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Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings

Author

Listed:
  • Rangan Gupta

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

  • Patrick Kanda

    (Laboratoire THéorie Économique, Modélisation et Applications (THEMA), Université de Cergy-Pontoise, France)

  • Mark E. Wohar

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

Abstract

In this paper we analyze whether presidential approval ratings can predict the S&P500 returns over the monthly period of 1941:07 to 2018:04, using a dynamic conditional correlation multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model. Our results show that, standard linear Granger causality test fail to detect any evidence of predictability. However, the linear model is found to be misspecified due to structural breaks and nonlinearity, and hence, the result of no causality from presidential approval ratings to stock returns cannot be considered reliable. When we use the DCC-MGARCH model, which is robust to such misspecifications, in 69 percent of the sample period, approval ratings in fact do strongly predict the S&P500 stock return. Moreover, using the DCC-MGARCH model we find that presidential approval rating is also a strong predictor of the realized volatility of S&P500. Overall, our results highlight that presidential approval ratings is helpful in predicting stock return and volatility, when one accounts for nonlinearity and regime changes through a robust time-varying model.

Suggested Citation

  • Rangan Gupta & Patrick Kanda & Mark E. Wohar, 2018. "Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings," Working Papers 201830, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201830
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    Cited by:

    1. Semei Coronado & Jose N. Martinez & Victor Gualajara & Rafael Romero-Meza & Omar Rojas, 2023. "Time-Varying Granger Causality of COVID-19 News on Emerging Financial Markets: The Latin American Case," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    2. Oguzhan Cepni & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2024. "Political Geography and Stock Market Volatility: The Role of Political Alignment across Sentiment Regimes," Working Papers 202414, University of Pretoria, Department of Economics.
    3. Rangan Gupta & Yuvana Jaichand & Christian Pierdzioch & Reneé van Eyden, 2023. "Realized Stock-Market Volatility of the United States and the Presidential Approval Rating," Mathematics, MDPI, vol. 11(13), pages 1-27, July.
    4. Celso-Arellano, Pedro & Gualajara, Victor & Coronado, Semei & Martinez, Jose N. & Venegas-Martínez, Francisco, 2023. "Impact of the global fear index (covid-19 panic) on the S&P global indices associated with natural resources, agribusiness, energy, metals and mining: Granger Causality and Shannon and Rényi Transfer ," MPRA Paper 117138, University Library of Munich, Germany, revised 06 Feb 2023.
    5. Rangan Gupta & Christian Pierdzioch & Aviral K. Tiwari, 2024. "Gasoline Prices and Presidential Approval Ratings of the United States," Working Papers 202427, University of Pretoria, Department of Economics.
    6. Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2024. "Modeling the Presidential Approval Ratings of the United States using Machine-Learning: Does Climate Policy Uncertainty Matter?," Working Papers 202406, University of Pretoria, Department of Economics.
    7. Caporin, Massimiliano & Costola, Michele, 2022. "Time-varying Granger causality tests in the energy markets: A study on the DCC-MGARCH Hong test," Energy Economics, Elsevier, vol. 111(C).

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    More about this item

    Keywords

    US Presidential Approval Ratings; DCC-MGARCH; Stock Returns; Realized Volatility; S&P500;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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