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Research Article

MCN portfolio: An efficient portfolio prediction and selection model using multiserial cascaded network with hybrid meta-heuristic optimization algorithm

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Received 17 Oct 2023, Accepted 17 Apr 2024, Published online: 08 May 2024

References

  • Abualigah L, Diabat A, Mirjalili S, Elaziz MA, Gandomi AH. 2021. The arithmetic optimization algorithm. Comput Methods Appl Mech Eng. 376:113609. doi: 10.1016/j.cma.2020.113609.
  • Adosoglou G, Lombardo G, Pardalos PM. 2021. Neural network embeddings on corporate annual filings for portfolio selection. Expert Syst Appl. 164:114053. doi: 10.1016/j.eswa.2020.114053.
  • Agarwal S, Muppalaneni NB. 2022. Portfolio optimization in stocks using mean–variance optimization and the efficient frontier. Int J Inf Technol. 14:2917–2926. doi: 10.1007/s41870-022-01052-2.
  • Akbar Movassagh A, Alzubi JA, Gheisari M, Rahimi M, kumarMohan S, Afzaal Abbasi A, Nabipour N. 2023. Artificial neural networkstraining algorithm integrating invasive weed optimization with diferentialevolutionary model. J Ambient Intell Humaniz Comput.
  • Alzaman C. 2024. Deep learning in stock portfolio selection and predictions. Expert Syst Appl. 237:121404. doi: 10.1016/j.eswa.2023.121404.
  • Alzubi OA, Alzubi JA, Alweshah M, Qiqieh I, Al-Shami S, Ramachandran M. 2020. An OptimalPruning algorithm of Classifier ensembles: dynamic programming approach. NeuralComputing & Applications.
  • Alzubi JA, Jain R, Nagrath P, Satapathy S, Taneja S, Gupta P. 2020. Deep image captioning using anensemble of CNN and LSTM based deep neural networks. Journal of Intelligent andFuzzy Systems.
  • Assaad M, Bone R, Cardot H. 2008. A new boosting algorithm for improved time-series forecasting with recurrent neural networks. Inf Fusion. 9(1):41–55. doi: 10.1016/j.inffus.2006.10.009.
  • Du J. 2022. Mean–variance portfolio optimization with deep learning based-forecasts for cointegrated stocks. Expert Syst Appl. 201:117005. doi: 10.1016/j.eswa.2022.117005.
  • Esfandyari M, Delouei AA, Jalai A. 2023. Optimization of ultrasonic-excited double-pipe heat exchanger with machine learning and PSO. Int Commun Heat Mass Transfer. 147:106985. doi: 10.1016/j.icheatmasstransfer.2023.106985.
  • Guo G, Rao Y, Zhu F, Xu F, Gherghina SC. 2020. Innovative deep matching algorithm for stock portfolio selection using deep stock profiles. PloS One. 15(11):e0241573. doi: 10.1371/journal.pone.0241573.
  • Hargreaves CA, Dixit P, Solanki A. 2013. Stock portfolio selection using data mining approach. IOSR J Eng. 3(11):42–48. doi: 10.9790/3021-031114248.
  • Henrique PAMB, Albuquerque PHM, Marcelino SSDF, Peng Y. 2021. Portfolio selection with support vector regression: multiple kernels comparison. Int J Bus Intell Data Min. 18(4):395–410. doi: 10.1504/IJBIDM.2021.115476.
  • Hu K, Gan Q, Zhang Y, Deng S, Xiao F, Huang W, Cao C, Gao X. 2019. Brain tumor segmentation using multi-cascaded convolutional neural networks and conditional random field. IEEE Access. 7:92615–92629. doi: 10.1109/ACCESS.2019.2927433.
  • Hung M-C, Hsia P-H, Kuang X-J, Lin S-K. 2024. Intelligent portfolio construction via news sentiment analysis. Int Rev Econ Financ. 89:605–617.
  • Hung MC, Hsia PH, Kuang XJ, Lin SK. 2024. Intelligent portfolio construction via news sentiment analysis. Int Rev Econ Finance. 89:605–617. doi: 10.1016/j.iref.2023.07.103.
  • Jung J, Kim S. 2015. An adaptively managed dynamic portfolio selection model using a time-varying investment target according to the market forecast. J Oper Res Soc. 66(7):1115–1131. doi: 10.1057/jors.2014.72.
  • Kaur S, Awasthi LK, Sangal AL, Dhiman G. 2020. Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell. 90:103541. doi: 10.1016/j.engappai.2020.103541.
  • Liu S, Wang B, Li H, Chen C, Wang Z. 2023. Continual portfolio selection in dynamic environments via incremental reinforcement learning.Int. Int J Mach Learn Cyber. 14(1):269–279. doi: 10.1007/s13042-022-01639-y.
  • Li J, Zhang Y, Yang X, Chen L. 2023. Online portfolio management via deep reinforcement learning with high-frequency data. Inf Process Manage. 60(3):103247. doi: 10.1016/j.ipm.2022.103247.
  • Ma Y, Han R, Wang W. 2021. Portfolio optimization with return prediction using deep learning and machine learning. Expert Syst Appl. 165:113973. doi: 10.1016/j.eswa.2020.113973.
  • Ma Y, Mao R, Lin Q, Wu P, Cambria E. 2024. Quantitative stock portfolio optimization by multi-task learning risk and return. Inf Fusion. 104:102165. doi: 10.1016/j.inffus.2023.102165.
  • Mattera G, Mattera R. 2023. Shrinkage estimation with reinforcement learning of large variance matrices for portfolio selection. Intell Syst Appl. 17:200181. doi: 10.1016/j.iswa.2023.200181.
  • Ma Y, Wang W, Ma Q. 2023. A novel prediction based portfolio optimization model using deep learning. Comput Ind Eng. 177:109023. doi: 10.1016/j.cie.2023.109023.
  • Medina AG, Moreno EA. 2023. LSTM–GARCH hybrid model for the prediction of volatility in cryptocurrency portfolios. Comput Econ.
  • Nadal AVO, DeMiguel V. 2018. Technical note—A robust perspective on transaction costs in portfolio optimization. Oper Res. 66(3):733–739. doi: 10.1287/opre.2017.1699.
  • Naik MJ, Albuquerque AL. 2022. Hybrid optimization search-based ensemble model for portfolio optimization and return prediction in business investment. Prog Artif Intell. 11(4):315–331. doi: 10.1007/s13748-022-00287-1.
  • Niknam SM, Esfandyari M, Karimi M. 2018. Efficient prediction of water vapor adsorption capacity in porous metal–organic framework materials: ANN and ANFIS modeling. J Iran Chem Soc. 16(1):11–20. doi: 10.1007/s13738-018-1476-y.
  • Parida P, Pradhan C, Alzubi JA, Javadpour A, Gheisai M, Liu Y, Lee C-C. 2023. EllipticCurve cryptographic image encryption using henon map and Hopfield ChaoticNeural Network. Multimedia Tools Appl.
  • Patalaya S, Bandlamudi MR. 2020. Stock price prediction and portfolio selection using artificial intelligence. Asia Pacific J Inf Syst. 30(1):31–52. doi: 10.14329/apjis.2020.30.1.31.
  • Peng Y, Albuquerque PHM, Nascimento IFD, Machado JF. 2019. Between nonlinearities, complexity, and noises: an application on portfolio selection using kernel Principal component analysis. Entropy. 21(4):376. doi: 10.3390/e21040376.
  • Qiao Y, Wang Y, Ma C, Yang J. 2021. Short-term traffic flow prediction based on 1DCNN-LSTM neural network structure. Mod Phys Lett B. 35(2):2150042. doi: 10.1142/S0217984921500421.
  • Sharma M, Shekhawat HS. 2021. Intelligent portfolio asset prediction enabled by hybrid Jaya-based spotted hyena optimization algorithm Jaya-based spotted hyena optimization algorithm. Kybernetes. 50(12):3331–3366. doi: 10.1108/K-09-2020-0563.
  • Sharma M, Shekhawat HS. 2022. Portfolio optimization and return prediction by integrating modified deep belief network and recurrent neural network. Knowledge-Based Syst. 250:109024. doi: 10.1016/j.knosys.2022.109024.
  • Soleymani F, Paquet E. 2020. Financial portfolio optimization with online deep reinforcement learning and restricted stacked autoencoder—DeepBreath. Expert Syst Appl. 156:113456. doi: 10.1016/j.eswa.2020.113456.
  • Ta VD, Liu C, Tadesse DA. 2020. Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Appl Sci. 10(2):437. doi: 10.3390/app10020437.
  • Wang L, Cao Q, Zhang Z, Mirjalili S, Zhao W. 2022. Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems.Eng. Appl Artif Intell. 114:105082. doi: 10.1016/j.engappai.2022.105082.
  • Wang H, Wu Z. 2020. Mean-variance portfolio selection with discontinuous prices and random horizon in an incomplete market. Sci China Inf Sci. 63(7):63. doi: 10.1007/s11432-018-9531-7.
  • Wei W, Wu H, Ma H. 2019. An AutoEncoder and LSTM-Based traffic flow prediction method. Sensors. 19(13):2946. doi: 10.3390/s19132946.
  • Wu Q, Liu X, Qin J, Zhou L, Mardani A, Deveci M. 2022. An integrated generalized TODIM model for portfolio selection based on financial performance of firms. Knowledge-Based Syst. 249:108794. doi: 10.1016/j.knosys.2022.108794.
  • Xidonas P, Askounis D, Psarras J. 2009. Common stock portfolio selection: a multiple criteria decision making methodology and an application to the Athens stock Exchange. Oper Res. 9(1):55–79. doi: 10.1007/s12351-008-0027-1.
  • Xidonas P, Mavrotas G, Psarras J. 2010. Equity portfolio construction and selection using multiobjective mathematical programming. J Global Optim. 47(2):185–209. doi: 10.1007/s10898-009-9465-4.
  • Zhang Z, Zohren S, Roberts S. 2020. Deep learning for portfolio optimization. Portfolio Manage. doi: 10.2139/ssrn.3613600.
  • Zhaoa W, Wanga L, Mirjalili S. 2022. Artificial hummingbird algorithm: a new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng. 388:114194. doi: 10.1016/j.cma.2021.114194.
  • Zhao T, Ma X, Li X, Zhang C. 2023. Asset correlation based deep reinforcement learning for the portfolio selection. Expert Syst Appl. 221:119707. doi: 10.1016/j.eswa.2023.119707.

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