Enhancing Accuracy in Stock Price Prediction: The Power of Optimization Algorithms
Keywords:
Bat Algorithm, Optimization, Prediction, Particle Swarm Optimization, Stock price
Abstract
The purpose of this research was to improve the accuracy of stock price prediction by implementing optimization algorithms on forecasting methods, in this case, the exponential smoothing method. This research implemented the Particle Swarm Optimization (PSO) and Bat Algorithm metaheuristic optimization algorithms to determine the single-exponential smoothing method’s smoothing parameters. Before implementing the optimization algorithm, the way to determine the smoothing parameters was by trial-and-error method, which is considered less effective. Therefore, the novelty of this research is tuning the parameters of the exponential smoothing method using a comparison of two metaheuristic algorithms, namely the particle swarm optimization algorithm compared to the bat algorithm. The Single Exponential Smoothing method with PSO and Bat algorithms was proven to improve accuracy. The alpha parameter found by the PSO algorithm is 0.9346, and the bat algorithm is 0.936465. With a MAPE of 1.0311%, it was better than the MAPE generated in the Single Exponential smoothing method by trial and error of 1.0316%. This research contributes to providing insight that in a highly sensitive stock prediction situation, metaheuristic algorithms can be used to create more accurate and efficient prediction results.
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2021, https://doi.org/https://doi.org/10.3390/joitmc7010056.
[2] B. Mujib and I. R. Candraningrat, “Capital market reaction to Covid-19 pandemic on LQ45 shares at Indonesia stock exchange
(IDX),” American Journal of Humanities and Social Sciences Research, vol. 5, no. 3, pp. 74–80, 2021.
[3] S. Corbet, Y. G. Hou, Y. Hu, L. Oxley, and D. Xu, “Pandemic-related financial market volatility spillovers: Evidence from the
Chinese COVID-19 epicentre,” International Review of Economics & Finance, vol. 71, pp. 55–81, jan 2021, https://doi.org/10.
1016/j.iref.2020.06.022.
[4] G. Sonkavde, D. S. Dharrao, A. M. Bongale, S. T. Deokate, D. Doreswamy, and S. K. Bhat, “Forecasting Stock Market Prices
Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications,”
International Journal of Financial Studies, vol. 11, no. 3, p. 94, jul 2023, https://doi.org/10.3390/ijfs11030094.
[5] R. Gunawan Santosa, A. R. Chrismanto, and Y. Lukito, “STOCKS FORECASTING EXPLORATION ON LQ45 INDEX USING
ARIMA(p,d,q) MODEL,” Journal of Theoretical and Applied Information Technology, vol. 15, no. 13, 2022.
[6] K. Ali, “Forecasting Analysis of Share Price Index in Construction Companies Registered in Indonesia Stock Exchange 2015-
2019,” Journal of Economics Research and Social Sciences, vol. 5, no. 1, pp. 42–63, 2021.
[7] J. Tanuwijaya and S. Hansun, “LQ45 stock index prediction using k-nearest neighbors regression,” International Journal of
Recent Technology and Engineering, vol. 8, no. 3, pp. 2388–2391, 2019.
[8] R. Sabri, M. I. Tabash, M. Rahrouh, B. H. Alnaimat, S. Ayubi, and M. AsadUllah, “Prediction of macroeconomic variables of
Pakistan: Combining classic and artificial network smoothing methods,” Journal of Open Innovation: Technology, Market, and
Complexity, vol. 9, no. 2, p. 100079, jun 2023, https://doi.org/10.1016/j.joitmc.2023.100079.
[9] V. A. Fitria, “Parameter Optimization of Single Exponential Smoothing Using Golden Section Method for Groceries Forecasting,”
ZERO: Jurnal Sains, Matematika dan Terapan, vol. 2, no. 2, p. 89, 2019, https://doi.org/10.30829/zero.v2i2.3438.
[10] E. Cadenas, O. A. Jaramillo, and W. Rivera, “Analysis and forecasting of wind velocity in chetumal, quintana roo, using the
single exponential smoothing method,” Renewable Energy, vol. 35, no. 5, pp. 925–930, may 2010, https://doi.org/10.1016/J.
RENENE.2009.10.037.
[11] C. Deng, X. Zhang, Y. Huang, and Y. Bao, “Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization
Algorithm for Electricity Consumption Forecasting,” Energies, vol. 14, no. 13, p. 4036, jul 2021, https://doi.org/10.
3390/en14134036.
[12] A. Thakkar and K. Chaudhari, “A Comprehensive Survey on Portfolio Optimization, Stock Price and Trend Prediction Using
Particle Swarm Optimization,” Archives of Computational Methods in Engineering, vol. 28, no. 4, pp. 2133–2164, jun 2021,
https://doi.org/10.1007/s11831-020-09448-8.
[13] D. Tien Bui, N. D. Hoang, H. Nguyen, and X. L. Tran, “Spatial prediction of shallow landslide using Bat algorithm optimized
machine learning approach: A case study in Lang Son Province, Vietnam,” Advanced Engineering Informatics, vol. 42, p.
100978, oct 2019, https://doi.org/10.1016/J.AEI.2019.100978.
[14] Z. Xin-gang, L. Ji, M. Jin, and Z. Ying, “An improved quantum particle swarm optimization algorithm for environmental
economic dispatch,” Expert Systems with Applications, vol. 152, p. 113370, aug 2020, https://doi.org/10.1016/j.eswa.2020.
113370.
[15] L. Liu and L. Wu, “Predicting housing prices in China based on modified Holt’s exponential smoothing incorporating whale
optimization algorithm,” Socio-Economic Planning Sciences, vol. 72, p. 100916, dec 2020, https://doi.org/10.1016/j.seps.2020.
100916.
[16] S. M. Naik, R. P. K. Jagannath, and V. Kuppili, “Estimation of the Smoothing Parameter in Probabilistic Neural Network
Using Evolutionary Algorithms,” Arabian Journal for Science and Engineering, vol. 45, no. 4, pp. 2945–2955, apr 2020,
https://doi.org/10.1007/s13369-019-04227-5.
[17] N. P. S. Widitriani, W. G. S. Parwita, and N. P. S. Meinarni, “Forecasting system using single exponential smoothing with
golden section optimization,” Journal of Physics: Conference Series, vol. 1516, no. 1, p. 012008, apr 2020, https://doi.org/10.
1088/1742-6596/1516/1/012008.
[18] A. N. Ramadanti, D. C. R. Novitasari, I. A. Wijaya, V. T. P. Swindiarto, and W. D. Utami, “Optimization of Tug Services
in Tanjung Perak Port Using Assignment Model Based on Forecasting Results of Tug Services,” BAREKENG: Jurnal Ilmu
Matematika dan Terapan, vol. 16, no. 1, pp. 263–270, mar 2022, https://doi.org/10.30598/barekengvol16iss1pp261-268.
[19] M. Jahandideh-Tehrani, G. Jenkins, and F. Helfer, “A comparison of particle swarm optimization and genetic algorithm for daily
rainfall-runoff modelling: a case study for Southeast Queensland, Australia,” Optimization and Engineering, vol. 22, no. 1, pp.
29–50, mar 2021, https://doi.org/10.1007/s11081-020-09538-3.
[20] E. Vivas, H. Allende-Cid, and R. Salas, “A systematic review of statistical and machine learning methods for electrical power
forecasting with reported mape score,” Entropy, vol. 22, no. 12, p. 1412, 2020.
[21] W. Lertthaitrakul, P. Khumsawat, and N. Manirochana, “A comparison forecast volume of outbound containers in case of The
Bangkok port Between Exponential Smoothing and ARIMA Model,” Turkish Journal of Computer and Mathematics Education
(TURCOMAT), vol. 12, no. 8, pp. 3010–3016, apr 2021, https://doi.org/10.17762/TURCOMAT.V12I8.4137.
[22] R. Gustriansyah, N. Suhandi, F. Antony, and A. Sanmorino, “Single exponential smoothing method to predict sales multiple
products,” Journal of Physics: Conference Series, vol. 1175, no. 1, p. 012036, mar 2019.
[23] A. G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Archives of Computational
Methods in Engineering, vol. 29, no. 5, pp. 2531–2561, aug 2022, https://doi.org/10.1007/s11831-021-09694-4.
[24] T. Anagnostopoulos, F. Komisopoulos, A. Vlachos, A. Psarras, I. Salmon, and K. Ntalianis, “Sustainable supply chain management
of electric grid power consumption load for smart cities based on second-order exponential smoothing algorithm,” WSEAS
Transactions on Systems, vol. 21, pp. 247–256, 2022.
[25] D. Rahmalia and T. Herlambang, “Application Bat Algorithm for Estimating Super Pairwise Alignment Parameters on Similarity
Analysis Between Virus Protein Sequences,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 6, no. 2, p. 1, jan
2021, https://doi.org/10.26555/jiteki.v6i2.14323.
Published
2024-03-25
How to Cite
Fitria, V., & Widayanti, L. (2024). Enhancing Accuracy in Stock Price Prediction: The Power of Optimization Algorithms. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(2), 405-418. https://doi.org/https://doi.org/10.30812/matrik.v23i2.3785
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