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The Profitability in the FTSE 100 Index: A New Markov Chain Approach

Author

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  • Flavio Ivo Riedlinger

    (Universidade de Lisboa and CEMAPRE ISEG)

  • João Nicolau

    (Universidade de Lisboa and CEMAPRE ISEG)

Abstract

In this paper, we propose a new method to predict stock market trends based on the multivariate Markov chain (MMC) methodology. Our approach consists of forecasting the one-period ahead FTSE 100 Index behavior, using the MTD-Probit model. The MTD-Probit model is a new approach for estimating MMC, based on multiple categorical data sequences that can be used to forecast financial markets. In this context, we propose a simple trading strategy and analyze its profitability using the White “Reality Check” and the Hansen SPA data snooping bias tests. Our empirical results suggest that the MTD-Probit model applied to the FTSE 100 Index cannot significantly out-perform the buy-and-hold benchmark after data-snooping is controlled.

Suggested Citation

  • Flavio Ivo Riedlinger & João Nicolau, 2020. "The Profitability in the FTSE 100 Index: A New Markov Chain Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(1), pages 61-81, March.
    • Handle: RePEc:kap:apfinm:v:27:y:2020:i:1:d:10.1007_s10690-019-09282-4
    DOI: 10.1007/s10690-019-09282-4
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    1. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    2. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, July.
    3. Kübra Akca & Serda Selin Ozturk, 2016. "The Effect of 2008 Crisis on the Volatility Spillovers among Six Major Markets," International Review of Finance, International Review of Finance Ltd., vol. 16(1), pages 169-178, March.
    4. McQueen, Grant & Thorley, Steven, 1991. "Are Stock Returns Predictable? A Test Using Markov Chains," Journal of Finance, American Finance Association, vol. 46(1), pages 239-263, March.
    5. Massoud Metghalchi & Juri Marcucci & Yung-Ho Chang, 2012. "Are moving average trading rules profitable? Evidence from the European stock markets," Applied Economics, Taylor & Francis Journals, vol. 44(12), pages 1539-1559, April.
    6. Andre Berchtold, 2001. "Estimation in the Mixture Transition Distribution Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(4), pages 379-397, July.
    7. Cheng-Wei Chen & Chin-Sheng Huang & Hung-Wei Lai, 2011. "Data Snooping on Technical Analysis: Evidence from the Taiwan Stock Market," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 14(02), pages 195-212.
    8. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    9. Andreas Neuhierl & Bernd Schlusche, 2011. "Data Snooping and Market-Timing Rule Performance," Journal of Financial Econometrics, Oxford University Press, vol. 9(3), pages 550-587, Summer.
    10. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    11. Natarajana, Vinodh & Raja , Azariah & Priyac, Nagarajan, 2014. "Examining mean-volatility spillovers across national stock markets," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 19(36), pages 55-62.
    12. Baele, Lieven, 2005. "Volatility Spillover Effects in European Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(2), pages 373-401, June.
    13. Angelos Kanas, 1998. "Volatility spillovers across equity markets: European evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 8(3), pages 245-256.
    14. Lo, Andrew W & MacKinlay, A Craig, 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 3(3), pages 431-467.
    15. Yun-Shan Dai & Wei-Ming Lee, 2011. "The profitability of technical analysis in the Taiwan-U.S. forward foreign exchange market," Economics Bulletin, AccessEcon, vol. 31(2), pages 1606-1612.
    16. Shynkevich, Andrei, 2012. "Performance of technical analysis in growth and small cap segments of the US equity market," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 193-208.
    17. Wai Ki Ching & Eric S. Fung & Michael K. Ng, 2004. "Higher‐order Markov chain models for categorical data sequences," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(4), pages 557-574, June.
    18. Bessembinder, Hendrik & Chan, Kalok, 1995. "The profitability of technical trading rules in the Asian stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 3(2-3), pages 257-284, July.
    19. Kuang, P. & Schröder, M. & Wang, Q., 2014. "Illusory profitability of technical analysis in emerging foreign exchange markets," International Journal of Forecasting, Elsevier, vol. 30(2), pages 192-205.
    20. Bruce D. Fielitz & T. N. Bhargava, 1973. "The Behavior of Stock-Price Relatives—A Markovian Analysis," Operations Research, INFORMS, vol. 21(6), pages 1183-1199, December.
    21. Charlotte Christiansen, 2007. "Volatility‐Spillover Effects in European Bond Markets," European Financial Management, European Financial Management Association, vol. 13(5), pages 923-948, November.
    22. João Nicolau, 2014. "A New Model for Multivariate Markov Chains," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1124-1135, December.
    23. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    24. Abou-Zaid, Ahmed S., 2011. "Volatility Spillover Effects in Emerging MENA Stock Markets," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 7(1-2), pages 1-21, March.
    25. T. C. Mills & J. V. Jordanov, 2003. "The size effect and the random walk hypothesis: evidence from the London Stock Exchange using Markov Chains," Applied Financial Economics, Taylor & Francis Journals, vol. 13(11), pages 807-815.
    26. Cheol‐Ho Park & Scott H. Irwin, 2010. "A reality check on technical trading rule profits in the U.S. futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(7), pages 633-659, July.
    27. Hsu, Po-Hsuan & Taylor, Mark P. & Wang, Zigan, 2016. "Technical trading: Is it still beating the foreign exchange market?," Journal of International Economics, Elsevier, vol. 102(C), pages 188-208.
    28. Chan, Justin S.P. & Jain, Ravi & Xia, Yihong, 2008. "Market segmentation, liquidity spillover, and closed-end country fund discounts," Journal of Financial Markets, Elsevier, vol. 11(4), pages 377-399, November.
    29. Subrata Kumar Mitra, 2011. "How rewarding is technical analysis in the Indian stock market?," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 287-297.
    30. Yu, Hao & Nartea, Gilbert V. & Gan, Christopher & Yao, Lee J., 2013. "Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 356-371.
    31. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    32. Hsu, Po-Hsuan & Hsu, Yu-Chin & Kuan, Chung-Ming, 2010. "Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 471-484, June.
    33. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    34. Hamao, Yasushi & Masulis, Ronald W & Ng, Victor, 1990. "Correlations in Price Changes and Volatility across International Stock Markets," Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 281-307.
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