US Stock Prices Research Articles

A Historical Analysis of the US Stock Price Index Using Empirical Mode Decomposition over 1791–2015

Abstract In this paper, the dynamics of Standard and Poor’s 500 (S&P 500) stock price index is analysed within a time-frequency framework over a monthly period 1791:08–2015:05. Using the Empirical Mode Decomposition technique, the S&P 500 stock price index is divided into different frequencies known as intrinsic mode functions (IMFs) and one residual. The IMFs and the residual are then reconstructed into high frequency, low frequency and trend components using the hierarchical clustering method. Using different measures, it is shown that the low frequency and trend components of stock prices are relatively important drivers of the S&P 500 index. These results are also robust across various subsamples identified based on structural break tests. Therefore, US stock prices have been driven mostly by fundamental laws rooted in economic growth and long-term returns on investment.

A historical analysis of the US stock price index using empirical mode decomposition over 1791-2015

In this paper, the dynamics of Standard and Poor's 500 (S&P 500) stock price index is analysed within a time-frequency framework over a monthly period 1791:08-2015:05. Using the Empirical Mode Decomposition technique, the S&P 500 stock price index is divided into different frequencies known as intrinsic mode functions (IMFs) and one residual. The IMFs and the residual are then reconstructed into high frequency, low frequency and trend components using the hierarchical clustering method. Using different measures, it is shown that the low frequency and trend components of stock prices are relatively important drivers of the S&P 500 index. These results are also robust across various subsamples identified based on structural break tests. Therefore, US stock prices have been driven mostly by fundamental laws rooted in economic growth and longterm returns on investment.

Gold as a Hedge and a Diversifier: The Perspective from Lebanon

The purpose of this paper is to find out whether gold, valued in Lebanese terms, is a hedge against losses in purchasing power, and whether it is a portfolio diversifier. Gold is found to be a partial hedge to consumer price inflation, a weak hedge to the depreciation of the domestic currency, and a modest hedge to commodity price changes, like oil prices. Although in a multiple regression context gold seems to be unrelated to US stock prices, converted to Lebanese pounds, the bivariate correlation is positive and high. The diversification properties of gold, when it is added to (1) oil, to (2) the DJIA, to (3) the Euro, to (4) a deposit in Lebanese pounds, or to (5) oil, the DJIA, the Euro and a deposit in Lebanese pounds, all together, are studied. It is found that the diversification benefits are rather substantial. The implied price of risk is commensurate with the one in the US financial market. Finally, gold volatility is asymmetrically affected by positive and negative shocks in gold prices, similar to the evidence elsewhere. But unlike the evidence elsewhere the volatility of gold responds strictly to positive shocks but not to negative shocks.

The roles of past returns and firm fundamentals in driving US stock price movements

This paper provides new empirical evidence that incorporating past stock returns from different time horizons can enhance the ability of firm fundamentals to better explain stock price movements but this benefit dissipates under uncertainty. We apply both OLS and state-space modeling to US firms' stock price movements over the period from 1999 to 2012 to compare the roles of the two main types of information typically used by equity investors. Empirical results reveal the importance of firm fundamentals over longer term horizons for particularly, small-cap stocks with greater information uncertainty. Furthermore, when market uncertainty is high, fundamentals unambiguously dominate in driving stock price movements of smaller sized firms indicating that uncertainty at the firm and market level both create attention bias on firm fundamentals.

Quantitative Easing and US Stock Prices

Conventional wisdom is that unconventional monetary policy a.k.a. Quantitative Easing (QE) pursued by the Federal Reserve has helped sustain and even boost U.S. stock market prices in the aftermath of the Global Financial Crisis. By design, QE has supported long-term Treasury bond prices and put downward pressure on long term rates; however the link to stock prices is more complicated because other factors operate as well, e.g., the performance of stock markets in the rest of the world, US dollar performance, virtually zero fed funds rate via conventional monetary policy (independent of policies such as QE), and changes in the interaction between economic activity and credit conditions – as measured for instance by the high yield spread along the lines of the financial accelerator theory. This paper presents evidence that these other factors explain most of the variation in stock returns before and after the financial crisis with stable coefficients and that the different rounds of QE explain significantly some of the remaining variation in stock prices. A vector autoregression provides further evidence of the effects of QE on stock returns and its relative importance vis-a-vis other variables via a Pesaran-Shin generalized impulse-response analysis (invariant to the causality ordering). The evidence in this paper is consistent with the boost in stock prices after the Fed actually started tapering in January 2014 because the “fundamentals” were supportive of the stock market. Furthermore, if the positive effects of QE on the stock market are less strong than commonly believed, the negative effects from a gradual shrinking of the Fed’s balance sheet may not be as bad as commonly feared.

Determinants of the CNY/USD exchange rate: a simultaneous-equation model

Applying the generalised autoregressive conditional heteroscedasticity (GARCH) model and using a sample during 2005.Q3-2014.Q1, this paper finds that the CNY/USD exchange rate is positively associated with the US interest rate, the US stock price and the inflation rate differential (China's inflation rate minus the US inflation rate) and negatively affected by China's interest rate, US real gross domestic product (GDP), and China's stock price. The coefficient of real GDP in China is positive but insignificant.

Bubbles and the Weibull distribution: was there an explosive bubble in US stock prices before the global economic crisis?

Existing studies on bubbles have been mainly concerned with investigating the stationarity properties of stock prices and market fundamentals. We develop a new method of testing for bubbles that relates the bubble component of stock prices to the probability of bursting in the context of the Weibull distribution. There were several eruptions and subsequent collapses of seeming bubbles over the past three decades: 1987 (Black Monday), 2000 (information technology (IT) boom) and 2007 (housing market boom). Using US monthly data for the S&P 500 and NASDAQ series, we have found that the S&P 500 series contained an explosive bubble only during the boom of the housing market that occurred before the 2007 global economic crisis, and the NASDAQ market contained an explosive bubble during the surge of stock prices peaking in 1987 and 2007, although our stationarity tests fail to detect the bubbles. No bubble was found in both the S&P and NASDAQ series during the 2000 IT boom. Our evidence corroborates the criticism that the traditional unit root and cointegration tests may not be able to detect some important class of bubbles.

Quantifying irrational sentiment

The measure exploits two biases, loss aversion and recency, to create two indicators: a rational sentiment indicator and an irrational one. The rational indicator mutes the biases, while the irrational indicator turns the biases on. The irrational-rational index (IRI) equals the spread between the two indicators. A large gap between the two indicators signifies greater irrational sentiment in the marketplace. I test whether these large gaps in the IRI lead to US stock price reversals to the mean. Results indicate that the IRI is successful in detecting irrational fear, but that it is not successful in detecting irrational optimism. Even so, the IRI was able to detect irrational optimism and fear before and after the two largest crises since 1980: those in 1987 and in 2008.

Japanese Stock Markets and the US Stock Price Index Ratios

This paper investigates which US stock price index is strongly influenced by the Japanese stock markets. Our empirical tests as to the time-varying correlations derived from a multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model reveal the following evidence. First, (1) we clarify that all US stock returns of the Dow Jones, S&P 500, and NASDAQ Composite indices are statistically significantly positively influenced by the returns of TOPIX and the Nikkei 225. Moreover, we also find that (2) the changes of the NASDAQ-S&P 500 (NS) ratio are statistically significantly positively influenced by the returns of TOPIX and the Nikkei 225 while the changes of the Dow Jones-S&P 500 (DS) ratio and the Dow Jones-NASDAQ (DN) ratio are statistically significantly negatively influenced by the returns of TOPIX and the Nikkei 225. These relations between the returns of TOPIX and the Nikkei 225 and the US stock price index ratios are clearly shown in all three sample periods employed in this paper. Hence our results suggest that, in our analyzing periods, (3) the NASDAQ index is most strongly positively connected with the Japanese stock markets.

Structural Analysis with Independent Innovations

Structural innovations in multivariate dynamic systems are typically hidden and often identified by means of a-priori economic reasoning. Under multivariate Gaussian model innovations there is no loss measure available to distinguish alternative orderings of variables or, put differently, between particular identifying restrictions and rotations thereof. Based on a non Gaussian framework of independent innovations, a loss statistic is proposed in this paper that allows to discriminate between alternative identifying assumptions on the basis of nonparametric density estimates. The merits of the proposed identification strategy are illustrated by means of a Monte Carlo study. Real data applications cover bivariate systems comprising US stock prices and total factor productivity, and four couples of international breakeven inflation rates to investigate monetary autonomy of the Bank of Canada and the Bank of England.

An Empirical Investigation of Factors Affecting Stock Prices in Vietnam

This paper investigates factors affecting Vietnam’s stock prices including US stock prices, foreign exchange rates, gold prices and crude oil prices. Using the daily data from 2005 to 2012, the results indicate that Vietnam’s stock prices are influenced by crude oil prices. In addition, Vietnam’s stock prices are also affected significantly by US stock prices, and foreign exchange rates over the period before the 2008 Global Financial Crisis. There is evidence that Vietnam’s stock prices are highly correlated with US stock prices, foreign exchange rates and gold prices for the same period. Furthermore, Vietnam’s stock prices were cointegrated with US stock prices both before and after the crisis, and with foreign exchange rates, gold prices and crude oil prices only during and after the crisis.

The relationship between oil prices and stock prices: a nonlinear asymmetric cointegration approach

This article investigates the long-run and short-run dynamics between US stock prices and oil prices over the period from 1 January 1992 to 22 November 2013 using the S&P 500 index and West Texas Intermediate spot oil prices. Unlike the majority of previous studies that are based on the conventional time series analysis, we examine for the presence of different sources of nonlinearities, such as structural breaks and asymmetric adjustments in the dynamic links between the investigated markets. The results from the threshold autoregressive (TAR) and momentum threshold autoregressive (MTAR) models of Enders and Siklos (2001) in conjunction with the Threshold Error Correction Model estimations provide evidence of asymmetric responses towards the equilibrium.

US Stocks and the US Dollar

The purpose of this paper is to study the relation between US stock prices, as exemplified by the S&P 500 stock index, and the change in eleven foreign exchange rates against the US dollar. The null hypothesis of no-cointegration fails to be rejected for all dual specifications. Granger-causality tests on the log returns reveals no effect of the exchange rates on stock prices, but there is Granger causality of US stock prices upon three foreign exchange rates. The model developed requires the inclusion in the regressions of the change in the cost of equity. The latter is substituted for by the baa or the aaa corporate bond yield. All regressions are estimated with a GARCH(1,1) model of the conditional variance. The regressions of stock log returns on the log returns of each foreign exchange rate uncover significant impacts for five different rates. However when the ‘fundamental variable’ is added to the regressions, which is the change in the cost of equity, replaced by the change in the baa corporate bond yield, the above impacts reduce to only one with an additional impact that is marginally significant. If the change in the aaa corporate bond yield replaces the change in the cost of equity two significant impacts out of the eleven are found, with one additional marginal result. The evidence is therefore rather strong that the US stock market and the US dollar are effectively independent of each other once fundamentals are accounted for.

Noncausality and Asset Pricing

Misspecification of agents' information sets or expectation formation mechanisms may lead to noncausal autoregressive representations of asset prices. Annual US stock prices are found to be noncausal, implying that agents' expectations are not revealed to an outside observer such as an econometrician observing only realized market data. A simulation study shows that noncausal processes can be generated by asset-pricing models featuring heterogeneous expectations.

Wall Street’s Bail-out Bet: Market Reactions to House Price Releases in the Presence of Bail-out Expectations

The rise and subsequent collapse of US house prices was one of the factors underlying the recent financial crisis. One could expect that the crisis brought increased attention to the housing market and thus led to stronger market reactions to house price news. We find that reactions indeed change, but with a peculiar twist: from September 2008 on, good news from the housing market are associated with falling US stock prices, and vice versa. The likely explanation, for which we provide cross-sectional evidence, is that falling house prices increased the market’s trust in a government bail-out, thereby increasing market valuations of firms that were expected to benefit from government rescue measures.

Wall Street’s bailout bet: Market reactions to house price releases in the presence of bailout expectations

The rise and subsequent collapse of US house prices was one of the factors underlying the recent financial crisis. One could expect that the crisis brought increased attention to the housing market and thus led to stronger market reactions to house price news. We find that reactions indeed change, but with a peculiar twist: from September 2008 on, good news from the housing market are associated with falling US stock prices, and vice versa. The likely explanation, for which we provide cross-sectional evidence, is that falling house prices increased the market’s trust in a government bailout, thereby increasing market valuations of firms that were expected to benefit from government rescue measures.

Noncausality and asset pricing

Misspecification of agents’ information sets or expectation formation mechanisms may lead to noncausal autoregressive representations of asset prices. Within the class of linear (vector) autoregressions, annual US stock prices are found to be best described by noncausal models, implying that agents’ expectations are not revealed to an outside observer such as an econometrician observing only realized market data. A simulation study shows that noncausal asset prices are observed when the data are generated by asset-pricing models featuring heterogeneous expectations.

Investing in Gold: What to Expect?

Gold is an attractive investment vehicle for many people. Some risks of in investing in gold are surveyed. The price of gold and its fluctuation are examined which raises the concern to all short and long-term investors in gold. This prompts us to study what drives the price movements of gold. In order to shed some light on this intriguing issue, we briefly review the recent history of gold prices within the last century and look at some relationships of gold with the US dollar and stock prices, among other variables. We conclude with some forecast of future prices.

The US tech pulse, stock prices, and exchange rate dynamics: Evidence from Asian developing countries

This paper empirically investigates the economic relationship between the US and Asian economies after the Asian currency crisis in Indonesia, Korea, the Philippines, Singapore, and Thailand, employing a cointegration methodology. Based on the empirical results, we conclude that the interdependence between the US and these Asian economies has intensified especially in information technology industries, and that their stock markets are integrated. On the other hand, the relationship between the domestic stock and foreign exchange markets is found to have a negative sign, interpreted by portfolio balance approach, in Indonesia, Korea, and Thailand. This result implies that the exchange rates of these countries are relatively vulnerable to fluctuation in international portfolio investments.

Systemic risk measures: The simpler the better?

This paper estimates and compares two groups of high-frequency market-based systemic risk measures using European and US interbank rates, stock prices and credit derivatives data from 2004 to 2009. Measures belonging to the macro group gauge the overall tension in the financial sector and micro group measures rely on individual institution information to extract joint distress. We rank the measures using three criteria: (i) Granger causality tests, (ii) Gonzalo and Granger metric, and (iii) correlation with an index of systemic events and policy actions. We find that the best systemic measure in the macro group is the first principal component of a portfolio of Credit Default Swap (CDS) spreads whereas the best measure in the micro group is the multivariate densities computed from CDS spreads. These results suggest that the measures based on CDSs outperform measures based on interbank rates or stock market prices.