network parameter optimization","volume":"12","year":"2019","journal-title":"International Journal of Computational Intelligence Systems"},{"key":"key2024120604514067700_ref025","first-page":"788","article-title":"Analysis of look back period for stock price prediction with RNN variants: a case study on banking sector of NEPSE","year":"2022"},{"issue":"9","key":"key2024120604514067700_ref026","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.3390\/math8091441","article-title":"Stock price forecasting with deep learning: a comparative study","volume":"8","year":"2020","journal-title":"Mathematics"},{"issue":"5","key":"key2024120604514067700_ref027","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.3390\/app10051597","article-title":"Importance of event binary features in stock price prediction","volume":"10","year":"2020","journal-title":"Applied Sciences-Basel"},{"issue":"3","key":"key2024120604514067700_ref028","first-page":"897","article-title":"A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction","volume":"43","year":"2019","journal-title":"Applied Intelligence"},{"issue":"5","key":"key2024120604514067700_ref029","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3390\/data7050051","article-title":"A hybrid stock price prediction model based on PRE and deep neural network","volume":"7","year":"2022","journal-title":"Data"},{"issue":"1","key":"key2024120604514067700_ref030","first-page":"12","article-title":"Stock price prediction using artificial neural network integrated moving average","volume":"1641","year":"2020","journal-title":"Journal of Physics: Conference Series"},{"issue":"5","key":"key2024120604514067700_ref031","first-page":"1292","article-title":"Stock prices' long memory in China and the United States","volume":"17","year":"2020","journal-title":"International Journal of Emerging Markets"},{"issue":"3","key":"key2024120604514067700_ref005","first-page":"505","article-title":"Modeling daily realized futures volatility with singular spectrum analysis","volume":"312","year":"2002","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"1","key":"key2024120604514067700_ref032","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1142\/S021962201841002X","article-title":"A new approach for stock price analysis and prediction based on SSA and SVM","volume":"18","year":"2019","journal-title":"International Journal of Information Technology and Decision Making"},{"issue":"3","key":"key2024120604514067700_ref034","article-title":"Estimating the memory parameter for potentially non-linear and non-Gaussian time series with wavelets","volume":"38","year":"2022","journal-title":"Inverse Problems"},{"issue":"21","key":"key2024120604514067700_ref035","doi-asserted-by":"crossref","first-page":"13513","DOI":"10.1007\/s00500-021-06122-4","article-title":"Research on a hybrid prediction model for stock price based on long short-term memory and variational mode decomposition","volume":"25","year":"2021","journal-title":"Soft Computing"},{"key":"key2024120604514067700_ref036","first-page":"70","article-title":"Oil price risk evaluation using a novel hybrid model based on time-varying long memory","volume":"81","year":"2018","journal-title":"Journal of Energy Finance and Development"},{"issue":"8","key":"key2024120604514067700_ref038","first-page":"1431","article-title":"Short-term stock price prediction by supervised learning of rapid volume decrease patterns","volume":"E105D","year":"2022","journal-title":"Ieice Transactions on Information and Systems"},{"issue":"5-6","key":"key2024120604514067700_ref039","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1016\/j.physa.2007.08.061","article-title":"Long-term memory and volatility clustering in high-frequency price changes","volume":"387","year":"2008","journal-title":"Physica A-Statistical Mechanics and Its Applications"},{"issue":"11","key":"key2024120604514067700_ref037","doi-asserted-by":"crossref","first-page":"237","DOI":"10.3390\/fi11110237","article-title":"Feature fusion text classification model combining CNN and BiGRU with multi-attention mechanism","volume":"11","year":"2019","journal-title":"Future Internet"}],"container-title":["Kybernetes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-02-2023-0286\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-02-2023-0286\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:47:06Z","timestamp":1753393626000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/k\/article\/53\/12\/5905-5931\/1220108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,12]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,9,12]]},"published-print":{"date-parts":[[2024,12,9]]}},"alternative-id":["10.1108\/K-02-2023-0286"],"URL":"https:\/\/doi.org\/10.1108\/k-02-2023-0286","relation":{},"ISSN":["0368-492X"],"issn-type":[{"value":"0368-492X","type":"print"}],"subject":[],"published":{"date-parts":[[2023,9,12]]}}}