Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies
(1) Department of Informatic Engineering, STMIK YMI Tegal, Indonesia
(2) Department of Informatic Engineering, STMIK YMI Tegal, Indonesia
(3) Department of Informatic Engineering, STMIK YMI Tegal, Indonesia
(4) Department of Informatic Engineering, STMIK YMI Tegal, Indonesia
(5) Department of Information System, STMIK YMI Tegal, Indonesia
(6) Department of Management, Universitas Negeri Semarang, Indonesia
Abstract
Purpose: Share price movements after the COVID-19 pandemic experienced a decline in several sectors, especially in the share prices of the Aneka Tambang Company, which operates in the mining sector, the Wijaya Karya Company in the construction sector, and the Sinar Mas Company, which is a Holding Company. Several factors influence this, including investors' hesitation in investing their money. This research aims to predict stock price movements using a Deep Learning algorithm, which is optimized using Selection optimization at three large companies in Indonesia, namely PT. ANTAM, PT. WIKA, and PT. SINAR MAS. So that it can provide the correct information to investors to avoid losses.
Method: research through collecting data from the three companies, preprocessing, and then analyzing research data with several alternatives. The combination of inputs from the three companies using the deep learning method is then optimized using selection optimization to produce the best accuracy and use the results of the RMSE evaluation.
Results: The results of this research show that by using the Deep Learning method, the best evaluation results were obtained for the Company PT Wijaya Karya with an RMSE value of 0.432, an MAE value of 0.31505 and an MSE value of 1913.953. These results were then optimized using Selection optimization to obtain an RMSE increase of 0.022, namely 0.410.
Novelty: The contribution of this research is to get the best combination of input variables obtained using the windowing process from the three companies, which are then processed using the Deep Learning method to produce the most accurate evaluation results from the three companies, then the results are optimized again using Selection optimization to get the more optimal results.
Keywords
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B. Sulistio and D. Suhartono, “Utilizing Bert and Cnn-Lstm in Stock Price Prediction Using Data Sentiment Analysis and Technical Analysis of Stock and Commodity,” ICIC Express Lett., vol. 17, no. 2, pp. 171–179, 2023, doi: 10.24507/icicel.17.02.171.
F. Ronaghi, M. Salimibeni, F. Naderkhani, and A. Mohammadi, “COVID19-HPSMP: COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction,” Expert Syst. Appl., vol. 187, p. 115879, 2022, doi: https://doi.org/10.1016/j.eswa.2021.115879.
Q. Wu, X. Liu, J. Qin, L. Zhou, A. Mardani, and M. Deveci, “An integrated generalized TODIM model for portfolio selection based on financial performance of firms,” Knowledge-Based Syst., vol. 249, 2022, doi: 10.1016/j.knosys.2022.108794.
W. Tarczyński, U. Mentel, G. Mentel, and U. Shahzad, “The influence of investors’ mood on the stock prices: Evidence from energy firms in warsaw stock exchange, poland,” Energies, vol. 14, no. 21, 2021, doi: 10.3390/en14217396.
I. E. Ahmed, R. Mehdi, and E. A. Mohamed, “The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS),” Artif. Intell. Rev., vol. 56, no. 11, pp. 13873–13895, 2023, doi: 10.1007/s10462-023-10473-9.
I. M. García-Sánchez and A. García-Sánchez, “Corporate social responsibility during COVID-19 pandemic,” J. Open Innov. Technol. Mark. Complex., vol. 6, no. 4, pp. 1–21, 2020, doi: 10.3390/joitmc6040126.
L. P. Ling and Y. Dasril, “Portfolio Selection Strategies in Bursa Malaysia Based on Quadratic Programming,” J. Inf. Syst. Explor. Res., vol. 1, no. 2, pp. 93–102, 2023, doi: 10.52465/joiser.v1i2.178.
N. Linder, S. Rosenthal, P. Sörqvist, and S. Barthel, “Internal and External Factors’ Influence on Recycling: Insights From a Laboratory Experiment With Observed Behavior,” Front. Psychol., vol. 12, no. July, pp. 1–12, 2021, doi: 10.3389/fpsyg.2021.699410.
B. Eichengreen, R. Hausmann, and U. Panizza, Yet it Endures: The Persistence of Original Sin, vol. 34, no. 1. Springer US, 2023. doi: 10.1007/s11079-022-09704-3.
V. K. Sen Liew, “The effect of novel coronavirus pandemic on tourism share prices,” J. Tour. Futur., vol. 8, no. 1, pp. 109–124, 2022, doi: 10.1108/JTF-03-2020-0045.
T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey, “Meta-Learning in Neural Networks: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 9, pp. 5149–5169, 2022, doi: 10.1109/TPAMI.2021.3079209.
X. Liu et al., “Self-Supervised Learning: Generative or Contrastive,” IEEE Trans. Knowl. Data Eng., vol. 35, no. 1, pp. 857–876, 2023, doi: 10.1109/TKDE.2021.3090866.
M. Endah H, R. N. Wijaya, and H. Khotibul Ahsan, “Enhancing cirrhosis detection: A deep learning approach with convolutional neural networks,” J. Soft Comput. Explor., vol. 4, no. 4, pp. 195–203, 2023, doi: 10.52465/joscex.v4i4.226.
D. H. Fudholi, S. Rani, D. M. Arifin, and M. R. Satyatama, “Deep Learning-based Mobile Tourism Recommender System,” Sci. J. Informatics, vol. 8, no. 1, pp. 111–118, 2021, doi: 10.15294/sji.v8i1.29262.
S. Kim et al., “Integration of neural network-based symbolic regression in deep learning for scientific discovery,” IEEE Trans. neural networks Learn. Syst., vol. 32, no. 9, pp. 4166–4177, 2020.
A. Mehonic, A. Sebastian, B. Rajendran, O. Simeone, E. Vasilaki, and A. J. Kenyon, “Memristors—From in‐memory computing, deep learning acceleration, and spiking neural networks to the future of neuromorphic and bio‐inspired computing,” Adv. Intell. Syst., vol. 2, no. 11, p. 2000085, 2020.
X. Wang, Y. Zhao, and F. Pourpanah, “Recent advances in deep learning,” Int. J. Mach. Learn. Cybern., vol. 11, pp. 747–750, 2020.
A. Dogan and D. Birant, “Machine learning and data mining in manufacturing,” Expert Syst. Appl., vol. 166, p. 114060, 2021, doi: 10.1016/j.eswa.2020.114060.
H. Jin, L. Wang, Z. Xiao, and H.-G. Fung, “What firm risk factors drive bank loan pricing and other terms? Evidence from China,” Account. Financ., vol. 63, no. 3, pp. 2985–3010, Sep. 2023, doi: https://doi.org/10.1111/acfi.13001.
A. Rai, S. R. Luwang, M. Nurujjaman, C. Hens, P. Kuila, and K. Debnath, “Detection and forecasting of extreme events in stock price triggered by fundamental, technical, and external factors,” Chaos, Solitons & Fractals, vol. 173, p. 113716, 2023, doi: https://doi.org/10.1016/j.chaos.2023.113716.
S. Mohan, S. Mullapudi, S. Sammeta, P. Vijayvergia, and D. C. Anastasiu, “Stock price prediction using news sentiment analysis,” Proc. - 5th IEEE Int. Conf. Big Data Serv. Appl. BigDataService 2019, Work. Big Data Water Resour. Environ. Hydraul. Eng. Work. Medical, Heal. Using Big Data Technol., pp. 205–208, 2019, doi: 10.1109/BigDataService.2019.00035.
T. Sidogi, R. Mbuvha, and T. Marwala, “Stock price prediction using sentiment analysis,” in 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, pp. 46–51.
M. Kim, J. Ryu, D. Cha, and M. K. Sim, “Stock Price Prediction Using Sentiment Analysis: from ‘Stock Discussion Room’ in Naver,” J. Soc. E-bus. Stud., vol. 25, no. 4, 2022.
C. S. Wu, M. Badshah, and V. Bhagwat, “Heart disease prediction using data mining techniques,” ACM Int. Conf. Proceeding Ser., pp. 7–11, 2019, doi: 10.1145/3352411.3352413.
P. Shetgaonkar and S. Aswale, “Heart disease prediction using data mining techniques,” Int. J. Eng. Res. Technol., vol. 10, no. 2, pp. 281–286, 2021.
L. Ji, X. Zhang, and L. Zhang, “Research on the algorithm of education data mining based on big data,” in 2020 IEEE 2nd International Conference on Computer Science and Educational Informatization (CSEI), 2020, pp. 344–350.
C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data,” Front. Energy Res., vol. 9, p. 652801, 2021.
E. Uncuoglu et al., “Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems,” Appl. Soft Comput., vol. 129, p. 109623, 2022.
T. Han, L. Zhang, Z. Yin, and A. C. C. Tan, “Rolling bearing fault diagnosis with combined convolutional neural networks and support vector machine,” Measurement, vol. 177, p. 109022, 2021.
A. Afradi and A. Ebrahimabadi, “Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate,” SN Appl. Sci., vol. 2, no. 12, pp. 1–16, 2020, doi: 10.1007/s42452-020-03767-y.
M. S. Bakay and Ü. Ağbulut, “Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms,” J. Clean. Prod., vol. 285, p. 125324, 2021, doi: https://doi.org/10.1016/j.jclepro.2020.125324.
A. Prasetyo and T. Faturohman, “Financial Distress and Financial Performance Analysis of Highway Companies Before and During the COVID-19 Pandemic: Evidence from Indonesia Stock Exchange,” in Comparative Analysis of Trade and Finance in Emerging Economies, vol. 31, W. A. Barnett and B. S. Sergi, Eds. Emerald Publishing Limited, 2023, pp. 151–165. doi: 10.1108/S1571-038620230000031021.
N. S. Utomo, S. S. Devi, and H. Siregar, “Financial Performance Analysis of Construction State-Owned Enterprises Listed in Indonesia Stock Exchange During Covid-19,” Amwaluna J. Ekon. Dan Keuang. Syariah, vol. 6, no. 2, pp. 244–268, 2022.
S. R. Ika, R. A. Nugroho, B. A. Santoso, N. F. Takril, and A. K. Widagdo, “Does the COVID-19 epidemic impact on economic sustainability of big agricultural firms in Indonesia?,” IOP Conf. Ser. Earth Environ. Sci., vol. 1241, no. 1, p. 12050, 2023, doi: 10.1088/1755-1315/1241/1/012050.
“Stock Price Movements PT. Antam.”
“Stcok Price Movement PT. WIKA.”
“Stock Price Movements PT. Sinar Mas.”
K. Chen, B. Xue, M. Zhang, and F. Zhou, “An Evolutionary Multitasking-Based Feature Selection Method for High-Dimensional Classification,” IEEE Trans. Cybern., vol. 52, no. 7, pp. 7172–7186, 2022, doi: 10.1109/TCYB.2020.3042243.
Z. Jin, Y. Yang, and Y. Liu, “Stock closing price prediction based on sentiment analysis and LSTM,” Neural Comput. Appl., vol. 32, pp. 9713–9729, 2020.
S. Parveen, Z. W. Satti, Q. A. Subhan, and S. Jamil, “Exploring market overreaction, investors’ sentiments and investment decisions in an emerging stock market,” Borsa Istanbul Rev., vol. 20, no. 3, pp. 224–235, 2020.
P. Mishra, A. Biancolillo, J. M. Roger, F. Marini, and D. N. Rutledge, “New data preprocessing trends based on ensemble of multiple preprocessing techniques,” TrAC Trends Anal. Chem., vol. 132, p. 116045, 2020, doi: https://doi.org/10.1016/j.trac.2020.116045.
Y. Yang et al., “Clinical characteristics of hospitalized mild/moderate COVID-19 patients with a prolonged negative conversion time of SARS-CoV-2 nucleic acid detection,” BMC Infect. Dis., vol. 21, pp. 1–8, 2021.
S. Rasp, P. D. Dueben, S. Scher, J. A. Weyn, S. Mouatadid, and N. Thuerey, “WeatherBench: a benchmark data set for data‐driven weather forecasting,” J. Adv. Model. Earth Syst., vol. 12, no. 11, p. e2020MS002203, 2020.
S. Wu, W.-C. Yau, T.-S. Ong, and S.-C. Chong, “Integrated churn prediction and customer segmentation framework for telco business,” IEEE Access, vol. 9, pp. 62118–62136, 2021.
B. G. Marcot and A. M. Hanea, “What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?,” Comput. Stat., vol. 36, no. 3, pp. 2009–2031, 2021.
H. L. Vu, K. T. W. Ng, A. Richter, and C. An, “Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation,” J. Environ. Manage., vol. 311, p. 114869, 2022.
D.-C. Feng et al., “Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach,” Constr. Build. Mater., vol. 230, p. 117000, 2020.
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