Support vector machines for regression and applications to software quality prediction

Software metrics are the key tool in software quality management. In this paper, we propose to use support vector machines for regression applied to software metrics to predict software quality. In experiments we compare this method with other regression techniques such as Multivariate Linear Regression, Conjunctive Rule and Locally Weighted Regression. Results on benchmark dataset MIS, using mean absolute error, and correlation coefficient as regression performance measures, indicate that support vector machines regression is a promising technique for software quality prediction. In addition, our investigation of PCA based metrics extraction shows that using the first few Principal Components (PC) we can still get relatively good performance.

The performance and persistence of exchange-traded funds: evidence for iShares MSCI country-specific ETFs

The aim of this paper is to investigate the performance and persistence of 20 iShares MSCI country-specific exchange-traded funds (ETFs) in comparison with S&P 500 index over the period July 2001 to June 2006. There are several studies analysing mutual funds performance in past years, but very little is known about ETFs. In our analysis the Sharpe, Treynor and Sortino ratios are used as risk-adjusted performance measures. To evaluate performance persistence and therefore if there is any relationship among past performance and future performance, we apply to the Spearman Rank Correlation Coefficient and the Winner-loser Contingency Table. The main findings are at two levels. First, ETFs can beat the U.S. market index based on risk-adjusted performance measures. Second, there is evidence of ETFs performance persistence based on annual return.