Dividend Payout Forecast : Multiple Linear Regression vs Genetic Algorithm-Neural Network

Lidya Chitra Laoh


This research aims to compare two methods of forecasting, i.e Multiple Linear Regression (MLR) and Genetic Algorithm-Neural Network (GA-NN), in forecasting dividend payout of Indonesian manufacturing company listed on Indonesia Stock Exchange from 2010-2014. Having collected 1384 firm-year observations, the result shows that these two methods could be used to predict dividend payout by considering earnings, free cash flow, growth opportunity, leverage, liquidity and size. This resesarch finds that even though both methods are powerful in prediction, yet in this case, MLR outperforms GA-NN.


Keywords : Forecasting, Genetic Algorithm-Neural Network (GA-NN), Multiple Linear Regression (MLR), Dividend Payout Policy , Indonesian Manufacturing Companies

Full Text:



L. J. Gitman , C. J. Zutter. "Principles of managerial finance” 14th edition Pearson Education South Asia Pte Ltd, Singapore , 2018.

J. Kim, C. Won, and J. K. Bae, "A knowledge integration model for the prediction of corporate dividends," Expert Systems with Applications, vol. 37, pp. 1344-1350, 2010.

H. Ghoddusi, G.G. Creamer, N. Rafizadeh, “Machine learning in energy economics and finance : A review, “Energy Economics”, vol.81, pp.709-727, 2019

G. Kant and K. S. Sangwan, "Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network coupled with genetic algorithm," Procedia Cirp, vol. 31, pp. 453-458, 2015.

F. F. Ping and F. X. Fei, "Multivariant forecasting mode of Guangdong province port throughput with genetic algorithms and Back Propagation neural network," Procedia-Social and Behavioral Sciences, vol. 96, pp. 1165-1174, 2013.

F. Yang and Z. Yue, "Kernel density estimation of three-parameter Weibull distribution with neural network and genetic algorithm," Applied Mathematics and Computation, vol. 247, pp. 803-814, 2014.

H. Chiroma, A. Y. u. Gital, A. Abubakar, M. J. Usman, and U. Waziri, "Optimization of neural network through genetic algorithm searches for the prediction of international crude oil price based on energy products prices," in Proceedings of the 11th ACM Conference on Computing Frontiers, 2014, p. 27.

M. L. R. Torregoza and E. P. Dadios, "Comparison of neural network and hybrid genetic algorithm-neural network in forecasting of Philippine Peso-US Dollar exchange rate," in 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014, pp. 1-5.

Y. Zhang, X. Gao, and S. Katayama, "Weld appearance prediction with BP neural network improved by genetic algorithm during disk laser welding," Journal of Manufacturing Systems, vol. 34, pp. 53-59, 2015.

D. N. Gujarati, Basic econometrics: Tata McGraw-Hill Education, 2009.

L. D. Whitley and T. Hanson, "Optimizing Neural Networks Using FasterMore Accurate Genetic Search," in Proceedings of the 3rd international conference on genetic algorithms, 1989, pp. 391-397.

D. J. Montana and L. Davis, "Training Feedforward Neural Networks Using Genetic Algorithms," in Ijcai, 1989, pp. 762-767.

S. A. Harp, T. Samad, and A. Guha, "The genetic synthesis of neural networks," in Proceedings of the International Conference on Genetic Algorithms, 1989.

D. Dasgupta and D. R. McGregor, "Designing application-specific neural networks using the structured genetic algorithm," in [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks, 1992, pp. 87-96.

K. Blomström, "Benchmarking an artificial neural network tuned by a genetic algorithm," , 2012.

DOI: http://dx.doi.org/10.31154/cogito.v5i2.210.252%20-%20265


  • There are currently no refbacks.

CogITo Smart Journal
A publication of Fakultas Ilmu Komputer, Universitas Klabat
In partnership with Coris and IndoCEISS
Phone: +62 (431) 891035
email: editorial.cogito@unklab.ac.id | web: http://cogito.unklab.ac.id/index.php/cogito
Free counters!
View CogITo Smart Journal Stats

CogITo Smart Journal is indexed by:
  DOAJ    SINTA     Indonesia OneSearch by Perpusnas    Crossref    Google Scholar      Base Search PKP Index    neliti    EBSCO Information Science    mendeley          scilit    road    worldcat    DRJI    OpenAIREplus    copac    Gent University Library Stanford Library    Harvard Library    Leiden University Libraries    The University of Sheffield    Boston University Library    University of Manchester    University of Oxford    CORE    Livivo

CogITo Smart Journal is licensed under a Creative Commons Attribution 4.0 International License.