Abstract
Stock price prediction is one of the challenging tasks for researchers and academics due to frequent changes in stock prices. The stock prices are speculation, and it purely depends on the demand and supply of the market during the trading session. Most of the existing work approach is foresting stock prices using machine learning methods. There has been a limited number of studies on stock crisis identification. Log periodic power law (LPPL) is one of the approaches to identify bubbles in the stock market before crises happened. By looking at existing work, we found that LPPL has not applied in the Indian stock market. In this paper, we have considered LPPL to identify a bubble in the Indian stock market. Due to fluctuation in the market, stock price follows the nonlinearity behavior, hence LPPL is considered to fit the equations. The experiment is carried out R Studio platform.
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
Chatzis, S.P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., Vlachogiannakis, N.: Forecasting stock market crisis events using deep and statistical machine learning techniques. Exp. Syst. Appl. 112, 353–371 (2018)
Enke, D., Thawornwong, S.: The use of data mining and neural networks for forecasting stock market returns. Exp. Syst. Appl. 29(4), 927–940 (2005)
Filimonov, V., Sornette, D.: A stable and robust calibration scheme of the log-periodic power law model. Phys. A Stat. Mech. Appl. 392(17), 3698–3707 (2013)
Huang, C.-J., Yang, D.-X., Chuang, Y.-T.: Application of wrapper approach and composite classifier to the stock trend prediction. Exp. Syst. Appl. 34(4), 2870–2878 (2008)
Emilie, J.: How to predict crashes in financial markets with the log-periodic power law. Department of Mathematical Statistics, Stockholm University, Master disseration (2009)
Johansen, A., Sornette, D.: Log-periodic power law bubbles in latin-american and asian markets and correlated anti-bubbles in western stock markets: an empirical study. arXiv preprint cond-mat/9907270 (1999)
Kara, Y., Boyacioglu, M.A., Baykan, O.K.: Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange. Exp. Syst. Appl. 38(5), 5311–5319 (2011)
Kristjanpoller, W., Minutolo, M.C.: A hybrid volatility forecasting framework integrating garch, artificial neural network, technical analysis and principal components analysis. Exp. Syst. Appl. 109, 1–11 (2018)
Li, C.: Log-periodic view on critical dates of the Chinese stock market bubbles. Phys. A Stat. Mech. Appl. 465, 305–311 (2017)
Long, W., Zhichen, L., Cui, L.: Deep learning-based feature engineering for stock price movement prediction. Knowle.-Based Syst. 164, 163–173 (2019)
Nikolaev, N.Y., Boshnakov, G.N., Zimmer, R.: Heavy-tailed mixture garch volatility modeling and value-at-risk estimation. Exp. Syst. Appl. 40(6), 2233–2243 (2013)
Orhan, M., Köksal, B.: A comparison of garch models for var estimation. Exp. Syst. Appl. 39(3), 3582–3592 (2012)
Patel, J., Shah, S., Thakkar, P., Kotecha, K.: Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Exp. Syst. Appl. 42(1), 259–268 (2015)
Sornette, D.: Dragon-kings, black swans and the prediction of crises. arXiv preprint arXiv:0907.4290 (2009)
Jan Henrik Wosnitza and Cornelia Denz: Liquidity crisis detection: an application of log-periodic power law structures to default prediction. Phys. A Stat. Mech. Appl. 392(17), 3666–3681 (2013)
Yu, P., Yan, X.: Stock price prediction based on deep neural networks. Neural Comput. Appl., pp. 1–20 (2019)
Zhang, Q., Zhang, Q., Sornette, D.: Early warning signals of financial crises with multi-scale quantile regressions of log-periodic power law singularities. PloS one 11(11), e0165819 (2016)
Zhong, X., Enke, D.: Forecasting daily stock market return using dimensionality reduction. Exp. Syst. Appl. 67, 126–139 (2017)
Acknowledgment
This work is supported by MeitY, Government of India.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Naik, N., Mohan, B.R. (2020). Log Periodic Power Law Fitting on Indian Stock Market. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_4
Download citation
DOI: https://doi.org/10.1007/978-981-15-6318-8_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6317-1
Online ISBN: 978-981-15-6318-8
eBook Packages: Computer ScienceComputer Science (R0)