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International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-9 Issue-95 October-2022
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Paper Title : Stock market prediction in Bangladesh perspective using artificial neural network
Author Name : Md. Ashikur Rahman Khan, Md. Furkan Uzzaman, Ishtiaq Ahammad, Ratul Prosad, Zayed Us Salehin, Tanvir Zaman Khan, Md. Sabbir Ejaz and Main Uddin
Abstract :

Stock market price prediction is now a prominent and significant issue in financial and academic studies as the stock market plays a vital role in the economy. The process of attempting to anticipate the future valuation of a company's share is known as stock market price prediction. Share prices are time-series information, and artificial neural networks (ANNs) can uncover non-linear associations among time-series information. This makes ANN the best method for predicting stock market values. Many researchers are working on this topic and trying to find the best algorithm which is suitable for predicting the stock price. But significant improvement in prediction is still not achieved. Therefore, in this work, an ANN model is proposed and implemented by using a multilayer feedforward backpropagation method. In this work, data of fifteen companies over a six years span have been analyzed. To predict the specific result, the proposed model has been trained with four different algorithms: Levenberg Marquardt (LM), Bayesian regularization (BR), scaled conjugate gradient (SCG) and Quasi Newton) by changing their parameters. Number of hidden layers, hidden neurons and percentage of training data have been changed to get better output. The split ratio of training, testing and validation data sets is 70:15:15. The projected results are then compared to the actual data after the training and testing procedure to determine the accuracy. The accuracy of LM is 95.64%, BR is 91.26%, SCG accuracy is 88.91% and Quasi Newton is 84.20%. The result showed that, LM algorithm provides better accuracy than other models. In addition, less error has been found from the LM algorithm, making it the best algorithm for prediction in our proposed model.
Keywords : Stock market, Price prediction, Artificial neural network, Levenberg Marquardt(LM), Bayesian regularization(BR), Scaled conjugate gradient(SCG), Quasi newton.
Cite this article : Khan MA, Uzzaman MF, Ahammad I, Prosad R, Salehin ZU, Khan TZ, Ejaz MS, Uddin M. Stock market prediction in Bangladesh perspective using artificial neural network . International Journal of Advanced Technology and Engineering Exploration. 2022; 9(95):1397-1427. DOI:10.19101/IJATEE.2021.875852.
References :
[1]Hiransha M, Gopalakrishnan EA, Menon VK, Soman KP. NSE stock market prediction using deep-learning models. Procedia Computer Science. 2018; 132:1351-62.
[Crossref] [Google Scholar]
[2]https://www.statista.com/statistics/274490/global-value-of-share-holdings-since 2000/#:~:text=This% 20statistic%20presents%20the%20global,U.S.%20dollars%20in%20H1%202021. Accessed 18 September 2022.
[3]https://www.ceicdata.com/en/indicator/bangladesh/market-capitalization. Accessed 18 September 2022.
[4]Upadhyay A, Bandyopadhyay G, Dutta A. Forecasting stock performance in indian market using multinomial logistic regression. Journal of Business Studies Quarterly. 2012; 3(3):16-39.
[Google Scholar]
[5]Tan TZ, Quek C, Ng GS. Biological brain‐inspired genetic complementary learning for stock market and bank failure prediction. Computational Intelligence. 2007; 23(2):236-61.
[Crossref] [Google Scholar]
[6]Ahammad I, Khan MA, Salehin ZU. Advancement of IoT system QoS by integrating cloud, fog, roof, and dew computing assisted by SDN: basic framework architecture and simulation. International Journal of Ambient Computing and Intelligence. 2021; 12(4):132-53.
[Google Scholar]
[7]Nayak A, Pai MM, Pai RM. Prediction models for Indian stock market. Procedia Computer Science. 2016; 89:441-9.
[Crossref] [Google Scholar]
[8]Falat L, Pancikova L. Quantitative modelling in economics with advanced artificial neural networks. Procedia Economics and Finance. 2015; 34:194-201.
[Crossref] [Google Scholar]
[9]Ferreira FG, Gandomi AH, Cardoso RT. Artificial intelligence applied to stock market trading: a review. IEEE Access. 2021; 9:30898-917.
[Crossref] [Google Scholar]
[10]Naeini MP, Taremian H, Hashemi HB. Stock market value prediction using neural networks. In2010 international conference on computer information systems and industrial management applications 2010 (pp. 132-6). IEEE.
[Crossref] [Google Scholar]
[11]Grudnitski G, Osburn L. Forecasting S&P and gold futures prices: An application of neural networks. Journal of Futures Markets. 1993; 13(6):631-43.
[Google Scholar]
[12]http://www.dse.com. Accessed 18 September 2022.
[13]https://www.investing.com/. Accessed 18 September 2022.
[14]Ahammad I, Khan AR, Salehin ZU. A review on cloud, fog, roof, and dew computing: IoT perspective. International Journal of Cloud Applications and Computing. 2021; 11(4):14-41.
[Crossref] [Google Scholar]
[15]Soni P, Tewari Y, Krishnan D. Machine learning approaches in stock price prediction: a systematic review. In journal of physics: conference series 2022 (pp. 1-10). IOP Publishing.
[Crossref] [Google Scholar]
[16]Rouf N, Malik MB, Arif T, Sharma S, Singh S, Aich S, Kim HC. Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics. 2021; 10(21):2717.
[Crossref] [Google Scholar]
[17]Strader TJ, Rozycki JJ, Root TH, Huang YH. Machine learning stock market prediction studies: Review and research directions. Journal of International Technology and Information Management. 2020; 28(4):63-83.
[Google Scholar]
[18]Ketsetsis AP, Kourounis C, Spanos G, Giannoutakis KM, Pavlidis P, Vazakidis D, et al. Deep learning techniques for stock market prediction in the European union: a systematic review. In international conference on computational science and computational intelligence 2020 (pp. 605-10). IEEE.
[Crossref] [Google Scholar]
[19]Patel R, Choudhary V, Saxena D, Singh AK. Review of stock prediction using machine learning techniques. In international conference on trends in electronics and informatics 2021 (pp. 840-6). IEEE.
[Crossref] [Google Scholar]
[20]Vadlamudi S. Stock market prediction using machine learning: a systematic literature review. American Journal of Trade and Policy. 2017; 4(3):123-8.
[Crossref] [Google Scholar]
[21]Pyo S, Lee J, Cha M, Jang H. Predictability of machine learning techniques to forecast the trends of market index prices: hypothesis testing for the Korean stock markets. PloS One. 2017; 12(11).
[Crossref] [Google Scholar]
[22]Anwar M, Rahman S. Forecasting stock market prices using advanced tools of machine learning (Doctoral Dissertation, Brac University).
[Google Scholar]
[23]Reddy VK. Stock market prediction using machine learning. International Research Journal of Engineering and Technology. 2018; 5(10):1033-5.
[Google Scholar]
[24]Chen K, Zhou Y, Dai F. A LSTM-based method for stock returns prediction: a case study of China stock market. In international conference on big data (pp. 2823-4). IEEE.
[Crossref] [Google Scholar]
[25]Yetis Y, Kaplan H, Jamshidi M. Stock market prediction by using artificial neural network. In world automation congress 2014 (pp. 718-22). IEEE.
[Crossref] [Google Scholar]
[26]Selvamuthu D, Kumar V, Mishra A. Indian stock market prediction using artificial neural networks on tick data. Financial Innovation. 2019; 5(1):1-12.
[Crossref] [Google Scholar]
[27]Prastyo A, Junaedi D, Sulistiyo MD. Stock price forecasting using artificial neural network:(Case Study: PT. Telkom Indonesia). In international conference on information and communication technology 2017 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[28]Tsai CF, Wang SP. Stock price forecasting by hybrid machine learning techniques. In proceedings of the international multiconference of engineers and computer scientists 2009.
[Google Scholar]
[29]Vijh M, Chandola D, Tikkiwal VA, Kumar A. Stock closing price prediction using machine learning techniques. Procedia Computer Science. 2020; 167:599-606.
[Crossref] [Google Scholar]
[30]Kannan KS, Sekar PS, Sathik MM, Arumugam P. Financial stock market forecast using data mining techniques. In proceedings of the international multiconference of engineers and computer scientists 2010 (pp. 1-5).
[Google Scholar]
[31]Wang H, Wang J, Cao L, Li Y, Sun Q, Wang J. A stock closing price prediction model based on CNN-bislstm. Complexity. 2021.
[Crossref] [Google Scholar]
[32]Milosevic N. Equity forecast: predicting long term stock price movement using machine learning. arXiv preprint arXiv:1603.00751. 2016.
[Crossref] [Google Scholar]
[33]Shen J, Shafiq MO. Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of Big Data. 2020; 7(1):1-33.
[Crossref] [Google Scholar]
[34]Shen S, Jiang H, Zhang T. Stock market forecasting using machine learning algorithms. Department of Electrical Engineering, Stanford University, Stanford, CA. 2012:1-5.
[Google Scholar]
[35]Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research. 2018; 270(2):654-69.
[Crossref] [Google Scholar]
[36]Attigeri GV, MM MP, Pai RM, Nayak A. Stock market prediction: a big data approach. In TENCON 2015 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[37]Kirkpatrick II CD, Dahlquist JA. Technical analysis: the complete resource for financial market technicians. FT Press; 2010.
[Google Scholar]
[38]Saad EW, Prokhorov DV, Wunsch DC. Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks. 1998; 9(6):1456-70.
[Crossref] [Google Scholar]
[39]Vlasenko A, Vlasenko N, Vynokurova O, Bodyanskiy Y, Peleshko D. A novel ensemble neuro-fuzzy model for financial time series forecasting. Data. 2019; 4(3):1-11.
[Crossref] [Google Scholar]
[40]Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State-of-the-art in artificial neural network applications: a survey. Heliyon. 2018; 4(11):1-41.
[Crossref] [Google Scholar]
[41]Hemeida AM, Hassan SA, Mohamed AA, Alkhalaf S, Mahmoud MM, Senjyu T, et al. Nature-inspired algorithms for feed-forward neural network classifiers: a survey of one decade of research. Ain Shams Engineering Journal. 2020; 11(3):659-75.
[Crossref] [Google Scholar]
[42]De Jesús Rubio J. Stability analysis of the modified Levenberg–Marquardt algorithm for the artificial neural network training. IEEE Transactions on Neural Networks and Learning Systems. 2020; 32(8):3510-24.
[Crossref] [Google Scholar]
[43]Ticknor JL. A bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications. 2013; 40(14):5501-6.
[Crossref] [Google Scholar]
[44]Farizawani AG, Puteh M, Marina Y, Rivaie A. A review of artificial neural network learning rule based on multiple variant of conjugate gradient approaches. In journal of physics: conference series 2020 (pp. 1-13). IOP Publishing.
[Crossref] [Google Scholar]
[45]Liu Q, Liu J, Sang R, Li J, Zhang T, Zhang Q. Fast neural network training on FPGA using Quasi-Newton optimization method. IEEE Transactions on Very Large Scale Integration Systems. 2018; 26(8):1575-9.
[Crossref] [Google Scholar]