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Forecasting the Equity Risk Premium: The Role of Technical Indicators

Author

Listed:
  • Christopher J. Neely

    (Federal Reserve Bank of St. Louis)

  • David E. Rapach

    (Saint Louis University)

  • Jun Tu

    (Singapore Management University)

  • Guofu Zhou

    (Washington University in St. Louis and CAFR)

Abstract

While macroeconomic variables have been used extensively to forecast the U.S. equity risk premium and build models to explain it, relatively little attention has been paid to the technical stock market indicators widely employed by practitioners. Our paper fills this gap by studying the forecasting ability of a variety of technical indicators in comparison to that of a number of well-known macroeconomic variables from the literature. We find that technical indicators have statistically and economically significant out-of-sample forecasting power and can be as useful as macroeconomic variables. Out-of-sample predictability is closely connected to the business cycle for both technical indicators and macroeconomic variables, although in a com- plementary manner: technical indicators detect the typical decline in the equity risk premium near cyclical peaks, while macroeconomic variables more readily pick up the typical rise near cyclical troughs. We further show that utilizing information from both technical indicators and macroeconomic variables substantially increases the out-of-sample gains relative to using either macroeconomic variables or technical indicators alone.

Suggested Citation

  • Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2011. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Working Papers CoFie-02-2011, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
  • Handle: RePEc:skb:wpaper:cofie-02-2011
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    More about this item

    Keywords

    Equity risk premium predictability; Macroeconomic variables; Moving-average rules; Momentum; Volume; Out-of-sample forecasts; Asset allocation; Mean-variance in- vestor; Business cycle; Principal components;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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