Keshri, Abhinav and Sharma, Charu (2020): Exploratory Analysis of Functional Principal Components to Observe the Absorption of Election Sentiments in the Indian Stock Market.
Preview |
PDF
MPRA_paper_122325.pdf Download (383kB) | Preview |
Abstract
An election year is expected to be of high volatility and movement in the stock markets, reflecting the aspirations and expectations of the common people from the upcoming government. In this paper, we explore Functional Principal Component Analysis to show how a big event like the general elections affects the stock market in the country. We take the Indian general election years of 2009, 2014, and 2019 and demonstrate how the unique circumstances before the elections affect the absorption of election sentiments and how this method can be used to find and foresee the effect of other such events through a detailed analysis of eigenfunctions.
Item Type: | MPRA Paper |
---|---|
Original Title: | Exploratory Analysis of Functional Principal Components to Observe the Absorption of Election Sentiments in the Indian Stock Market |
Language: | English |
Keywords: | PCA, FPCA, General elections effect, Functional Data Analysis |
Subjects: | C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading |
Item ID: | 122325 |
Depositing User: | Abhinav Keshri |
Date Deposited: | 16 Oct 2024 13:22 |
Last Modified: | 16 Oct 2024 13:22 |
References: | Baker, S. R., Bloom, N., & Davis, S. J. (2013). Measuring economic policy uncertainty. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2198490 Barbi, A. Q., & Prataviera, G. A. (2019). Nonlinear dependencies on Brazilian equity network from mutual information minimum spanning trees. Physica A: Statistical Mechanics and its Applications, 523, 876–885. https://doi.org/10.1016/j.physa.2019.04.147 Bialkowski, J., Gottschalk, K., & Wisniewski, T. P. (2008). Stock market volatility around national elections. Journal of Banking & Finance, 32(9), 1941–1953. https://doi.org/10.1016/j.jbankfin.2007.12.021 Fiedor, P. (2014). Networks in financial markets based on the mutual information rate. Physical Review E, 89(5). https://doi.org/10.1103/physreve.89.052801 Gervini, D., Cao, J., & Ramsay, J. O. (2007). Parameter cascades and profiling in functional data analysis. Computational Statistics. Müller, H.-G., Sen, R., & Stadtmüller, U. (2011). Functional data analysis for volatility. Journal of Econometrics, 165(2), 233–245. https://doi.org/10.1016/j.jeconom.2011.08.002 National Stock Exchange of India. (2019). Nifty 100 index. https://www.nseindia.com/products/content/equities/indices/nifty100.html Pastor, L., & Veronesi, P. (2013). Political uncertainty and risk premia. Journal of Financial Economics, 110(3), 520–545. https://doi.org/10.1016/j.jfineco.2013.08.007 Ramsay, J. O. (n.d.). Functional data analysis: Weather example. http://www.psych.mcgill.ca/misc/fda/ex-weather-c1.html Ramsay, J. O. (1998). Estimating smooth monotone functions. Journal of the Royal Statistical Society. Ramsay, J. O. (2000). Functional components of variation in handwriting. Journal of the American Statistical Association. Ramsay, J. O., Munhall, K. G., Gracco, V. L., & Ostry, D. J. (1996). Functional data analysis of lip motion. Journal of the Acoustical Society of America. Ramsay, J. O., & Ramsey, J. B. (2001). Functional data analysis of the dynamics of the monthly index of nondurable goods production. Journal of Econometrics. Silverman, B. W., Ramsay, J. O., & Heckman, N. E. (1997). Spline smoothing with model-based penalties. Behavior Research Methods, Instruments, and Computers. Wang, Z., Sun, Y., & Li, P. (2014). Functional principal components analysis of Shanghai stock exchange 50 index. Discrete Dynamics in Nature and Society, 2014, 1–7. https://doi.org/10.1155/2014/365204 Zhou, Y., Bhattacharjee, S., Carroll, C., Chen, Y., Dai, X., Fan, J., Gajardo, A., Hadjipantelis, P. Z., Han, K., Ji, H., Zhu, C., Müller, H.-G., & Wang, J.-L. (2022). Fdapace: Functional data analysis and empirical dynamics [R package version 0.5.9]. https://CRAN.R-project.org/package=fdapace |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/122325 |