{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T01:28:46Z","timestamp":1785461326512,"version":"3.56.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s10115-025-02475-6","type":"journal-article","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T02:05:42Z","timestamp":1749261942000},"page":"8599-8671","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A novel approach for dynamic portfolio management integrating K-means clustering, mean-variance optimization, and reinforcement learning"],"prefix":"10.1007","volume":"67","author":[{"given":"Zakia","family":"Zouaghia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Kodia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lamjed","family":"Ben\u00a0said","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,7]]},"reference":[{"key":"2475_CR1","doi-asserted-by":"crossref","unstructured":"Zouaghia Z, Aouina ZK, Said LB (2023) Hybrid machine learning model for predicting nasdaq composite index. In: 2023 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1\u20136. IEEE","DOI":"10.1109\/ISNCC58260.2023.10323903"},{"issue":"1","key":"2475_CR2","doi-asserted-by":"crossref","first-page":"612","DOI":"10.18080\/jtde.v12n1.843","volume":"12","author":"Z Zouaghia","year":"2024","unstructured":"Zouaghia Z, Kodia Z, Ben Said L (2024) A novel autocnn model for stock market index prediction. Journal of Telecommunications and the Digital Economy 12(1):612\u2013636","journal-title":"Journal of Telecommunications and the Digital Economy"},{"issue":"1","key":"2475_CR3","doi-asserted-by":"crossref","first-page":"4680140","DOI":"10.1155\/2018\/4680140","volume":"2018","author":"Y Tang","year":"2018","unstructured":"Tang Y, Xiong JJ, Jia Z-Y, Zhang Y-C (2018) Complexities in financial network topological dynamics: Modeling of emerging and developed stock markets. Complexity 2018(1):4680140","journal-title":"Complexity"},{"issue":"Suppl 6","key":"2475_CR4","doi-asserted-by":"crossref","first-page":"14477","DOI":"10.1007\/s10586-018-2316-7","volume":"22","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Liu X, Xiao H, Wu S, Liu Y (2019) A dea-based moea\/d algorithm for portfolio optimization. Cluster Computing 22(Suppl 6):14477\u201314486","journal-title":"Cluster Computing"},{"key":"2475_CR5","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.omega.2014.11.006","volume":"52","author":"W Liu","year":"2015","unstructured":"Liu W, Zhou Z, Liu D, Xiao H (2015) Estimation of portfolio efficiency via dea. Omega 52:107\u2013118","journal-title":"Omega"},{"issue":"1","key":"2475_CR6","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.ejor.2016.05.044","volume":"256","author":"T Bodnar","year":"2017","unstructured":"Bodnar T, Mazur S, Okhrin Y (2017) Bayesian estimation of the global minimum variance portfolio. European Journal of Operational Research 256(1):292\u2013307","journal-title":"European Journal of Operational Research"},{"key":"2475_CR7","volume":"100","author":"W Chen","year":"2021","unstructured":"Chen W, Zhang H, Mehlawat MK, Jia L (2021) Mean-variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing 100:106943","journal-title":"Applied Soft Computing"},{"key":"2475_CR8","unstructured":"Reilly FK (2002) Investment analysis and portfolio management. \u4e2d\u4fe1\u51fa\u7248\u793e"},{"key":"2475_CR9","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s10479-017-2467-6","volume":"266","author":"PJ Kremer","year":"2018","unstructured":"Kremer PJ, Talmaciu A, Paterlini S (2018) Risk minimization in multi-factor portfolios: What is the best strategy? Annals of Operations Research 266:255\u2013291","journal-title":"Annals of Operations Research"},{"key":"2475_CR10","doi-asserted-by":"crossref","unstructured":"Infanger G (2008) Dynamic asset allocation strategies using a stochastic dynamic programming aproach. Handbook of asset and liability management, 199\u2013251","DOI":"10.1016\/B978-044453248-0.50011-3"},{"key":"2475_CR11","unstructured":"Maginn JL, Tuttle DL, McLeavey DW, Pinto JE (2007) Managing investment portfolios: a dynamic process 3"},{"issue":"5","key":"2475_CR12","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1002\/asmb.2535","volume":"36","author":"KK Law","year":"2020","unstructured":"Law KK, Li WK, Yu PL (2020) Evaluation methods for portfolio management. Applied Stochastic Models in Business and Industry 36(5):857\u2013876","journal-title":"Applied Stochastic Models in Business and Industry"},{"key":"2475_CR13","doi-asserted-by":"crossref","first-page":"32595","DOI":"10.1109\/ACCESS.2023.3263260","volume":"11","author":"PK Aithal","year":"2023","unstructured":"Aithal PK, Geetha M, Dinesh U, Savitha B, Menon P (2023) Real-time portfolio management system utilizing machine learning techniques. IEEE access 11:32595\u201332608","journal-title":"IEEE access"},{"issue":"2","key":"2475_CR14","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1287\/opre.7.2.145","volume":"7","author":"MF Osborne","year":"1959","unstructured":"Osborne MF (1959) Brownian motion in the stock market. Operations research 7(2):145\u2013173","journal-title":"Operations research"},{"issue":"1","key":"2475_CR15","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1086\/294849","volume":"39","author":"EF Fama","year":"1966","unstructured":"Fama EF, Blume ME (1966) Filter rules and stock-market trading. The Journal of Business 39(1):226\u2013241","journal-title":"The Journal of Business"},{"issue":"1","key":"2475_CR16","first-page":"59","volume":"7","author":"ME Mangram","year":"2013","unstructured":"Mangram ME (2013) A simplified perspective of the markowitz portfolio theory. Global journal of business research 7(1):59\u201370","journal-title":"Global journal of business research"},{"key":"2475_CR17","doi-asserted-by":"crossref","unstructured":"Connor G, Goldberg LR, Korajczyk RA (2010) Portfolio risk analysis","DOI":"10.1515\/9781400835294"},{"key":"2475_CR18","doi-asserted-by":"crossref","unstructured":"Fabozzi FJ, Kolm PN, Pachamanova DA, Focardi SM (2007) Robust portfolio optimization and management","DOI":"10.1002\/9780470404324.hof003068"},{"key":"2475_CR19","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.113042","volume":"143","author":"W Wang","year":"2020","unstructured":"Wang W, Li W, Zhang N, Liu K (2020) Portfolio formation with preselection using deep learning from long-term financial data. Expert Systems with Applications 143:113042","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"2475_CR20","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/s11036-020-01647-8","volume":"26","author":"F Affonso","year":"2021","unstructured":"Affonso F, Dias TMR, Pinto AL (2021) Financial times series forecasting of clustered stocks. Mobile Networks and Applications 26(1):256\u2013265","journal-title":"Mobile Networks and Applications"},{"key":"2475_CR21","unstructured":"Wang Y (2024) Network representations for multivariate time-series with applications in portfolio optimization and deep learning. PhD thesis, UCL (University College London)"},{"key":"2475_CR22","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.eswa.2018.08.003","volume":"115","author":"FD Paiva","year":"2019","unstructured":"Paiva FD, Cardoso RTN, Hanaoka GP, Duarte WM (2019) Decision-making for financial trading: A fusion approach of machine learning and portfolio selection. Expert Systems with Applications 115:635\u2013655","journal-title":"Expert Systems with Applications"},{"key":"2475_CR23","unstructured":"Gr\u00f6nholm R (2023) Performance of clustering-based stock portfolios: case: Exploratory study of k-means clustering for s &p500 in 2010-2022 data using combinations of selected key figures. LUT University"},{"key":"2475_CR24","doi-asserted-by":"crossref","unstructured":"Indriyanti D, Dhini A (2019) Clustering high-dimensional stock data using data mining approach. In: 2019 16th International Conference on Service Systems and Service Management (ICSSSM), pp. 1\u20135. IEEE","DOI":"10.1109\/ICSSSM.2019.8887724"},{"issue":"2","key":"2475_CR25","doi-asserted-by":"crossref","first-page":"207","DOI":"10.5958\/2321-5763.2020.00032.3","volume":"11","author":"AA Saji","year":"2020","unstructured":"Saji AA, Joseph JJM, Kumar BS (2020) Portfolio construction and testing using cluster analysis. Asian Journal of Management 11(2):207\u2013212","journal-title":"Asian Journal of Management"},{"issue":"4","key":"2475_CR26","first-page":"419","volume":"5","author":"M Zanjirdar","year":"2020","unstructured":"Zanjirdar M (2020) Overview of portfolio optimization models. Advances in mathematical finance and applications 5(4):419\u2013435","journal-title":"Advances in mathematical finance and applications"},{"key":"2475_CR27","doi-asserted-by":"crossref","first-page":"140234","DOI":"10.1109\/ACCESS.2020.3013097","volume":"8","author":"S Lim","year":"2020","unstructured":"Lim S, Kim M-J, Ahn CW (2020) A genetic algorithm (ga) approach to the portfolio design based on market movements and asset valuations. IEEE Access 8:140234\u2013140249","journal-title":"IEEE Access"},{"key":"2475_CR28","unstructured":"Sen J (2022) \u2018a comparative study on the sharpe ratio, sortino ratio, and calmar ratio in portfolio optimization. Dept. Data Sci. Praxis Bus. School Kolkata, India, Tech. Rep"},{"issue":"5","key":"2475_CR29","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1093\/rfs\/hhm075","volume":"22","author":"V DeMiguel","year":"2009","unstructured":"DeMiguel V, Garlappi L, Uppal R (2009) Optimal versus naive diversification: How inefficient is the 1\/n portfolio strategy? The review of Financial studies 22(5):1915\u20131953","journal-title":"The review of Financial studies"},{"key":"2475_CR30","doi-asserted-by":"crossref","unstructured":"Day M-Y, Lin J-T (2019) Artificial intelligence for etf market prediction and portfolio optimization. In: Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1026\u20131033","DOI":"10.1145\/3341161.3344822"},{"key":"2475_CR31","doi-asserted-by":"crossref","unstructured":"Zouaghia Z, Aouina ZK, Said LB (2023) Stock movement prediction based on technical indicators applying hybrid machine learning models. In: 2023 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1\u20134. IEEE","DOI":"10.1109\/ISNCC58260.2023.10323971"},{"key":"2475_CR32","volume":"165","author":"Y Ma","year":"2021","unstructured":"Ma Y, Han R, Wang W (2021) Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications 165:113973","journal-title":"Expert Systems with Applications"},{"key":"2475_CR33","doi-asserted-by":"crossref","unstructured":"Solin MM, Alamsyah A, Rikumahu B, Saputra MAA (2019) Forecasting portfolio optimization using artificial neural network and genetic algorithm. In: 2019 7th International Conference on Information and Communication Technology (ICoICT), pp. 1\u20137. IEEE","DOI":"10.1109\/ICoICT.2019.8835344"},{"issue":"4","key":"2475_CR34","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1016\/j.jestch.2021.01.007","volume":"24","author":"P Koratamaddi","year":"2021","unstructured":"Koratamaddi P, Wadhwani K, Gupta M, Sanjeevi SG (2021) Market sentiment-aware deep reinforcement learning approach for stock portfolio allocation. Engineering Science and Technology, an International Journal 24(4):848\u2013859","journal-title":"Engineering Science and Technology, an International Journal"},{"key":"2475_CR35","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.eswa.2017.06.023","volume":"87","author":"S Almahdi","year":"2017","unstructured":"Almahdi S, Yang SY (2017) An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. Expert Systems with Applications 87:267\u2013279","journal-title":"Expert Systems with Applications"},{"key":"2475_CR36","unstructured":"Yu P, Lee JS, Kulyatin I, Shi Z, Dasgupta S (2019) Model-based deep reinforcement learning for dynamic portfolio optimization. arXiv preprint arXiv:1901.08740"},{"key":"2475_CR37","doi-asserted-by":"crossref","unstructured":"Sen J (2023) Portfolio optimization using reinforcement learning and hierarchical risk parity approach. In: Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications, pp. 509\u2013554. Springer","DOI":"10.1007\/978-3-031-38325-0_20"},{"key":"2475_CR38","volume":"198","author":"T Cui","year":"2024","unstructured":"Cui T, Du N, Yang X, Ding S (2024) Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach. Technological Forecasting and Social Change 198:122944","journal-title":"Technological Forecasting and Social Change"},{"key":"2475_CR39","doi-asserted-by":"crossref","unstructured":"Sattar A, Sarwar A, Gillani S, Bukhari M, Rho S, Faseeh M. A novel rms-driven deep reinforcement learning for optimized portfolio management in stock trading. IEEE Access (2025)","DOI":"10.1109\/ACCESS.2025.3546099"},{"key":"2475_CR40","doi-asserted-by":"crossref","DOI":"10.1016\/j.iswa.2024.200467","volume":"25","author":"PK Aritonang","year":"2025","unstructured":"Aritonang PK, Wiryono SK, Faturohman T (2025) Hidden-layer configurations in reinforcement learning models for stock portfolio optimization. Intelligent Systems with Applications 25:200467","journal-title":"Intelligent Systems with Applications"},{"key":"2475_CR41","doi-asserted-by":"crossref","unstructured":"Haraty RA, Sobeh S (2024) Unveiling insights from unstructured wealth: A comparative analysis of clustering techniques on blockchain cryptocurrency data. Advances in Computing & Engineering 4(1)","DOI":"10.21622\/698"},{"key":"2475_CR42","unstructured":"Cleuziou G (2024) Unsupervised learning for dynamic and multi-view data analysis. PhD thesis, Universit\u00e9 de Paris"},{"issue":"1","key":"2475_CR43","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s10994-020-05896-2","volume":"110","author":"D Bertsimas","year":"2021","unstructured":"Bertsimas D, Orfanoudaki A, Wiberg H (2021) Interpretable clustering: an optimization approach. Machine Learning 110(1):89\u2013138","journal-title":"Machine Learning"},{"issue":"1","key":"2475_CR44","first-page":"77","volume":"7","author":"H Markowitz","year":"1952","unstructured":"Markowitz H (1952) Portfolio selection. The Journal of Finance 7(1):77\u201391","journal-title":"The Journal of Finance"},{"issue":"1","key":"2475_CR45","doi-asserted-by":"crossref","first-page":"31","DOI":"10.2469\/faj.v45.n1.31","volume":"45","author":"RO Michaud","year":"1989","unstructured":"Michaud RO (1989) The markowitz optimization enigma: Is \u2018optimized\u2019 optimal? Financial Analysts Journal 45(1):31\u201342","journal-title":"Financial Analysts Journal"},{"issue":"2","key":"2475_CR46","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/S0047-259X(03)00096-4","volume":"88","author":"O Ledoit","year":"2004","unstructured":"Ledoit O, Wolf M (2004) A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 88(2):365\u2013411","journal-title":"Journal of Multivariate Analysis"},{"issue":"4","key":"2475_CR47","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1111\/1540-6261.00580","volume":"58","author":"R Jagannathan","year":"2003","unstructured":"Jagannathan R, Ma T (2003) Risk reduction in large portfolios: Why imposing the wrong constraints helps. The Journal of Finance 58(4):1651\u20131683","journal-title":"The Journal of Finance"},{"issue":"498","key":"2475_CR48","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1080\/01621459.2012.682825","volume":"107","author":"J Fan","year":"2012","unstructured":"Fan J, Zhang J, Yu K (2012) Vast portfolio selection with gross-exposure constraints. Journal of the American Statistical Association 107(498):592\u2013606","journal-title":"Journal of the American Statistical Association"},{"issue":"5","key":"2475_CR49","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1093\/rfs\/hhm075","volume":"24","author":"V DeMiguel","year":"2011","unstructured":"DeMiguel V, Garlappi L, Nogales FJ, Uppal R (2011) Optimal versus naive diversification: How inefficient is the 1\/n portfolio strategy? The Review of Financial Studies 24(5):1915\u20131953","journal-title":"The Review of Financial Studies"},{"key":"2475_CR50","doi-asserted-by":"crossref","unstructured":"Zouaghia Z, Kodia Z, Ben\u00a0Said L (2024) A machine learning-based trading strategy integrating technical analysis and multi-agent simulation. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 302\u2013313. Springer","DOI":"10.1007\/978-3-031-70415-4_26"},{"key":"2475_CR51","doi-asserted-by":"crossref","unstructured":"Zouaghia Z, Kodia Z, Ben\u00a0Said L (2024) Predicting the stock market prices using a machine learning-based framework during crisis periods. Multimedia Tools and Applications, 1\u201335","DOI":"10.1007\/s11042-024-20270-3"},{"issue":"2","key":"2475_CR52","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1504\/IJDMMM.2022.123357","volume":"14","author":"K Nakagawa","year":"2022","unstructured":"Nakagawa K, Yoshida K (2022) Time-series gradient boosting tree for stock price prediction. International Journal of Data Mining, Modelling and Management 14(2):110\u2013125","journal-title":"International Journal of Data Mining, Modelling and Management"},{"key":"2475_CR53","doi-asserted-by":"publisher","unstructured":"Zouaghia Z, Kodia Z, Ben\u00a0Said L (2024) A collective intelligence to predict stock market indices applying an optimized hybrid ensemble learning model. In: ICCCI 2024, Part I, LNCS 14810 , Computational Collective Intelligence, pp. 1\u201313. https:\/\/doi.org\/10.1007\/978-3-031-70816-9_6 . Springer Nature","DOI":"10.1007\/978-3-031-70816-9_6"},{"key":"2475_CR54","unstructured":"Jr, JWW (1978) New Concepts in Technical Trading Systems. Trend Research, Greensboro, NC. First introduced the Relative Strength Index (RSI) in Chapter 2"},{"key":"2475_CR55","doi-asserted-by":"crossref","unstructured":"Dhankar R, Maheshwari S (2016) Behavioural finance: A new paradigm to explain momentum effect. Available at SSRN 2785520","DOI":"10.2139\/ssrn.2785520"},{"issue":"11","key":"2475_CR56","first-page":"77","volume":"7","author":"H Markowitz","year":"1952","unstructured":"Markowitz H (1952) Modern portfolio theory. Journal of Finance 7(11):77\u201391","journal-title":"Journal of Finance"},{"key":"2475_CR57","unstructured":"Marek P, Sediva B (2017) Optimization and testing of rsi. In: 11th International Scientific Conference on Financial Management of Firms and Financial Institutions"},{"issue":"2","key":"2475_CR58","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.finmar.2011.10.003","volume":"15","author":"G Zhou","year":"2012","unstructured":"Zhou G, Zhu Y (2012) Time series momentum and mean reversion across markets. Journal of Financial Markets 15(2):217\u2013229. https:\/\/doi.org\/10.1016\/j.finmar.2011.10.003","journal-title":"Journal of Financial Markets"},{"key":"2475_CR59","unstructured":"Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347"},{"key":"2475_CR60","doi-asserted-by":"crossref","unstructured":"Sevriuk VA, Tan KY, Hyypp\u00e4 E, Silveri M, Partanen M, Jenei M, Masuda S, Goetz J, Vesterinen V, Gr\u00f6nberg L, et al (2019) Fast control of dissipation in a superconducting resonator. Applied Physics Letters 115(8)","DOI":"10.1063\/1.5116659"},{"key":"2475_CR61","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.patrec.2019.10.019","volume":"128","author":"S Sieranoja","year":"2019","unstructured":"Sieranoja S, Fr\u00e4nti P (2019) Fast and general density peaks clustering. Pattern recognition letters 128:551\u2013558","journal-title":"Pattern recognition letters"},{"key":"2475_CR62","unstructured":"Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. John Wiley & Sons"},{"key":"2475_CR63","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/13537.001.0001","author":"A Bucci","year":"2022","unstructured":"Bucci A, Mastromatteo I, T\u00f3th B (2022) Machine learning for finance: From theory to practice. MIT Press. https:\/\/doi.org\/10.7551\/mitpress\/13537.001.0001","journal-title":"MIT Press"},{"issue":"3","key":"2475_CR64","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1093\/jjfinec\/nbaa013","volume":"20","author":"S Bayer","year":"2022","unstructured":"Bayer S, Dimitriadis T (2022) Regression-based expected shortfall backtesting. Journal of Financial Econometrics 20(3):437\u2013471","journal-title":"Journal of Financial Econometrics"},{"issue":"1","key":"2475_CR65","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.jeconom.2022.04.007","volume":"235","author":"X Zhang","year":"2023","unstructured":"Zhang X, Liu C-A (2023) Model averaging prediction by k-fold cross-validation. Journal of Econometrics 235(1):280\u2013301","journal-title":"Journal of Econometrics"},{"key":"2475_CR66","doi-asserted-by":"crossref","unstructured":"Zouaghia Z, Kodia Z, Said LB (2024) Pred-ifdss: An intelligent financial decision support system based on machine learning models. In: 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 67\u201372. IEEE","DOI":"10.1109\/CoDIT62066.2024.10708156"},{"issue":"21","key":"2475_CR67","doi-asserted-by":"crossref","first-page":"18421","DOI":"10.1007\/s00521-022-07431-x","volume":"34","author":"R Kumar","year":"2022","unstructured":"Kumar R, Kumar P, Kumar Y (2022) Three stage fusion for effective time series forecasting using bi-lstm-arima and improved de-abc algorithm. Neural Computing and Applications 34(21):18421\u201318437","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"2475_CR68","doi-asserted-by":"crossref","first-page":"2111134","DOI":"10.1080\/08839514.2022.2111134","volume":"36","author":"Z Fathali","year":"2022","unstructured":"Fathali Z, Kodia Z, Ben Said L (2022) Stock market prediction of nifty 50 index applying machine learning techniques. Applied Artificial Intelligence 36(1):2111134","journal-title":"Applied Artificial Intelligence"},{"key":"2475_CR69","volume":"242","author":"J Zou","year":"2024","unstructured":"Zou J, Lou J, Wang B, Liu S (2024) A novel deep reinforcement learning based automated stock trading system using cascaded lstm networks. Expert Systems with Applications 242:122801","journal-title":"Expert Systems with Applications"},{"key":"2475_CR70","unstructured":"Ndikum P, Ndikum S (2024) Advancing investment frontiers: Industry-grade deep reinforcement learning for portfolio optimization. arXiv preprint arXiv:2403.07916"},{"key":"2475_CR71","unstructured":"Lixandru A (2024) Proximal policy optimization with adaptive exploration. arXiv preprint arXiv:2405.04664"},{"issue":"24","key":"2475_CR72","doi-asserted-by":"crossref","first-page":"12526","DOI":"10.3390\/app122412526","volume":"12","author":"N Malibari","year":"2022","unstructured":"Malibari N, Katib I, Mehmood R (2022) Smart robotic strategies and advice for stock trading using deep transformer reinforcement learning. Applied Sciences 12(24):12526","journal-title":"Applied Sciences"},{"issue":"5","key":"2475_CR73","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1287\/mnsc.1080.0986","volume":"55","author":"V DeMiguel","year":"2009","unstructured":"DeMiguel V, Garlappi L, Nogales FJ, Uppal R (2009) A generalized approach to portfolio optimization: Improving performance by constraining portfolio norms. Management science 55(5):798\u2013812","journal-title":"Management science"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02475-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-025-02475-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02475-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:57:22Z","timestamp":1760525842000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-025-02475-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,7]]},"references-count":73,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["2475"],"URL":"https:\/\/doi.org\/10.1007\/s10115-025-02475-6","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,7]]},"assertion":[{"value":"22 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal ethics"}}]}}