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Profiteering from the Dot-com Bubble, Sub-Prime Crisis and Asian Financial Crisis

Author

Listed:
  • Michael McAleer

    (Erasmus University Rotterdam, Complutense University of Madrid, Kyoto University)

  • John Suen

    (Chinese University of Hong Kong)

  • Wing Keung Wong

    (Hong Kong Baptist University)

Abstract

This paper explores the characteristics associated with the formation of bubbles that occurred in the Hong Kong stock market in 1997 and 2007, as well as the 2000 dot-com bubble of Nasdaq. It examines the profitability of Technical Analysis (TA) strategies generating buy and sell signals with knowing and without trading rules. The empirical results show that by applying long and short strategies during the bubble formation and short strategies after the bubble burst, it not only produces returns that are significantly greater than buy and hold strategies, but also produces greater wealth compared with TA strategies without trading rules. We conclude these bubble detection signals help investors generate greater wealth from applying appropriate long and short Moving Average (MA) strategies.

Suggested Citation

  • Michael McAleer & John Suen & Wing Keung Wong, 2013. "Profiteering from the Dot-com Bubble, Sub-Prime Crisis and Asian Financial Crisis," Tinbergen Institute Discussion Papers 13-077/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20130077
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    References listed on IDEAS

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    2. Kai-Yin Woo & Chulin Mai & Michael McAleer & Wing-Keung Wong, 2020. "Review on Efficiency and Anomalies in Stock Markets," Economies, MDPI, vol. 8(1), pages 1-51, March.
    3. Ayesha Liaqat & Mian Sajid Nazir & Iftikhar Ahmad, 2019. "Identification of multiple stock bubbles in an emerging market: application of GSADF approach," Economic Change and Restructuring, Springer, vol. 52(3), pages 301-326, August.
    4. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2015. "Behavioural, Financial, and Health & Medical Economics: A Connection," Documentos de Trabajo del ICAE 2015-14, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    5. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," JRFM, MDPI, vol. 11(1), pages 1-29, March.
    6. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, And Big Data: Connections," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 36-94, December.
    7. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2018. "Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections," Econometric Institute Research Papers 18-024/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Edward C. H. Tang, 2024. "Examining the Impacts of the Pandemic on the Housing Bubble in Hong Kong," Advances in Decision Sciences, Asia University, Taiwan, vol. 28(1), pages 27-46, March.
    9. Suchismita Mishra & Le Zhao, 2021. "Order Routing Decisions for a Fragmented Market: A Review," JRFM, MDPI, vol. 14(11), pages 1-32, November.
    10. Ayesha Liaqat & Mian Sajid Nazir & Iftikhar Ahmad & Hammad Hassan Mirza & Farooq Anwar, 2020. "Do stock price bubbles correlate between China and Pakistan? An inquiry of pre‐ and post‐Chinese investment in Pakistani capital market under China‐Pakistan Economic Corridor regime," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(3), pages 323-335, July.
    11. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 11(1), pages 1-29, March.

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    More about this item

    Keywords

    Technical analysis; moving average; buy-and-hold strategy; dot-com bubble; Asian financial crisis; sub-prime crisis; moving linear regression; volatility;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C0 - Mathematical and Quantitative Methods - - General

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