Information, Policy, and Market Disorder Under Democracy: Evidences from the United States

Abstract

Financial markets are perplexed by economic functioning, policies undertaken by the then government which is determined by elections wherein civilians interact and create noise that adds predictable bias while evaluating portfolios. This chapter takes the stand against the possibility of efficient market system while the system interacts socially. Information for the market is in three dimensions—phase (path dependency), economy, and globalization, but underneath all these, there runs an undercurrent of election cycle. Though Republicans are friendly to investor-based ethos, it is the Democratic government that appears to be favoring portfolio at least statistically. But the selfish investor defies ethos of development. The chapter finds that the failure of Republicans is partially related to their taxation anomaly. The convolution of politics, economics, and financial markets is illustrated through a bipolar lens of system with political exogeneity and endogeneity. We found that irrational exuberance among rational but myopic investor’s provisions for growth friendly behavior which is often non-friendly towards the concept of development but without development the endeavors of his feed from the by-product of globalization is incomplete. What solves this conflict? Our answer is Politics. Further, our analysis suggests that financial market is in disorder following their denial towards development. In order to reach a favorable state, one needs to first identify broader aspects of developmental processes.

Appendices

Appendix 1

Table 5.5 shows that the stock prices (adjusted close price) of ADX, NYSE, Merval & SENSEX exhibit weak form of market efficiency. As null hypothesis is not rejected, the market is weakly efficient in most of the cases. In 6 out 10 stock exchange markets, Z test value is between Z critical value, i.e., −1.96 & 1.96 (5% sig.). Also, at universal level, the evidences do not reject the null hypothesis and therefore favor the random walk theory.

On the basis of empirical results given by runs tests, we conclude that New York Stock Exchange (NYSE), Sensex, Buenos Aires Stock Exchange and Abu Dhabi Securities Exchange (A.D.X.) did not accept the null hypothesis and hence were not efficient. One the other hand, NASDAQ, Moscow Stock Exchange (MSE) Shanghai Stock Exchange (SSE), Hong Kong Stock Exchange (SEHK), Frankfurt Stock Exchange (DAX), National Stock Exchange (NSE) and Toronto Stock Exchange (TSX) accepted the null hypothesis and hence, are weakly form of efficient market.

Appendix 2

Appendix 3

Figure 5.9 displays the market returns, i.e., weekly difference in price of Wilshire 5000 Price Index, by presidential term served starting from President Reagan (1981–1988) till Obama (2009–2016).

Fig. 5.9

Market Returns by presidential term, 1981 to 2016

Appendix 4

5.1.1 Political Endogenous System

$$ {\mathrm{Risk}}_t={\alpha}_0+{\alpha}_1.{\mathrm{Return}}_{t-1}+{\alpha}_2.{\mathrm{Risk}}_{t-1}+{\epsilon}_t $$

(5.9)

$$ {\mathrm{Return}}_t=\alpha {\prime}_0+\alpha {\prime}_1.{\mathrm{Return}}_{t-1}+\alpha {\prime}_2.{\mathrm{Risk}}_{t-1}+{\epsilon}_t^{\prime } $$

(5.10)

5.1.2 Political Exogenous System

$$ {\mathrm{Risk}}_t={\alpha}_0^{\prime \prime }+{\alpha}_1^{\prime \prime }.{\mathrm{Return}}_{t-1}+{\alpha}_2^{\prime \prime }.{\mathrm{Risk}}_{t-1}+{\alpha}_3^{\prime \prime }.\mathrm{term}{2}_t+{\alpha}_4^{\prime \prime }.{\mathrm{Democratic}}_t+{e}_t $$

(5.11)

$$ {\mathrm{Return}}_t={\alpha}_5^{\prime \prime }+{\alpha}_6^{\prime \prime }.{\mathrm{Return}}_{t-1}+{\alpha}_7^{\prime \prime }.{\mathrm{Risk}}_{t-1}+{\alpha}_8^{\prime \prime }.\mathrm{term}{2}_t+{\alpha}_9^{\prime \prime }.{\mathrm{Democratic}}_t+e{\prime}_t $$

(5.12)

Appendix 5

5.1.1 Politics Endogenous Model

$$ \Delta {\mathrm{ME}}_t={\beta}_0+{\sum}_{i=1}^3{\beta}_i.\Delta {\mathrm{ME}}_{t-1}+{\sum}_{i=1}^3{\delta}_i.\Delta {\mathrm{CE}}_{t-1}+{\vartheta}_t $$

(5.13)

$$ \Delta {\mathrm{CE}}_t={\beta}_0^{\prime }+{\sum}_{i=1}^3{\beta}_i^{\prime }.\Delta {\mathrm{ME}}_{t-1}+{\sum}_{i=1}^3{\delta}_i^{\prime }.\Delta {\mathrm{CE}}_{t-1}+{\vartheta}_t^{\prime } $$

(5.14)

5.1.2 Politics Exogenous Model

$$ \Delta {\mathrm{ME}}_t={\beta}_0^{\prime \prime }+{\sum}_{i=1}^3{\beta}_i^{\prime \prime }.\Delta {\mathrm{ME}}_{t-1}+{\sum}_{i=1}^3{\delta}_i^{\prime \prime }.\Delta {\mathrm{CE}}_{t-1}+{\tau}_1.\mathrm{term}{2}_t+{\eta}_1.{\mathrm{Democratic}}_t+{\upsilon}_t $$

(5.15)

$$ \Delta {\mathrm{CE}}_t={\beta}_0^{\prime \prime \prime }+{\sum}_{i=1}^3{\beta}_i^{\prime \prime \prime }.\Delta {\mathrm{ME}}_{t-1}+{\sum}_{i=1}^3{\delta}_i^{\prime \prime \prime }.\Delta {\mathrm{CE}}_{t-1}+{\tau}_2.\mathrm{term}{2}_t+{\eta}_2.{\mathrm{Democratic}}_t+{\upsilon}_t^{\prime } $$

(5.16)

Appendix 6

Appendix 7

The stationary test results for the variables used in Tables 5.2, 5.3, and 5.4 and Appendices 4, 5, and 6. It was done to make these variables time-invariant.

Appendix 8

Figure 5.10 shows the kernel density estimate of variable, age dependency ratio; from which

Fig. 5.10

Kernel density estimate of Age Dependency Ratio

a binary variable, High ADR is created referring to the period when age dependency was comparatively higher than the rest of the period.