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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Glossary
- ARIMA
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Autoregressive integrated moving average
- BLUE
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Best linear unbiased estimator
- CV
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Coefficient of variation
- SD
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Standard deviation
- MSE
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Mean squared error
- RMSE
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Root mean squared error
- .
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Multicollinearity
- .
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Coefficient of determination (\(R^{2}\))
- .
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Pearson correlation coefficient
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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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DOI: https://doi.org/10.1007/978-981-19-6525-8_53
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