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Time Series Analysis of National Stock Exchange: A Multivariate Data Science Approach

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Soft Computing for Problem Solving

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 547))

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Abstract

Stock price prediction attracts individual decisions to invest in share market and may encourage the common people to become active in share trading. Stock trading gives us direct monetary benefit under prevalent uncertainties. Factors like political developments at state, national and international levels, conservative, diverse and complex social conditions, natural disasters, famine, pandemics, economic trade cycle (recession, boom and recursion) and many others have great effects on share prices and stock cost. The present work is based on secondary data sources acquisitioned from various public and private data portals accessed freely. There have been various efforts made in the past to predict the trends of the stock prices on the basis of secondary data. The predicted confidence estimates may enable the common investors to make a profit despite large risk of loss at different point of time under the dynamics of market fluctuations. The present work is based on an inferential methodology, which has been found to be instrumental and particularly suitable to the financial time series analysis. Time series modelling and forecasting have fundamental importance in stock market prediction and analysis. Further, with the application of multivariate methods and regression modelling, we expect better forecast accuracy. The implementation of the proposed approach has been incorporated on real-time data set on daily basis. The software used for various computations is Python, SPSS, MS Excel and MS Solver. The current work is relevant in prediction and analysis of the stock market prices and also seems to be useful to a large number of peoples engaged in day to day trading.

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Author information

Authors and Affiliations

  1. School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    G. Venkata Manish Reddy,  Iswarya & Jitendra Kumar

  2. Department of Computer Science and Engineering, Indian Institute of Information Technology Bhagalpur, Bhagalpur, Bihar, India

    Dilip Kumar Choubey

Authors
  1. G. Venkata Manish Reddy
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  2. Iswarya
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  3. Jitendra Kumar
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  4. Dilip Kumar Choubey
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Corresponding author

Correspondence to Jitendra Kumar .

Editor information

Editors and Affiliations

  1. School of Mathematical and Statistical Sciences, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Manoj Thakur

  2. School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Samar Agnihotri

  3. School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Bharat Singh Rajpurohit

  4. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  5. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  6. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

Glossary

ARIMA

Autoregressive integrated moving average

BLUE

Best linear unbiased estimator

CV

Coefficient of variation

SD

Standard deviation

MSE

Mean squared error

RMSE

Root mean squared error

.

Multicollinearity

.

Coefficient of determination (\(R^{2}\))

.

Pearson correlation coefficient

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Venkata Manish Reddy, G., Iswarya, Kumar, J., Choubey, D.K. (2023). Time Series Analysis of National Stock Exchange: A Multivariate Data Science Approach. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_53

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