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Simulated Annealing Algorithm as Heuristic Search Method in the Weibull Distribution for Investment Return Modelling

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Digital Economy, Business Analytics, and Big Data Analytics Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1010))

Abstract

In this paper, a modified internal rate of return (MIRR) has been presented on the assumption of Weibull distribution to investigate the investment's attractiveness in the Malaysian property development sector (MPDS). The research intends is to produce parameters estimates of the Weibull distribution for investment analysis for a long-time investment period. The MIRR data were obtained from the company financial report for 5 years investment period from 2010–2014. The Maximum likelihood estimation method has been incorporated with the Simulated annealing algorithm (SA) in estimating the parameters of Weibull distribution. The shape parameter of the Weibull distribution reflects the effectiveness in maximizing the investment return based on MIRR with lower returns and is represented as the slope of the fitted line on a Weibull probability plot. The Weibull parameter estimated using Simulated annealing (SAA) has been compared with the existing Weibull parameter estimation methods. The finding reveals that the proposed methods have good agreement with other methods used for Weibull parameter estimates based on MIRR data. The research is expected to provide an overview of the investment behaviour for the long term investment period. Therefore, the new approach based on SA in estimating the parameters Weibull function can serve as a good alternative approach for the assessment of the rate of return on investment potential.

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References

  1. Mustafa W, Sabri SRM (2020) A simulation study: obtaining a sufficient sample size of discrete-time Markov chains of investment in a short frequency of time 10:906–919. https://doi.org/10.18488/journal.aefr.2020.108.906.919

  2. Sabri SRM, Mustafa Sarsour W (2019) Modelling on stock investment valuation for long-term strategy. J Invest Manag 8:60. https://doi.org/10.11648/j.jim.20190803.11

  3. Doganaksoy N (2004) Weibull models. Technometrics. https://doi.org/10.1198/tech.2004.s226

    Article  Google Scholar 

  4. Erik Karl’en CW (2017) Eturn ate rediction

    Google Scholar 

  5. Genschel U, Meeker WQ (2010) A comparison of maximum likelihood and median-rank regression for Weibull estimation. Qual Eng. https://doi.org/10.1080/08982112.2010.503447

    Article  Google Scholar 

  6. Sgarbossa F, Zennaro I, Florian E, Persona A (2018) Impacts of weibull parameters estimation on preventive maintenance cost. IFAC-PapersOnLine. https://doi.org/10.1016/j.ifacol.2018.08.369

    Article  Google Scholar 

  7. Teimouri M, Gupta AK (2013) On the three-parameter Weibull distribution shape parameter estimation. J Data Sci

    Google Scholar 

  8. Jamei R (2020) Investigating the mathematical models (TOPSIS, SAW) to prioritize the investments in the accepted pharmaceutical companies in Tehran Stock Exchange 5:215–227. https://doi.org/10.22034/amfa.2020.1880616.1312

  9. Raei R, Bahrani Jahromi M (2012) Portfolio optimization using a hybrid of fuzzy ANP, VIKOR and TOPSIS. Manag Sci Lett https://doi.org/10.5267/j.msl.2012.07.019

  10. Sarsour WM, Sabri SRM (2020) Forecasting the long-run behavior of the stock price of some selected companies in the Malaysian construction sector: a Markov chain approach. Int J Math Eng Manag Sci 5:296–308. https://doi.org/10.33889/IJMEMS.2020.5.2.024

  11. Abubakar H, Sabri SRM (2021) Simulation study on modified Weibull distribution for modelling of Investment return. Partanika J Sci Technol 29

    Google Scholar 

  12. Weibull W (1951) A statistical distribution function of wide applicability. J Appl Mech 18(18):293–297

    Article  Google Scholar 

  13. Peng X, Yan Z (2014) Estimation and application for a new extended Weibull distribution. Reliab Eng Syst Saf. https://doi.org/10.1016/j.ress.2013.07.007

    Article  Google Scholar 

  14. Lawless JF (2003) Statistical models and methods for lifetime data, 2nd edn

    Google Scholar 

  15. Elmahdy EE, Aboutahoun AW (2013) A new approach for parameter estimation of finite Weibull mixture distributions for reliability modeling. Appl Math Model. https://doi.org/10.1016/j.apm.2012.04.023

    Article  MathSciNet  MATH  Google Scholar 

  16. Abbasi B, Eshragh Jahromi AH, Arkat J, Hosseinkouchack M (2006) Estimating the parameters of Weibull distribution using simulated annealing algorithm. Appl Math Comput. https://doi.org/10.1016/j.amc.2006.05.063

    Article  MathSciNet  MATH  Google Scholar 

  17. Nadarajah S, Kotz S (2006) The modified Weibull distribution for asset returns. Quant Financ 6:449

    Article  Google Scholar 

  18. Lai CD, Xie M, Murthy DNP (2003) A modified Weibull distribution. IEEE Trans Reliab. https://doi.org/10.1109/TR.2002.805788

    Article  Google Scholar 

  19. Malevergne Y, Pisarenko V, Sornette D (2006) The modified Weibull distribution for asset returns: Reply

    Google Scholar 

  20. Almetwally EM, Almongy HM (2019) Estimation methods for the New Weibull-Pareto distribution: simulation and application 17, 613–632. https://doi.org/10.6339/JDS.201907

  21. Akdaǧ SA, Dinler A (2009) A new method to estimate Weibull parameters for wind energy applications. Energy Convers Manag. https://doi.org/10.1016/j.enconman.2009.03.020

    Article  Google Scholar 

  22. Rahmani M, Eraqi MK, Nikoomaram H (2019) Portfolio optimization by means of meta heuristic algorithms 4:83–97. https://doi.org/10.22034/amfa.2019.579510.1144

  23. Gao W, Sheng H, Wang J, Wang S (2019) Artificial bee colony algorithm based on novel mechanism for fuzzy portfolio selection. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2018.2856120

    Article  Google Scholar 

  24. Chang TJ, Yang SC, Chang KJ (2009) Portfolio optimization problems in different risk measures using genetic algorithm. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2009.02.062

    Article  Google Scholar 

  25. Bavarsad Salehpoor I, Molla-Alizadeh-Zavardehi S (2019) A constrained portfolio selection model at considering risk-adjusted measure by using hybrid meta-heuristic algorithms. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2018.11.011

    Article  Google Scholar 

  26. Ni Q, Yin X, Tian K, Zhai Y (2017) Particle swarm optimization with dynamic random population topology strategies for a generalized portfolio selection problem. Nat Comput. https://doi.org/10.1007/s11047-016-9541-x

    Article  MathSciNet  MATH  Google Scholar 

  27. Erana-Diaz ML, Cruz-Chavez MA, Rivera-Lopez R, Martinez-Bahena B, Avila-Melgar EY, Heriberto Cruz-Rosales M (2020) Optimization for risk decision-making through simulated annealing. IEEE Access. 8:117063–117079. https://doi.org/10.1109/ACCESS.2020.3005084

    Article  Google Scholar 

  28. Javidrad F, Nazari M (2017) A new hybrid particle swarm and simulated annealing stochastic optimization method. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2017.07.023

    Article  Google Scholar 

  29. Zhang W, Maleki A, Rosen MA, Liu J (2018) Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage. Energy. https://doi.org/10.1016/j.energy.2018.08.112

    Article  Google Scholar 

  30. Abubakar H, Rijal S, Sabri M, Masanawa SA, Yusuf S (2020) Modified election algorithm in hopfield neural network for optimal random k satisfiability representation. Int J Simul Multidisci Des Optim 16:1–13

    Google Scholar 

  31. Abubakar H, Danrimi ML (2021) Hopfield type of artificial neural network via election algorithm as heuristic search method for random Boolean kSatisfiability. Int J Comput Digit Syst 10:659–673. https://doi.org/10.12785/ijcds/100163

  32. Ghadiri Nejad M, Güden H, Vizvári B, Vatankhah Barenji R (2018) A mathematical model and simulated annealing algorithm for solving the cyclic scheduling problem of a flexible robotic cell. Adv Mech Eng. https://doi.org/10.1177/1687814017753912

    Article  Google Scholar 

  33. Kellison S (2009) stephen-kellison-theory-of-interest-3e.pdf

    Google Scholar 

  34. Protter P, Capinski M, Zastawniak T (2004) Mathematics for finance: an introduction to financial engineering

    Google Scholar 

  35. Thomas GM (1995) Weibull parameter estimation using genetic algorithms and a heuristic approach to cut-set analysis

    Google Scholar 

  36. Abbasi B, Niaki STA, Khalife MA, Faize Y (2011) A hybrid variable neighborhood search and simulated annealing algorithm to estimate the three parameters of the Weibull distribution. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2010.07.022

    Article  Google Scholar 

  37. Yonar A, Yapici Pehlivan N (2020) Artificial bee colony with levy flights for parameter estimation of 3-p Weibull distribution. Iran J Sci Technol Trans A Sci. https://doi.org/10.1007/s40995-020-00886-4

  38. Yang F, Ren H, Hu Z (2019) Maximum likelihood estimation for three-parameter Weibull distribution using evolutionary strategy. Math Probl Eng 2019. https://doi.org/10.1155/2019/6281781

  39. Jiang H, Wang J, Wu J, Geng W (2017) Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions

    Google Scholar 

  40. Sultana T, Muhammad F, Aslam M (2019) Estimation of parameters for the lifetime distributions 12:77–92

    Google Scholar 

  41. Lei J (2016) A goodness-of-fit test for stochastic block models. Ann Stat 44:401–424

    Article  MathSciNet  Google Scholar 

  42. Tashkova K, Šilc J, Atanasova N, Džeroski S (2012) Parameter estimation in a nonlinear dynamic model of an aquatic ecosystem with meta-heuristic optimization. Ecol Modell. https://doi.org/10.1016/j.ecolmodel.2011.11.029

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Mathematical Sciences, Universiti Sains Malaysia, 11800, Pulau Penang, Malaysia

    Hamza Abubakar & Shamsul Rijal Muhammad Sabri

  2. Department of Mathematics, Isa Kaita College of Education, Dutsin-Ma, Katsina State, Nigeria

    Hamza Abubakar

Authors
  1. Hamza Abubakar
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  2. Shamsul Rijal Muhammad Sabri
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Corresponding author

Correspondence to Hamza Abubakar .

Editor information

Editors and Affiliations

  1. Business Analytics and Information Systems, Faculty of Business, Al-Zaytoonah University of Jordan, Amman, Jordan

    Saad G. Yaseen

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Abubakar, H., Sabri, S.R.M. (2022). Simulated Annealing Algorithm as Heuristic Search Method in the Weibull Distribution for Investment Return Modelling. In: Yaseen, S.G. (eds) Digital Economy, Business Analytics, and Big Data Analytics Applications. Studies in Computational Intelligence, vol 1010. Springer, Cham. https://doi.org/10.1007/978-3-031-05258-3_32

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