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Predictability in bond returns using technical trading rules

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  • Shynkevich, Andrei

Abstract

The predictability of future returns on bond portfolios at daily frequency is investigated using a large universe of mechanical trading rules that have been popularized in literature on equity and currency markets. The predictability in returns is inversely related to interest rate risk but positively related to default risk. The return predictability is more sensitive to fluctuations in the economic business cycle rather than changes in the Federal Reserve's monetary policy. Returns on portfolios of Treasury bonds are more predictable during the restrictive monetary policy regime, whereas returns on both Treasury bonds and corporate bonds exhibit much better predictability during the economic expansions rather than recessions. The predictability of returns in various segments of the U.S. bond market has declined over time. Findings for the predictability in the highly liquid bond exchange-traded funds are largely in line with the original results of the predictability in bond portfolio returns.

Suggested Citation

  • Shynkevich, Andrei, 2016. "Predictability in bond returns using technical trading rules," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 55-69.
  • Handle: RePEc:eee:jbfina:v:70:y:2016:i:c:p:55-69
    DOI: 10.1016/j.jbankfin.2016.06.010
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    References listed on IDEAS

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    Cited by:

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    7. Fong, Tom Pak Wing & Wu, Shui Tang, 2020. "Predictability in sovereign bond returns using technical trading rules: Do developed and emerging markets differ?," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    8. Andrei Shynkevich, 2021. "Impact of bitcoin futures on the informational efficiency of bitcoin spot market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 115-134, January.
    9. Leandro Maciel, 2020. "Technical analysis based on high and low stock prices forecasts: evidence for Brazil using a fractionally cointegrated VAR model," Empirical Economics, Springer, vol. 58(4), pages 1513-1540, April.
    10. Urquhart, Andrew & Zhang, Hanxiong, 2019. "The performance of technical trading rules in Socially Responsible Investments," International Review of Economics & Finance, Elsevier, vol. 63(C), pages 397-411.
    11. Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios, 2023. "Technical analysis, spread trading, and data snooping control," International Journal of Forecasting, Elsevier, vol. 39(1), pages 178-191.
    12. Ikhlaas Gurrib, 2022. "Technical Analysis, Energy Cryptos and Energy Equity Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 249-267, March.
    13. Robert Hudson & Andrew Urquhart, 2021. "Technical trading and cryptocurrencies," Annals of Operations Research, Springer, vol. 297(1), pages 191-220, February.
    14. Ikhlaas Gurrib, 2023. "Momentum in Low Carbon and Fossil Fuel Free Equity Investing," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 461-471, September.
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    More about this item

    Keywords

    Return predictability; Data snooping; Nonsynchronicity; Technical analysis; Trading rule; Market efficiency;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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