Abstract
Obtaining accurate and impartial forecasts from historical financial data is an emerging and dominant research area in the vast sphere of finance. An investment instrument in the market for structured investments is referred to as a mutual fund, and the tool to measure its price is the Net Asset Value (NAV). Investment managers and investors rely on accurate estimates of NAV of different investment funds to make informed decisions. However, predicting such complex financial series is challenging due to uncertainty and influence of economic and political factors. In this study, a NAV predictor model is designed using a low-complexity neural network, called Legendre Polynomial Neural Network (LPNN). Additionally, a Chaotic Flower Pollination Algorithm (CHFPA) was proposed in the learning phase of the network for tuning its unknown parameters. A series of ten chaotic maps has been applied to the classical FPA through three stagesto achieve improvement in the diversity of the initial population as well as to avoid local optima by carrying out unexpected swaps between global and local search processes. With an aim of accelerating global convergence speed, the selected chaotic variants have been thoroughly investigated on ten well known standard benchmark functions to find the most efficient one. The proposed LPNN-CHFPA NAV predictor model is tested on three separate data sets of reputed Indian financial firms for one day-ahead forecasting of NAV. In comparison with the other prediction models, based on the convergence curve, scatter plots, and various error metrics, the experimental results clearly show that the proposed framework performs well.
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References
Abdel-Raouf O, Abdel-Baset M, El-henawy I (2014) An improved flower pollination algorithm with Chaos, I.J. Educ Manag Eng 2:1–8
Anish CM, Majhi B (2015) Net asset value prediction using FLANN model. Int J Sci Res 4(2):2222–2227
Anish CM, Majhi B, Majhi R (2016) Development and evaluation of novel forecasting adaptive ensemble model. J Fin Data Sci 2(3):188–201
Arora S, Anand P (2017) Chaos-enhanced flower pollination algorithms for global optimization. J Intell Fuzzy Syst 33(6):3853–3869
Arora S, Anand P (2019) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl 31(8):4385–4405
Chauhan S, Singh M, Aggarwal AK (2021a) Bearing defect identification via evolutionary algorithm with adaptive wavelet mutation strategy. Measurement 179:109445
Chauhan S, Singh M, Aggarwal AK (2021b) Cluster head selection in heterogeneous wireless sensor network using a new evolutionary algorithm. Wirel Pers Commun 119(1):585–616
Chauhan S, Singh M, Aggarwal AK (2021c) Design of a two-channel quadrature mirror filter bank through a diversity-driven multi-parent evolutionary algorithm. Circuits Syst Signal Process 40(7):3374–3394
Chiang WC, Urban TL, Baldridge G (1996) A neural network fund net asset approach to mutual value forecasting. Omega 24(2):205–215
Dash R (2020) Performance analysis of an evolutionary recurrent Legendre polynomial neural network in application to FOREX prediction. J King Saud Univ Comput Inf Sci 32(9):1000–1011
Dash R, Dash PK (2016) Prediction of financial time series data using hybrid evolutionary Legendre neural network: evolutionary LENN. Int J Appl Evol Comput 7(1):16–32
Dash R, Dash PK (2017) MDHS–LPNN: a hybrid FOREX predictor model using a Legendre polynomial neural network with a modified differential harmony search technique. In: Handbook of neural computation, 1st edn, Chapter 25, pp 459–486. https://doi.org/10.1016/B978-0-12-811318-9.00025-9
Dash R, Rautray R, Dash R (2021) A Legendre neural network for credit card fraud detection. In: Intelligent and cloud computing, vol 153. Springer, Singapore, pp 573–580. https://doi.org/10.1007/978-981-15-6202-0_42
Emary E, Zawbaa HM, Hassanien AE, Parv B (2017) Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search. Adv Data Anal Classif 11(3):611–627
George NV, Panda G (2012) A reduced complexity adaptive Legendre neural network for nonlinear active noise control. In: 2012 19th international conference on systems, signals and image processing (IWSSIP). pp 560–563
Hota S, Pati SP, Satapathy P (2021) Forecasting of net asset value of Indian mutual funds using firefly algorithm-based neural network model. Lect Notes Netw Syst 151:217–224
Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Design Eng 5(3):275–284
Kaur A, Pal SK, Singh AP (2018) New chaotic flower pollination algorithm for unconstrained non-linear optimization functions. Int J Syst Assur Eng Manag 9(4):853–865
Kaur A, Pal SK, Singh AP (2020) Hybridization of Chaos and flower pollination algorithm over K-means for data clustering. Appl Soft Comput 97:105523
Kohli M, Arora S (2018) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Design Eng 5(4):458–472
Lin HS, Chen ML, Tong CC, Dai JW (2007) Using grey and RBFNN to predict the net asset value of single nation equity funds-a case study of Taiwan, US, and Japan. In: 2007 IEEE international conference on grey systems and intelligent services. IEEE, pp 892–897
Liu H, Abraham A, Clerc M (2007) Chaotic dynamic characteristics in swarm intelligence. Appl Soft Comput 7(3):1019–1026
Mall S, Chakraverty S (2016) Application of Legendre neural network for solving ordinary differential equations. Appl Soft Comput 43:347–356
Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys Rev E 49(5):4677–4683
Meng OK, Pauline O, Kiong SC, Wahab HA, Jafferi N (2017) Application of modified flower pollination algorithm on mechanical engineering design problem. In: IOP conference series: materials science and engineering, vol 165. IOP Publishing, p 012032. https://doi.org/10.1088/1757-899X/165/1/012032
Metwalli M, Abdel-Baset M, Hezam I (2015) A modified flower pollination algorithm for fractional programming problems. Int J Intell Syst Appl Eng 3(3):116–123
Mohakud R, Dash R (2022) Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2021.12.018
Mohanty S, Dash R (2021a) Application of computational intelligence techniques in the domain of net asset value prediction:a survey. In: Intelligent and cloud computing, vol 153. Springer, Singapore, pp 573–580. https://doi.org/10.1007/978-981-15-6202-0_59
Mohanty S, Dash R (2021b) A flower pollination algorithm based Chebyshev polynomial neural network for net asset value prediction. Evol Intell. https://doi.org/10.1007/s12065-021-00645-3
Narula A, Jha CB, Panda G (2015) Development and performance evaluation of three novel prediction models for mutual fund NAV prediction. Ann Res J Symbiosis Centre Manag Stud 3:227–238
Ozsoydan FB, Baykasoglu A (2021) Chaos and intensification enhanced flower pollination algorithm to solve mechanical design and unconstrained function optimization problems. Expert Syst Appl 184:115496
Patra JC, Chin WC, Meher PK, Chakraborty G (2008) Legendre-FLANN-based nonlinear channel equalization in wireless communication system. In: 2008 IEEE international conference on systems, man and cybernetics. pp 1826–1831
Pauline O, Meng OK, Kiong SC (2017) An improved flower pollination algorithm with chaos theory for function optimization. In: AIP conference proceedings. vol 1870, p 050012. https://doi.org/10.1063/1.4995922
Priyadarshini E (2015) A comparative analysis of prediction using artificial neural network and auto regressive integrated moving average. ARPN J Eng Appl Sci 10(7):3078–3081
Priyadarshini E, Babu AC (2012) A comparative analysis for forecasting the NAV’s of indian mutual fund using multiple regression analysis and artificial neural networks. Int J Trade Econ Financ 3(5):347–350
Reddy PDP, Reddy VV, Manohar TG (2016) Application of flower pollination algorithm for optimal placement and sizing of distributed generation in distribution systems. J Electr Syst Inf Technol 3(1):14–22
Rout AK, Dash PK, Dash R, Bisoi R (2017) Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach. J King Saud Univ Comput Inf Sci 29(4):536–552
Rout M, Koudjonou KM, Satapathy SC (2021) Analysis of net asset value prediction using low complexity neural network with various expansion techniques. EvolIntell 14:643–655
Vashishtha G, Kumar R (2022) An amended grey wolf optimization with mutation strategy to diagnose bucket defects in Pelton wheel. Measurement 187:110272
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
Yan H, Liu W, Liu X, Kong H, Lv C (2010) Predicting net asset value of investment fund based on BP neural network. In: 2010 international conference on computer application and system modelling (ICCASM 2010), vol 10. IEEE, pp V10–635
Yang D, Li G, Cheng G (2007) On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34(4):1366–1375
Yang XS (2012) Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation. Springer, Berlin, pp. 240–249
Yousri D, AbdelAty AM, Said LA, Elwakil AS, Maundy B, Radwan AG (2019a) Chaotic flower pollination and grey wolf algorithms for parameter extraction of bio-impedance models. Appl Soft Comput 75:750–774
Yousri D, Babu TS, Allam D, Ramachandaramurthy VK, Etiba MB (2019b) A novel chaotic flower pollination algorithm for global maximum power point tracking for photovoltaic system under partial shading conditions. IEEE Access 7:121432–121445
Yousri D, Allam D, Babu TS, AbdelAty AM, Radwan AG, Ramachandaramurthy VK, Eteiba MB (2020) Fractional chaos maps with flower pollination algorithm for chaotic systems’ parameters identification. Neural Comput Appl 32(20):16291–16327
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The first author designed the forecasting model, interpreted the data, conducted experiments, and analysis of the results. The second author explored the research area and was a major contributor in writing the manuscript. The author(s) read and approved the final manuscript.
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Mohanty, S., Dash, R. Evaluating chaotic functions with flower pollination algorithm for modelling an optimized low complexity neural network based NAV predictor model. Soft Comput 26, 9395–9417 (2022). https://doi.org/10.1007/s00500-022-07257-8
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DOI: https://doi.org/10.1007/s00500-022-07257-8