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Atomic halogen elimination from halogen-related compounds plays a vital role in the depletion of the ozone layer and is well investigated. However, the probabilities for elimination of molecular ha...

A photodissociation experiment of (R)-3-bromocamphor was carried out by the hexapole orientation technique by (2 + 1) REMPI detection at about 234 nm. The atomic bromine products in excited state (...

This article examines the profitability of trading rules based on the smoothed probability of Markov-switching models and executes two models in Taiwan's case. The results present that both proposed models can earn excess returns over the buy-and-hold strategy and support that both can be used to trade. However, the univariate Markov-switching model, which only uses daily returns series does not successfully capture the trend in the stock market, especially during a bull market. This implies that high-frequency returns series contain lots of noises. In order to overcome this problem, the Markov-switching vector autoregression model that combines a leading indicator and returns is performed in this study. The results indicate a better trading pattern. We conclude that the leading indicator chosen from open interest in the future market increases useful information and reduces noises to improve model estimation, which can well identify the position of bull and bear markets.

This article follows a three-stage data envelopment analysis (DEA) approach proposed by Fried et al. (2002) to decompose mutual fund underperformance, in order to obtain pure managerial performance. In the first stage, DEA is used to compute each fund's performance. In the second stage, a stochastic frontier regression decomposes fund underperformance into characteristics (including fund and management attributes), managerial inefficiency, and statistical noise. In the third stage, DEA with slack-adjusted data is used to find out the pure performance. It is found that a fund's performance significantly increases with its size, previous performance, manager's tenure and education, while it decreases with the age of the fund and number of managed funds.