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Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables

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  • Liu, Jingzhen
  • Kemp, Alexander

Abstract

In this study we propose a method of selecting the macroeconomic variables for forecasting the excess return signs of the U.S. oil and gas industry stock index by combining the Forward Sequential Variable Selection Algorithm and information criteria. We select predictors from a large monthly macroeconomic variable dataset designed by McCracken and Ng (2015). The method can adapt to the updated macroeconomic information and the possible time-varying relationship between the macroeconomic variables and the stock return signs. We also propose a method which can change the threshold value of the probit model automatically for considering the potential time-varying risk aversion level of the market participants. Further, we investigate the investment performance of an active trading strategy based on our forecasting model and compare it with a passive buy-and-hold trading strategy for different time periods.

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  • Liu, Jingzhen & Kemp, Alexander, 2019. "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, vol. 81(C), pages 672-686.
    • Handle: RePEc:eee:eneeco:v:81:y:2019:i:c:p:672-686
    DOI: 10.1016/j.eneco.2019.04.023
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    6. Sunil K. Mohanty & Stein Frydenberg & Petter Osmundsen & Sjur Westgaard & Christian Skjøld, 2023. "Risk factors in stock returns of U.S. oil and gas companies: evidence from quantile regression analysis," Review of Quantitative Finance and Accounting, Springer, vol. 60(2), pages 715-746, February.
    7. He, Feng & Ma, Feng & Wang, Ziwei & Yang, Bohan, 2021. "Asymmetric volatility spillover between oil-importing and oil-exporting countries' economic policy uncertainty and China's energy sector," International Review of Financial Analysis, Elsevier, vol. 75(C).

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    More about this item

    Keywords

    Excess stock return; U.S. Oil and gas industry; Probit model; Market timing; Big data;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • E00 - Macroeconomics and Monetary Economics - - General - - - General

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