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Predictability in sovereign bond returns using technical trading rule: do developed and emerging markets differ?

In: The use of big data analytics and artificial intelligence in central banking

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  • Tom Fong
  • Gabriel Wu

Abstract

The study examines the predictability of 48 sovereign bond markets based on a strategy of 27,000 technical trading rules. These rules represent four popular trading rule classes, they are: moving average, filtering, support and resistance, and channel breakout rules, with numerous variants in each class. Empirical results show that (i) investing in sovereign bond markets is predictable, based on the buy-sell signals generated by trading rules, with the predictability of the emerging Asian markets being significantly higher than those of the advanced markets; (ii) the predictability is generally higher when the US tightens its monetary policies or undergoes recession or a financial crisis; (iii) two-thirds of sovereign bond markets have a higher predictability when we use a machine learning algorithm to determine the best trading rule strategy; and (iv) the predictability of a sovereign bond market is higher when the economy has a less effective government, lower regulatory quality, lower degree of financial openness, higher political risk, lower income and faster real money growth. Our results suggest that shocks originating from US monetary policy or economic conditions could have a considerable spillover effect on sovereign bond markets, particularly the emerging Asian markets.
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  • Tom Fong & Gabriel Wu, 2019. "Predictability in sovereign bond returns using technical trading rule: do developed and emerging markets differ?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
  • Handle: RePEc:bis:bisifc:50-20
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    1. Sweeney, Richard J, 1986. "Beating the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 41(1), pages 163-182, March.
    2. Chinn, Menzie D. & Ito, Hiro, 2006. "What matters for financial development? Capital controls, institutions, and interactions," Journal of Development Economics, Elsevier, vol. 81(1), pages 163-192, October.
    3. Andrei Shynkevich, 2016. "Predictability of equity returns during a financial crisis," Applied Economics Letters, Taylor & Francis Journals, vol. 23(17), pages 1201-1205, November.
    4. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
    5. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    6. 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.
    7. Ireland, Peter N., 2015. "Monetary policy, bond risk premia, and the economy," Journal of Monetary Economics, Elsevier, vol. 76(C), pages 124-140.
    8. Hall, Stephen G & Miles, David K, 1992. "Measuring Efficiency and Risk in the Major Bond Markets," Oxford Economic Papers, Oxford University Press, vol. 44(4), pages 599-625, October.
    9. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    10. 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.
    11. Day, Theodore E. & Wang, Pingying, 2002. "Dividends, nonsynchronous prices, and the returns from trading the Dow Jones Industrial Average," Journal of Empirical Finance, Elsevier, vol. 9(4), pages 431-454, November.
    12. Bachar Fakhry & Christian Richter, 2015. "Is the sovereign debt market efficient? Evidence from the US and German sovereign debt markets," International Economics and Economic Policy, Springer, vol. 12(3), pages 339-357, September.
    13. Narayan, Paresh Kumar & Narayan, Seema & Sharma, Susan Sunila, 2013. "An analysis of commodity markets: What gain for investors?," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3878-3889.
    14. Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
    15. Shynkevich, Andrei, 2016. "Predictability in bond returns using technical trading rules," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 55-69.
    16. Dahlquist, Magnus & Hasseltoft, Henrik, 2013. "International Bond Risk Premia," Journal of International Economics, Elsevier, vol. 90(1), pages 17-32.
    17. Hong, Yoo Soo, 2005. "Republic of Korea," Documentos de Proyectos 4161, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    18. Julien Cujean & Michael Hasler, 2017. "Why Does Return Predictability Concentrate in Bad Times?," Journal of Finance, American Finance Association, vol. 72(6), pages 2717-2758, December.
    19. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    20. Frankel, Jeffrey A & Froot, Kenneth A, 1990. "Chartists, Fundamentalists, and Trading in the Foreign Exchange Market," American Economic Review, American Economic Association, vol. 80(2), pages 181-185, May.
    21. Puy, Damien, 2016. "Mutual funds flows and the geography of contagion," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 73-93.
    22. Po-Hsuan Hsu & Chung-Ming Kuan, 2005. "Reexamining the Profitability of Technical Analysis with Data Snooping Checks," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 606-628.
    23. Menkhoff, Lukas, 2010. "The use of technical analysis by fund managers: International evidence," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2573-2586, November.
    24. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    25. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
    26. Robert J. Shiller, 1992. "Market Volatility," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691515, April.
    27. Lukas Menkhoff & Mark P. Taylor, 2007. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Journal of Economic Literature, American Economic Association, vol. 45(4), pages 936-972, December.
    28. Sarno, Lucio & Schneider, Paul & Wagner, Christian, 2016. "The economic value of predicting bond risk premia," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 247-267.
    29. Alven H.S. Lam, 2000. "Republic of China (Taiwan)," American Journal of Economics and Sociology, Wiley Blackwell, vol. 59(5), pages 327-336, November.
    30. Shynkevich, Andrei, 2012. "Short-term predictability of equity returns along two style dimensions," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 675-685.
    31. Allen, Helen & Taylor, Mark P, 1990. "Charts, Noise and Fundamentals in the London Foreign Exchange Market," Economic Journal, Royal Economic Society, vol. 100(400), pages 49-59, Supplemen.
    32. Fong, Tom Pak Wing & Li, Ka-Fai & Fu, John, 2018. "Accounting for sovereign tail risk in emerging economies: The role of global and domestic risk factors," Emerging Markets Review, Elsevier, vol. 34(C), pages 98-110.
    33. Cheung, Yin-Wong & Chinn, Menzie David, 2001. "Currency traders and exchange rate dynamics: a survey of the US market," Journal of International Money and Finance, Elsevier, vol. 20(4), pages 439-471, August.
    34. Zunino, Luciano & Fernández Bariviera, Aurelio & Guercio, M. Belén & Martinez, Lisana B. & Rosso, Osvaldo A., 2012. "On the efficiency of sovereign bond markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4342-4349.
    35. Paresh Kumar Narayan & Huson Ali Ahmed & Seema Narayan, 2015. "Do Momentum‐Based Trading Strategies Work in the Commodity Futures Markets?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(9), pages 868-891, September.
    36. Matthew C. Roberts, 2005. "Technical analysis and genetic programming: Constructing and testing a commodity portfolio," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(7), pages 643-660, July.
    37. Henkel, Sam James & Martin, J. Spencer & Nardari, Federico, 2011. "Time-varying short-horizon predictability," Journal of Financial Economics, Elsevier, vol. 99(3), pages 560-580, March.
    38. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    39. Godfrey R.A. Dunkley, 2000. "Republic of South Africa," American Journal of Economics and Sociology, Wiley Blackwell, vol. 59(5), pages 299-311, November.
    40. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    41. 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.
    42. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    43. Marshall, Ben R. & Cahan, Rochester H. & Cahan, Jared M., 2008. "Does intraday technical analysis in the U.S. equity market have value?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 199-210, March.
    44. 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.
    45. Marshall, Ben R. & Cahan, Rochester H. & Cahan, Jared M., 2008. "Can commodity futures be profitably traded with quantitative market timing strategies?," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1810-1819, September.
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    1. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2021. "Bond return predictability: Evidence from 25 OECD countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    2. Vukovic, Darko & Vyklyuk, Yaroslav & Matsiuk, Natalia & Maiti, Moinak, 2020. "Neural network forecasting in prediction Sharpe ratio: Evidence from EU debt market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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