Abstract
The stock market price is one of the best indicators to showcase economic status of a country. As it is the concern of investors and the companies, there is a necessity to have a strong monitoring mechanism to predict and track the share prices. Though the prices are dynamic in nature and the learning curve is changing rapidly, robust mechanism has to be in place to facilitate prediction. Machine learning, a technique of learning using past data, seems to be a perfect platform which can capture the dynamic and changing landscape of stock market prices. Among the multiple analysis methods used in the market for prediction, time series analysis is a novel method in predicting the share prices. This study has attempted to predict the share price of ICICI Bank Limited, the private multinational banking and financial services firm using traditional time series method ‘autoregressive integrated moving average’ (ARIMA) and machine learning algorithm ‘artificial neural network’ (ANN) to understand the best method for prediction. The predictive performance of the both the models was compared using root mean-squared error metric. The findings of the study showed that the accuracy of traditional ARIMA model comparatively better and hold good for predicting the share price.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dassanayake W, Jayawardena C, Ardekani I, Sharifzadeh H (2019) Models applied in stock market prediction: a literature survey
Jiang W (2020) Applications of deep learning in stock market prediction: recent progress. arXiv preprint arXiv:2003.01859
Deng S, Mitsubuchi T, Shioda K, Shimada T, Sakurai A (2011) Combining technical analysis with sentiment analysis for stock price prediction. In: 2011 IEEE ninth international conference on dependable, autonomic and secure computing, pp 800–807
Chen Y, Cheng C (2007) Forecasting revenue growth rate using fundamental analysis: a feature selection based rough sets approach. In: Fourth international conference on fuzzy systems and knowledge discovery (FSKD 2007), vol 3, pp 151–155
Zhang J, Chung HS, Lo W (2008) Chaotic time series prediction using a NeuroFuzzy system with time-delay coordinates. IEEE Trans Knowl Data Eng 20(7)
Yule GU (1927) On a method of investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. In: Philosophical transactions of the royal society of London. Series A, containing papers of a mathematical or physical character, vol 226, pp 267–298
Slutzky E (1937) The summation of random causes as the source of cyclic processes. Econometrica: J Econometr Soc 105–146
Kumar M, Thenmozhi M (2014) Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models. Int J Bank Account Finan 5(3):284–308
Engle RF (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica 50:987–1008
Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econometr 31(3):307–327
Neto MCA, Calvalcanti GD, Ren TI (2009) Financial time series prediction using exogenous series and combined neural networks. In: 2019 International joint conference on neural networks June 149–156, IEEE
Ince H, Trafalis TB (2007) Kernel principal component analysis and support vector machines for stock price prediction. IIE Trans 39(6):629–637
Zhang N, Lin A, Shang P (2017) Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. Physica A 477:161–173
Kohara K, Ishikawa T, Fukuhara Y, Nakamura Y (1997) Stock price prediction using prior knowledge and neural networks. Intell Syst Account Finan Manag 6(1):11–22
Krauss C, Do XA, Huck N (2017) Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. Eur J Oper Res 259(2):689–702
Musa Y, Joshua S (2020) Analysis of ARIMA-artificial neural network hybrid model in forecasting of stock market returns. Asian J Probab Stati 42–53
Kim Y, Jeong SR, Ghani I (2014) Text opinion mining to analyze news for stock market prediction. Int J Adv Soft Comput Appl 6(1)
Sahoo S, Mohanty MN (2020) Stock market price prediction employing artificial neural network optimized by gray wolf optimization. In: New paradigm in decision science and management, pp 77–87. Springer, Singapore
Nabipour M, Nayyeri P, Jabani H, Mosavi A (2020) Deep learning for stock market prediction. arXiv preprint arXiv:2004.01497
Jeon S, Hong B, Kim J, Lee HJ (2016) Stock price prediction based on stock big data and pattern graph analysis. In IoTBD, pp 223–231
Henrique BM, Sobreiro VA, Kimura H (2018) Building direct citation networks. Scientometrics 115(2):817–832
Ayub S, Jafri YZ (2020) Comparative study of an ANN-ARIMA hybrid model for predicting Karachi stock price. Am J Math Stat 10(1):1–9
Cao Q, Leggio KB, Schniederjans MJ (2005) A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Comput Oper Res 32(10):2499–2512
Pandey, Bajpai (2019) Predictive efficiency of ARIMA and ANN models: a case analysis of nifty fifty in Indian stock market. Int J Appl Eng Res 14(2):232–244
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Uma Maheswari, B., Sujatha, R., Fantina, S., Mansurali, A. (2021). ARIMA Versus ANN—A Comparative Study of Predictive Modelling Techniques to Determine Stock Price. In: Goyal, D., Gupta, A.K., Piuri, V., Ganzha, M., Paprzycki, M. (eds) Proceedings of the Second International Conference on Information Management and Machine Intelligence. Lecture Notes in Networks and Systems, vol 166. Springer, Singapore. https://doi.org/10.1007/978-981-15-9689-6_35
Download citation
DOI: https://doi.org/10.1007/978-981-15-9689-6_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9688-9
Online ISBN: 978-981-15-9689-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)