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The Role of Partisan Conflict in Forecasting the U.S. Equity Premium: A Nonparametric Approach

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria, South Africa)

  • John W. Muteba Mwamba

    (Faculty of Economic and Financial Sciences, University of Johannesburg, South Africa)

  • Mark E. Wohar

    (College of Business Administration, University of Nebraska at Omaha USA, and School of Business and Economics, Loughborough University, UK)

Abstract

Information on partisan conflict is shown to matter in forecasting the U.S. equity premium, especially when accounting for omitted nonlinearities in their relationship, via a nonparametric predictive regression approach over the monthly period 1981:1-2016:6. Unlike as suggested by a linear predictive model, the nonparametric functional coefficient regression that includes the partisan conflict index, enhances significantly the out-of-sample excess stock returns predictability.

Suggested Citation

  • Rangan Gupta & John W. Muteba Mwamba & Mark E. Wohar, 2016. "The Role of Partisan Conflict in Forecasting the U.S. Equity Premium: A Nonparametric Approach," Working Papers 201686, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201686
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    References listed on IDEAS

    as
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    7. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
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    Cited by:

    1. Rangan Gupta & Patrick Kanda & Mark E. Wohar, 2021. "Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 324-335, March.
    2. Pandey, Dharen Kumar & Lucey, Brian M. & Kumar, Satish, 2023. "Border disputes, conflicts, war, and financial markets research: A systematic review," Research in International Business and Finance, Elsevier, vol. 65(C).
    3. Cheng, Chak Hung Jack & Chiu, Ching-Wai (Jeremy) & Hankins, William B. & Stone, Anna-Leigh, 2018. "Partisan conflict, policy uncertainty and aggregate corporate cash holdings," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 78-90.
    4. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022. "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, vol. 15(22), pages 1-26, November.
    5. Xianzheng Zhou & Hui Zhou & Huaigang Long, 2023. "Forecasting the equity premium: Do deep neural network models work?," Modern Finance, Modern Finance Institute, vol. 1(1), pages 1-11.
    6. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    7. Cai, Yifei & Wu, Yanrui, 2019. "Time-varied causality between US partisan conflict shock and crude oil return," Energy Economics, Elsevier, vol. 84(C).
    8. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
    9. Yong Jiang & Yi-Shuai Ren & Chao-Qun Ma & Jiang-Long Liu & Basil Sharp, 2018. "Does the price of strategic commodities respond to U.S. Partisan Conflict?," Papers 1810.08396, arXiv.org, revised Feb 2020.
    10. Afees A. Salisu & Rangan Gupta, 2021. "Commodity Prices and Forecastability of South African Stock Returns Over a Century: Sentiments versus Fundamentals," Working Papers 202144, University of Pretoria, Department of Economics.
    11. Demiralay, Sercan, 2020. "Political uncertainty and the us tourism index returns," Annals of Tourism Research, Elsevier, vol. 84(C).
    12. Jonathan A. Batten & Harald Kinateder & Niklas Wagner, 2022. "Beating the Average: Equity Premium Variations, Uncertainty, and Liquidity," Abacus, Accounting Foundation, University of Sydney, vol. 58(3), pages 567-588, September.
    13. Jia, Boxiang & Goodell, John W. & Shen, Dehua, 2021. "US partisan conflict and high-yield exchange rates," Finance Research Letters, Elsevier, vol. 40(C).
    14. Jiang, Yong & Ren, Yi-Shuai & Ma, Chao-Qun & Liu, Jiang-Long & Sharp, Basil, 2020. "Does the price of strategic commodities respond to U.S. partisan conflict?," Resources Policy, Elsevier, vol. 66(C).
    15. Rangan Gupta & Christian Pierdzioch & Refk Selmi & Mark E. Wohar, 2017. "Does Partisan Conflict Predict a Reduction in US Stock Market (Realized) Volatility? Evidence from a Quantile-on-Quantile Regression Model," Working Papers 201744, University of Pretoria, Department of Economics.

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    More about this item

    Keywords

    : Equity Premium; Partisan Conflict Index; Linear and Nonparametric Predictive Regressions;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G1 - Financial Economics - - General Financial Markets
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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