Predictability in sovereign bond returns using technical trading rules: Do developed and emerging markets differ?

Tom Fong () and Shui Tang Wu

The North American Journal of Economics and Finance, 2020, vol. 51, issue C

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.

Keywords: Trading rule; Return predictability; Sovereign bond; Market efficiency; Machine learning; Bayesian analysis; Time series (search for similar items in EconPapers)
JEL-codes: C11 C14 C58 E50 G14 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:51:y:2020:i:c:s1062940819300932

DOI: 10.1016/j.najef.2019.101105

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