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This study employs the quantile regression model to examine the non-monotonic impact of CEO stock-based compensation on firm performance, using the data for U.S. non-financial firms from 1993 to 2005. The results indicate that while the impact of CEO stock-based pay on firm performance is positive for firms in the higher earnings quantile levels, the impact is negative for firms in the lower levels. In addition, the "V-shaped" relationship between CEO stock-based pay and firm performance satisfactorily explains the longstanding disagreement among earlier studies with regard to whether CEO stock-based pay can enhance firm performance. Furthermore, the quantile-varying pattern of the impact of stock-based compensation on firm performance is robust after controlling for the industrial and yearly effects. It is also robust to the use of the payfor-performance sensitivity as an alternative explanatory variable or the market-based measure of performance as the dependent variable, or the consideration of the suspected endogenous problem between firm performance and stock-based compensation.

A multivariate Markov-switching ARCH (MVSWARCH) model in which variance/correlations for stock returns is controlled by a state-varying mechanism is introduced and used to design a state-varying US-EM (emerging market) portfolio establishment strategy. Additionally, a conventional random-variance framework, the MVGARCH (multivariate GARCH) model, in which a time-varying technique is involved is employed and subjected to comparative analysis. The empirical results are consistent with the following notions: First, as being consistent with a study conducted by Ramchand and Susmel , the US-EM market correlations are higher when the US market is more volatile. However, this study further indicates that the US-EM market correlations increase relatively more when both the US and EM markets simultaneously experience a high variance condition. Moreover, the situation of both the US and EM stock markets at a high volatility state is associated with a minimum risk reduction benefit and a maximum cross-market correlation. Second, the state-varying portfolio loadings established by the MVSWARCH model could effectively enhance asset allocation effectiveness; however, this benefit arises more as a result of risk reduction than an increase in mean returns. Copyright (c) 2009 The Authors. Journal compilation (c) 2009 Economic Society of South Africa.

As documented in the literature, the effects of firm size, financial leverage, and R&D expenditures on firm earnings are inclusive. Our hypothesis is that the inconsistent empirical results of such effects may be driven by the regression models implemented in data analysis. Using the quantile regression (QR) approach developed by Koenker and Basset (1978), this study analyses S&P 500 firms from 1996 to 2005. We find that the effects of firm size, financial leverage and R&D expenditures on firm earnings differ considerably across earnings quantiles. Comparing the results from the QR approach with those from the ordinary least squares (OLS) and least absolute deviation (LAD) methods, this study further explains the puzzling relationship between firm size, financial leverage, R&D expenditures and firm earnings.