Donald Trump's Random Walk Up Wall Street
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1 Donald Trump's Random Walk Up Wall Street Research Question: Did upward stock market trend since beginning of Obama era in January 2009 increase after Donald Trump was elected President? Data: Daily data for Dow Jones Industrial Aver, S&P 500 and Nasdaq for the January 5, 2009 December 22, 2017 period from the Federal Reserve Bank of St. Louis (FRED) database. Preliminary Diagnostics: Dickey-Fuller unit-root tests indicate the three market indices (Dow Jones, S&P, Nasdaq) are nonstationary and become stationary when first differenced.preliminary diagnostics Model: Random walk with drift, with additional dummy variable shifting from 0 to 1 on Nov 9, 2016, the day after Trump was elected. Asymmetric EGARCH (1,1) model for conditional error variance, generalized error distribution (GED) to allow for 'fat tails' in the error distribution and Trump dummy variable as covariate in the conditional variance model. Model parameters estimated using Eviews 15, November 30, 2017 build Key Finding: upward trend (drift) in major market indices (Dow Jones, S & P, Nasdaq) all increased significantly after Trump was elected president. The effects are sizable, e.g., for the Dow Jones, controlling for overall trend, the Trump drift term can account for 4,178 of the 6,421 point upward movement in the index since he was elected. Trump did not just 'surf along' on the overall upward market trend.
2 2 Figure 1. Dow Jones Daily Industrial Average, January 5, December 22, ,000 22,500 Dow Jones Industrial Average 20,000 17,500 15,000 12,500 10,000 7, Presidential Election--> 5, Date 2,800 Figure 2. Standard and Poor's 500 Daily Index, January 5, December 22, 2017 Standard and Poor 500 Index 2,400 2,000 1,600 1, Presidential Election--> Date
3 3 Figure 3. Nasdaq Daily Composite Index January 5, December 22, ,000 Nasdaq Daily Composite Index 7,000 6,000 5,000 4,000 3,000 2, Presidential Election--> 1, Date
4 4 Table 1. Dow Jones Random Walk With Overall and Trump Drift Model, EGARCH (1,1), GED Error Distribution, Asymmetric Innovations with Trump Covariate Dependent Variable: DJREVD Method: ML - ARCH (Marquardt) - Generalized error distribution (GED) Date: 12/28/17 Time: 10:29 Sample (adjusted): 1/06/ /22/2017 Included observations: 2339 after adjustments Convergence achieved after 16 iterations Presample variance: backcast (parameter = 0.7) LOG(GARCH) = C(3) + C(4)*ABS(RESID(-1)/@SQRT(GARCH(-1))) + C(5) *RESID(-1)/@SQRT(GARCH(-1)) + C(6)*LOG(GARCH(-1)) + C(7) *TRUMP Variable Coefficient Std. Error z-statistic Prob. C TRUMP Variance Equation C(3) C(4) C(5) C(6) C(7) GED PARAMETER R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat
5 5 Table 2. S & P Random Walk Model with Overall and Trump Drift, EGARCH (1,1), GED Error Distribution, Asymmetric Innovations with Trump Covariate Dependent Variable: SPHDCD Method: ML ARCH - Generalized error distribution (GED) (Marquardt / EViews legacy) Date: 12/27/17 Time: 07:53 Sample (adjusted): 1/06/ /22/2017 Included observations: 2339 after adjustments Convergence achieved after 17 iterations Presample variance: backcast (parameter = 0.7) LOG(GARCH) = C(3) + C(4)*ABS(RESID(-1)/@SQRT(GARCH(-1))) + C(5) *RESID(-1)/@SQRT(GARCH(-1)) + C(6)*LOG(GARCH(-1)) + C(7) *TRUMP Variable Coefficient Std. Error z-statistic Prob. C TRUMP Variance Equation C(3) C(4) C(5) C(6) C(7) GED PARAMETER R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat
6 6 Table 3. Nasdaq Random Walk Model with Overall and Trump Drift, EGARCH (1,1), GED Error Distribution, Asymmetric Innovations with Trump Covariate Dependent Variable: NASDAQREVD Method: ML ARCH - Generalized error distribution (GED) (BFGS / Marquardt steps) Date: 12/28/17 Time: 09:44 Sample (adjusted): 1/06/ /22/2017 Included observations: 2339 after adjustments Convergence achieved after 58 iterations Coefficient covariance computed using outer product of gradients Presample variance: backcast (parameter = 0.7) LOG(GARCH) = C(3) + C(4)*ABS(RESID(-1)/@SQRT(GARCH(-1))) + C(5) *RESID(-1)/@SQRT(GARCH(-1)) + C(6)*LOG(GARCH(-1)) + C(7) *TRUMP Variable Coefficient Std. Error z-statistic Prob. C TRUMP Variance Equation C(3) C(4) C(5) C(6) C(7) GED PARAMETER R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat Note: model diagnostics indicates residual autocorrelations and squared residual autocorrelation are white noise thru 36 lags.
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