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CEO compensation remains at the forefront of corporate financial discourse - primarily because of its interconnection with firm performance, growth and survivability. Diverse literature exists on CEO compensation and shareholder return, albeit with different findings and/or disagreements. This paper aims to apply a single independent variable enquiry - the CEO total payment to understand how it might influence shareholder return. Research employs a quantitative approach by using a simple OLS technique with data drawn from a random sample of fifty-six companies listed in the S&P 500 Index. The S&P 500 is a stock market index tracking the stock performance of 500 large companies listed on stock exchanges in the United States. It is one of the most commonly followed equity indices. The OLS result yields a P-value of 0.0001, with a negative regression coefficient, which thus indicates a significant negative relationship between the CEO total payment and shareholder return. The result corroborates some extant research regarding the inability of agency theory to provide an overriding explanation for the CEO pay and return relationship. Further research is therefore encouraged to apply more variables, particularly those that may capture CEO's opportunistic behaviour in future CEO and stock return analysis. [ABSTRACT FROM AUTHOR]

This study examines the empirical relationship between unusual trading volume and earnings surprises in China's A-share market. We provide evidence that an unusually low trading volume can signify negative information about firm fundamentals. Moreover, unusual trading volumes could predict abnormal returns close to the earnings announcement date. The degree of, and changes in, divergence of opinion could explain this result. Our study provides an insight into China's market, where short sales are strictly forbidden. We report a strong relationship that is quite different from that described in most studies on the United States market. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method to classify the stock cluster based on the motions of stock returns. Specifically, there are three criteria: (1) The positive or negative signs of elements in the eigenvector of correlation matrix indicate the response direction of individual stocks. (2) The components are included based on the sequence of corresponding eigenvalue magnitudes from large to small. (3) All the elements in the cluster representing individual stocks should have same signs across the components included in the classification process. With the number of vectors included in the process increasing, a speed-up process for cluster number is identified. We interpret this phenomenon as a phase transition from a state dominated by meaningful information to one dominated by trivial information. And a critical point exists in this process. The sizes of clusters near this critical point can be regarded as a power law distribution. The critical exponent evolvement indicates structure of the market. The vector number at this point can be adopted to classify the stock clusters. We analyze the cross-correlation matrices of stock logarithm returns of both China and US stock market for the period from January 4, 2005 to December 31, 2010. The period includes the anomalies time of financial crisis. The number of clusters in financial and technology sectors is further examined to reveal the varying feather of traditional industries. Distinct patterns of industrial differentiation between China and US have been found according to our study. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multifractality of Chinese and the U.S. stock markets using a multifractal detrending moving average algorithm. The results show that stock returns in both markets are multifractal at a similar extent. We detect the source of multifractality and find that long-range correlations are one of the major sources of multifractality in the US market but not in the Chinese market. Fat-tailed distribution plays a crucial role in multifractality of both markets. As an innovation, we quantify the effect of extreme events on multifractality and find the strong evidence of their contribution to multifractality. Furthermore, we investigate the usefulness of popular ARFIMA-GARCH models with skew-t distribution in capturing multifractality. Our results indicate that these models can capture only a fraction of multifractality. More complex models do not necessarily perform better than simple GARCH models in describing multifractality in stock returns. [ABSTRACT FROM AUTHOR]

This paper analyzes investor behavior depending on the flow-through capability (FTC) in the US stock market, because investors seek protection from inflation rate changes, and the FTC (a firm's ability to transmit inflation shocks to the prices of its products and services) is a key factor in investment decisions. Our estimates of the FTC of firms listed on the US stock exchange at the sector level are significantly different among industries, and we demonstrate a direct relationship between changes in stock prices (at the sector level) and FTC. These results would be relevant because they have important implications on investor behavior. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper is to compare and contrast the effect of individual and institutional sentiments on the US stock returns during a prolonged bull phase of ten years in the 1990s compared to shorter boom and bust cycles of the 2000s. The study is focussed on a set of stocks that are prone to sentiments and speculations. Design/methodology/approach -- To compare the dynamic interaction of individual and institutional sentiments and stock returns, the authors use the vector autoregression (VAR) approach. The VAR model has proven to be especially useful for describing the dynamic behavior of economic and financial time series because it does not impose a priori restriction on the structure of the system. Using impulse response function, the authors determine how stock returns respond over time to a shock in institutional and individual sentiments. Findings -- The authors find that sentiments of individual investors can affect returns mostly when there is a prolonged upward trend in stock prices, while sentiments of institutional investors can impact the returns when stock market is more volatile. Originality/value -- This paper compares the effect of noise traders and rational investors' sentiment on stock returns during the persistent period of positive abnormal returns of the 1990s and the more volatile stock returns of the 2000s. [ABSTRACT FROM AUTHOR]

The Internet has been extremely popular because of its many unique and powerful characteristics, such as ubiquity and global reach. Hence, it is interesting to study whether Internet companies can be sustained through a bubble and even after a bubble. This study therefore compares the Internet companies in the USA with other high-tech companies listed on the National Association of Securities Dealers Automated Quotations (NASDAQ) over a period covering the years from 1995 to 2006. First, we check whether there has been a bubble for these Internet companies, and we then try to compare the pre- and post-bubble periods to ascertain whether any of their stocks have been 'overvalued'. Conclusions are drawn regarding the respective futures of these Internet companies. [ABSTRACT FROM AUTHOR]

In this paper, various financial variables are examined as predictors of the probability of a recession in the USA and Germany. We propose a new dynamic probit model that outperforms the standard static model, giving accurate out-of-sample forecasts in both countries for the recession period that began in 2001, as well as the beginning of the recession in 2008. In accordance with previous findings, the domestic term spread proves to be an important predictive variable, but stock market returns and the foreign term spread also have predictive power in both countries. In the case of Germany, the interest rate differential between the USA and Germany is also a useful additional predictor. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper examines autocorrelation and cross-autocorrelation patterns for selected Asian stock returns. Special attention is given to examination of Asian stock returns and the impact on them of the past information. By employing a class of asymmetric specification of conditional mean and conditional variance models, we find the autocorrelation coefficient to be negative for the Japanese market and positive for the rest of the Asian markets studied. Our findings suggest that the Asian markets respond sensitively to the US market, especially on the down side. The asymmetric effects are found to be present in both mean and variance equations. The evidence is consistent with behavior in which investors in Asian markets tend to react more significantly to negative stock news originating from US sources than they do to positive news. [ABSTRACT FROM AUTHOR]