Twitter’s daily happiness sentiment,economic policy uncertainty,and stock index fluctuations

This research applies quantile Granger causality and impulse-response analyses to evaluate the causal linkages among Twitter’s daily happiness sentiment, economic policy uncertainty (EPU), and S&P 500 indices across the U.S. stock market cycles. We present notable evidence of bi-directional causality among cyclical components of Twitter’s daily happiness sentiment, economic policy uncertainty, and S&P 500 indices for most quantiles. The causal linkage of Twitter’s daily happiness sentiment and S&P 500 indices identified in this study reconciles the so-called Easterlin Paradox and Easterlin Illusion arguments from previous studies on income-happiness relationship. Moreover, given a high (low) EPU level, the positive (negative) impulse-response effects between the Twitter’s daily happiness sentiment and the S&P 500 indices are justified during a stock market bust cycle, but the signs of these correlations change to negative (positive) during a stock market boom cycle. These findings imply that investors’ hedging strategies can be linked to the surveillance of the Twitter’s daily happiness sentiment index.     

Higher moment exchange rate exposure of S&P500 firms

We examine the impact of higher order moments of changes in the exchange rate on stock returns of U.S. large-cap companies in the S&P500. We find a robust negative effect of exchange rate volatility on S&P500 company returns. The consumer discretionary and the consumer staples sectors have significant negative exposure to exchange rate volatility suggesting that exchange rate volatility affects stock returns through the channel of international operations. In terms of industries, the household products and personal products industries have significant negative exposure as well. The impact in the financial sector suggests that derivatives and hedging activity can mitigate exposure to exchange rate volatility. We find weak evidence that exchange rate skewness has an effect on S&P500 stock returns, but, find evidence that exchange rate kurtosis affects returns of companies that are more exposed to exchange rate volatility.     

A quantile-copula approach to dependence between financial assets

This article unveils the dependence structure between United States stock prices, crude oil prices, exchange rates, and U.S. interest rates. In particular, we employ linear and nonlinear estimation methods, such as quantile regression and the quantile-copula approach. Over the 1998–2017 period, we find that there is a positive relationship between the dollar value and the S&P 500 stock price, with the exception of the lower and upper tails of the stock return distribution. Further evidence is obtained on the dependence structure between other asset returns. The stock returns are negatively related to oil prices but positively to U.S. interest rates. Our results highlight the way that financial assets are linked, which have implications for risk management and monetary policy.     

The relationship between the smoothing of reported income and risk-adjusted returns

This paper uses risk-adjusted returns for the firms in the S&P 500 to test whether the stock market response to accounting performance measures is related to the smoothness of companies’ reported earnings. Three income models, increasing in their measure of smoothness, test the hypotheses using cumulative average abnormal returns. The results indicate that companies that report smooth income have significantly higher cumulative average abnormal returns than firms that do not. When size is considered, market returns are higher for small companies than for large companies. There is also a significant relationship between the type of industry and income smoothing.     

The potential effect of US baby-boom retirees on stock returns

Empirical studies demonstrated that US baby boomers consumption and savings patterns have affected economic aggregates over the past decades, among them equity returns. Boomers’ retirement is expected to mitigate the demand for equities until 2050, but its impact varies with the specific population age structure along decades. This paper employs a dynamic asset pricing model with optimum consumption and portfolio rules to estimate aging effects on S&P500 returns between 1950 and 2050. Calibration for demographic and economic data between 1950 and 2005 yields model estimates that significantly explain the moving average of S&P500 returns. Further, taking into account the present value of expected demographic effects until 2050 suggests that the S&P500 was fairly priced at the heart of the financial crisis, on April 2009, but overpriced thereafter.     

Day-of-the-week effect on the VIX. A parsimonious representation

The study of significant deterministic seasonal patterns in financial asset returns is of high importance to academia and investors. This paper analyzes the presence of seasonal daily patterns in the VIX and S&P 500 returns series using a trigonometric specification. First, we show that, given the isomorphism between the trigonometrical and alternative seasonality representations (i.e., daily dummies), it is possible to test daily seasonal patterns by employing a trigonometrical representation based on a finite sum of weighted sines and cosines. We find a potential evolutive seasonal pattern in the daily VIX that is not in the daily S&P 500 log-returns series. In particular, we find an inverted Monday effect in the VIX level and changes in the VIX, and a U-shaped seasonal pattern in the changes in the VIX when we control for outliers. The trigonometrical representation is more robust to outliers than the one commonly used by literature, but it is not immune to them. Finally, we do not find a day-of-the-week effect in S&P 500 returns series, which suggests the presence of a deterministic seasonal pattern in the relation between VIX and S&P 500 returns.     

Asymmetric volatility spillovers between international economic policy uncertainty and the U.S. stock market

This paper studies the asymmetric spillover effect of important economic policy uncertainty (EPU) on the S&P500 index. We use monthly EPU indexes from Australia, Canada, China, Japan, the U.K. and the U.S. and the realized volatility of the U.S. stock market to study the asymmetric pairwise directional spillovers on the U.S. stock market from 2000 to 2019. We find that S&P500 index volatility is a net recipient of spillovers from important EPU indexes. Japanese EPU has the strongest spillover effect on the U.S. stock markets, while EPU from the U.K. plays a very limited role. By decomposing the volatility into good and bad volatility, we find that the relationship between bad stock market volatility and EPU is stronger than between good volatility and EPU. Time-varying spillover characteristics show that bad volatility reacts more strongly to shocks in EPU following the debt crisis and trade negotiations. Several robustness checks are provided to verify the novelty of these findings.     

Selecting the portfolio investment strategy under political structure change in United States

This paper investigated whether stock market returns and volatilities were induced by change of long-term political structure. The empirical study finds that the political change is a crucial variable to DJIA and S&P 500 stock returns, but is insignificant to volatilities. But after the 1987 Crash, the political change has a positive effect on DJIA stock returns, and reduced the risk of DJIA and S&P 500. When political structure change, significant economic policies must submit to political realities and those proposed by previous governments often do not get implemented, resulting in market confusion. But following the increasing the consummation of market structure during post-1987 crash, hence, the political change effect increased DJIA stock returns, and reduced the risk of DJIA and S&P 500, and therefore the investors might be able to make a profit when they took active portfolio positions of DJIA.     

Modeling dynamic conditional correlations with leverage effects and volatility spillover effects: Evidence from the Chinese and US stock markets affected by the recent trade friction

In this article, we investigate the dynamic conditional correlations (DCCs) with leverage effects and volatility spillover effects that consider time difference and long memory of returns, between the Chinese and US stock markets, in the Sino-US trade friction and previous stable periods. The widespread belief that the developed markets dominate the emerging markets in stock market interactions is challenged by our findings that both the mean and volatility spillovers are bidirectional. We do find that most of the shocks to these DCCs between the two stock markets are symmetric, and all the symmetric shocks to these DCCs are highly persistent between Shanghai’s trading return and S&P 500′s trading or overnight return, however all the shocks to these DCCs are short-lived between S&P 500′s trading return and Shanghai’s trading or overnight return. We also find clear evidence that the DCC between Shanghai’s trading return and S&P 500′s overnight return has a downward trend with a structural break, perhaps due to the “America First” policy, after which it rebounds and fluctuates sharply in the middle and later periods of trade friction. These findings have important implications for investors to pursue profits.     

Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines dimensionality reduction, regime-switching models, and forecast combination to predict excess returns on the S&P 500. First, we aggregate the weekly information of 146 popular macroeconomic and financial variables using different principal component analysis techniques. Second, we estimate Markov-switching models with time-varying transition probabilities using the principal components as predictors. Third, we pool the models in forecast clusters to hedge against model risk and to evaluate the usefulness of different specifications. Our weekly forecasts respond to regime changes in a timely manner to participate in recoveries or to prevent losses. This is also reflected in an improvement of risk-adjusted performance measures as compared to several benchmarks. However, when considering stock market returns, our forecasts do not outperform common benchmarks. Nevertheless, they do add statistical and, in particular, economic value during recessions or in declining markets.     

The impact of the Financial Institutions Reform, Recovery, and Enforcement Act (FIRREA) on the value of S&L stocks

This study examines the effects of the Financial Reform, Recovery, and Enforcement Act of 1989 on the stock returns to shareholders of publicly traded savings and loans (S&Ls). Abnormal returns to stockholders are measured in response to each new piece of information concerning the passage of the Act. Using weekly data to have the largest possible sample, we found two significant time periods: a) the initial announcement of the Act and b) the time of passage and signing of the Act into law. We also provide evidence that stock return behavior differed between large and small S&Ls.     

Nonlinear effects of monetary policy on stock returns in a smooth transition autoregressive model

This paper employs smooth transition autoregressive (STAR) models to investigate the nonlinear effect of monetary policy on stock returns. The change in the Federal funds rate is used as an endogenous measure of monetary policy, and the growth rate of industrial production is also considered in the model. Our results show that the relationship between the monetary policy and excess returns on stock prices is positive and nonlinear. A decrease in the Federal funds rate causes a larger increase in excess returns if excess stock returns are located in the extreme low excess returns regime.     

Spillovers of currency carry trade returns,market risk sentiment,and U.S. market returns

This paper examines the link between spillovers of currency carry trade returns and U.S. market returns. Following Tse and Zhao (2012), this paper hypothesizes that the magnitude of spillovers of currency carry trade returns is positively correlated with market risk sentiment and, therefore, has an impact on market returns. Using the G10 currencies and S&P 500 index futures, the empirical results present a high magnitude of spillover effects of currency carry trade markets. The empirical findings also show a significantly positive relationship between spillovers of currency carry trade returns and subsequent market returns. Furthermore, the results indicate that this relationship is stronger in bear markets than in bull markets. Finally, our findings show that spillovers of currency carry trade returns significantly affect the subsequent transition probabilities of market returns.     

Economic uncertainty,oil prices,hedging and U.S. stock returns of the airline industry

This paper examines the impacts of economic policy uncertainty and oil price shocks on stock returns of U.S. airlines using both industry and firm-level data. Our empirical approach considers a structural vector-autoregressive model with variables recognized to be important for airline returns including jet fuel price volatility. Empirical results confirm that oil price increase, economic uncertainty and jet fuel price volatility have significantly adverse effect on real stock returns of airlines both at industry and at firm level. In addition, we also find that hedging future fuel purchase has statistically positive impact on the smaller airlines. Our results suggest policy implications for practitioners, managers of airline industry and commodity investors.     

Dynamic spillovers and linkages between gold,crude oil,S&P 500, and other economic and financial variables. Evidence from the USA

This paper focuses on the price determinants of gold, and on the challenges associated with gold’s safe haven property. Specifically, it analyses the interlinkages and the return spillover effect among gold, crude oil, S&P 500, dollar exchange rate, Consumer Price Index (CPI), economic policy uncertainty and Treasury bills, by employing a Vector Autoregression (VAR) and the spillover index of Diebold and Yilmaz (2012), Diebold and Yılmaz (2014). Monthly realized return series, covering the period from 2nd of January 1986 to 31st of December 2019 are used to examine the short-run linkages, and the return spillovers rolling-window estimates in analyzing the transmission mechanism in a time-varying fashion, respectively. Our findings identify gold as a strong dollar hedge, while crude oil and Treasury bills appear to drive inflation; they also indicate strong spillover effects between exchange rate and gold returns. In general, co-movement dynamics display state-dependent characteristics. Both total and directional spillovers increase significantly during market turbulence caused by severe financial crises such as the Global Financial Crisis (GFC) of 2007–2009 and the European Sovereign Debt Crisis of 2010–2012. Net spillovers switch between positive and negative values for all these markets, implying that the recipient/transmitter position changes drastically with market events. Economic policy uncertainty, stock market returns, and crude oil price returns are the main transmitters, while Treasury bills and CPI are the main return shock recipients. Gold and exchange rate act both as receivers and transmitters over the sample period.     

Unconventional monetary policy and the stock market’s reaction to Federal Reserve policy actions

We examine the change in the effect of Federal Reserve’s policy actions on stock returns after the Fed started to use unconventional policy actions. We find that the response of stock returns to monetary policy actions are almost seven times higher after the federal funds rate hit the zero lower bound. We conduct additional analysis to examine the underlying causes of the increase in the impact of monetary policy actions of stock returns. We show that investors rebalance their portfolios towards equity after selling Treasury securities to the Federal Reserve during large scale asset purchases.     

Conditional correlations and volatility spillovers between crude oil and stock index returns

This paper investigates the conditional correlations and volatility spillovers between the crude oil and financial markets, based on crude oil returns and stock index returns. Daily returns from 2 January 1998 to 4 November 2009 of the crude oil spot, forward and futures prices from the WTI and Brent markets, and the FTSE100, NYSE, Dow Jones and S&P500 stock index returns, are analysed using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer, Hoti, and Chan (2008), and DCC model of Engle (2002). Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the conditional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant conditional correlations is not supported empirically. Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the crude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that VARMA-AGARCH is superior to VARMA-GARCH and CCC.     

The VIX,the variance premium and stock market volatility

We decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while the conditional stock market variance predicts economic activity and has a relatively higher predictive power for financial instability than does the variance premium.     

Modeling and forecasting of stock index volatility with APARCH models under ordered restriction

This article examines volatility models for modeling and forecasting the Standard & Poor 500 (S&P 500) daily stock index returns, including the autoregressive moving average, the Taylor and Schwert generalized autoregressive conditional heteroscedasticity (GARCH), the Glosten, Jagannathan and Runkle GARCH and asymmetric power ARCH (APARCH) with the following conditional distributions: normal, Student's t and skewed Student's t‐distributions. In addition, we undertake unit root (augmented Dickey–Fuller and Phillip–Perron) tests, co‐integration test and error correction model. We study the stationary APARCH (p) model with parameters, and the uniform convergence, strong consistency and asymptotic normality are prove under simple ordered restriction. In fitting these models to S&P 500 daily stock index return data over the period 1 January 2002 to 31 December 2012, we found that the APARCH model using a skewed Student's t‐distribution is the most effective and successful for modeling and forecasting the daily stock index returns series. The results of this study would be of great value to policy makers and investors in managing risk in stock markets trading.     

Parametric pricing of higher order moments in S&P500 options

A general parametric framework based on the generalized Student t‐distribution is developed for pricing S&P500 options. Higher order moments in stock returns as well as time‐varying volatility are priced. An important computational advantage of the proposed framework over Monte Carlo‐based pricing methods is that options can be priced using one‐dimensional quadrature integration. The empirical application is based on S&P500 options traded on select days in April 1995, a total sample of over 100,000 observations. A range of performance criteria are used to evaluate the proposed model, as well as a number of alternative models. The empirical results show that pricing higher order moments and time‐varying volatility yields improvements in the pricing of options, as well as correcting the volatility skew associated with the Black–Scholes model. Copyright © 2004 John Wiley & Sons, Ltd.