Abstract
In the present era, stock market has become the storyteller of all the financial activities of any country. Therefore, stock market has become the place of high risks, but even then it is attracting the mass because of its high return value. Stock market tells about the economy of any country and has become one of the biggest investment places for the general public. In this manuscript, we present the various forecasting approaches and linear regression algorithm to successfully predict the Bombay Stock Exchange (BSE) SENSEX value with high accuracy. Depending upon the analysis performed, it can be said successfully that linear regression in combination with different mathematical functions produces the best results. This model gives the best output with BSE SENSEX values and gross domestic product (GDP) values as it shows the least p-value as 5.382e−10 when compared with other model’s p-values.
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
References
Armstrong JS (2001) Combining forecasts. In: Principles of forecasting. Springer, Boston, MA, pp 417–439
Frick RW (1996) The appropriate use of null hypothesis testing. Psychol Method 1(4):379
Cleveland WP, Tiao GC (1976) Decomposition of seasonal time series: a model for the census X-11 program. J Am Stat Assoc 71(355):581–587
Sharma N, Juneja A (2017) Combining of random forest estimates using LSboost for stock market index prediction. In: 2nd international conference for convergence in technology (I2CT). IEEE
Mondal P, Shit L, Goswami S (2014) Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. Int J Comput Sci Eng Appl 4(2):13
Rao A, et al (2015) Survey: stock market prediction using statistical computational methodologies and artificial neural networks
Cole R (1969) Data errors and forecasting accuracy. In: Economic forecasts and expectations: analysis of forecasting behavior and performance. NBER, pp 47–82
Litterman RB (1986) A statistical approach to economic forecasting. J Bus Econ Stat 4(1):1–4
Alam P (2016) Factors affecting stock market in India. Spl Int J Prof 3(9):7
Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis vol. 821. Wiley
Sapankevych NI, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag 4(2)
Devers KJ, Frankel RM (2000) Study design in qualitative research–2: sampling and data collection strategies. Educ Health 13(2):263
Rahm E, Do HH (2000) Data cleaning: problems and current approaches. IEEE Data Eng Bull 23(4):3–13
Angadi MC, Kulkarni AP (2015) Time series data analysis for stock market prediction using data mining techniques with R. Int J Adv Res Comput Sci 6(6)
BSE Homepage, http://www.bseindia.com. Last accessed 2018/07/05
The Worldwide Inflation Data Homepage, http://www.inflation.eu. Last accessed 2018/07/10
The World Bank Homepage, http://www.worldbank.org. Last accessed 2018/07/10
The Reserve Bank of India Homepage, https://www.rbi.org.in. Last accessed 2018/07/10
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yadav, S., Sharma, N. (2019). Forecasting of Indian Stock Market Using Time-Series Models. In: Peng, SL., Dey, N., Bundele, M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-13-7150-9_43
Download citation
DOI: https://doi.org/10.1007/978-981-13-7150-9_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7149-3
Online ISBN: 978-981-13-7150-9
eBook Packages: EngineeringEngineering (R0)