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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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Suggested Citation

  • 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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    2. 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).

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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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