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A Historical Analysis of the US Stock Price Index using Empirical Mode Decomposition over 1791-2015

Author

Listed:
  • Aviral K. Tiwari

    () (Faculty of Management, IBS Hyderabad, IFHE University)

  • Arif B. Dar

    () (Institute of Management Technology, Rajnagar, Ghaziabad, Delhi, 201001, India)

  • Niyati Bhanja

    () (Department of Economics and IB UPES, Dehradun India)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

Abstract

In this paper, the dynamics of Standard and Poor's 500 (S&P 500) stock price index is analysed within the time-frequency framework over a monthly period of 1791:08-2015:05. Using the Empirical Mode Decomposition technique, the S&P 500 stock price index is divided into different frequencies known as intrinsic mode functions (IMFs) and one residual. The IMFs and the residual are then reconstructed into high frequency, low frequency and trend components using the hierarchical clustering method. Using different measures, it is shown that the low frequency and trend components of the stock prices are relatively important drivers of the S&P 500 index. These results are also robust across various sub-samples identified based on structural break tests.The US stock prices are, therefore, mostly driven by fundamental laws rooted in economic growth and long-term returns on investment.

Suggested Citation

  • Aviral K. Tiwari & Arif B. Dar & Niyati Bhanja & Rangan Gupta, 2015. "A Historical Analysis of the US Stock Price Index using Empirical Mode Decomposition over 1791-2015," Working Papers 201588, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201588
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    References listed on IDEAS

    as
    1. Cheng, Ching-Hsue & Wei, Liang-Ying, 2014. "A novel time-series model based on empirical mode decomposition for forecasting TAIEX," Economic Modelling, Elsevier, vol. 36(C), pages 136-141.
    2. Tsangyao Chang & Xiao-lin Li & Stephen M. Miller & Mehmet Balcilar & Rangan Gupta, 2013. "The Co-Movement and Causality between the U.S. Real Estate and Stock Markets in the Time and Frequency Domains," Working Papers 201365, University of Pretoria, Department of Economics.
    3. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    4. Kumar Tiwari, Aviral & Billah Dar, Arif & Bhanja, Niyati & Shah, Aasif, 2013. "Stock Market Integration in Asian Countries: evidence from Wavelet multiple correlations," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 28, pages 441-456.
    5. Tiwari, Aviral Kumar & Dar, Arif Billah & Bhanja, Niyati, 2013. "Oil price and exchange rates: A wavelet based analysis for India," Economic Modelling, Elsevier, vol. 31(C), pages 414-422.
    6. Bangzhu Zhu & Ping Wang & Julien Chevallier & Yiming Wei, 2015. "Carbon Price Analysis Using Empirical Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 195-206, February.
    7. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    8. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
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    Cited by:

    1. Jihong Xiao & Xuehong Zhu & Chuangxia Huang & Xiaoguang Yang & Fenghua Wen & Meirui Zhong, 2019. "A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 287-310, January.

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    More about this item

    Keywords

    Empirical Mode Decomposition; Stock Prices; S&P 500 Index; United States;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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