The main objective of this study was to investigate whether searching firm's ticker symbol and name in Google can predict its future stock market activities. In so doing, the data related to firms' ticker symbol and firms' name were collected using Google Trend and stock market activity was measured using four proxies, namely abnormal return, return volatility, stock trading volume and stock trading count. In order to meet the main objective of the study, multiple regression and panel regression were used over13082 firmmonth observation during the years between 2005 and 2018. The results showed that the future market activity, including return volatility, stock trading volume and stock trading count, increased with searching the firms' ticker and name in Google. However, there was no significant relationship between the future abnormal return and Google searches. Findings also showed that future market activity can be predicted using Google searches. In addition, there was a more significant relationship between the searched firms' ticker symbol than the searched firms' name and future market activity.

The present investigation concerns evaluation, comparison and analysis of search options existing within web-based meta-search engines. 64 meta-search engines were identified. 19 meta-search engines that were free, accessible and compatible with the objectives of the present study were selected. An author’s constructed check list was used for data collection. Findings indicated that all meta-search engines studied used the AND operator, phrase search, number of results displayed setting, previous search query storage and help tutorials. Nevertheless, none of them demonstrated any search options for hypertext searching and displaying the size of the pages searched. 94.7% support features such as truncation, keywords in title and URL search and text summary display. The checklist used in the study could serve as a model for investigating search options in search engines, digital libraries and other internet search tools.

One of the most challenging and difficult aspects of translation is related to the translation of idioms by both machine systems and human translators. The problem arises from the recognition of the idioms on the one hand and translating them on the other hand. idiom recognition is not easy since an idiom may change its form based on the context in which it is used. Using systematic idiom collections and special rules to recognize idioms, the problem can be solved to a great extent. Moreover, the inclusion of a morphological as well as syntactic parser can greatly enhance the idiom recognition and translation process. The aim of this paper is twofold. First, we want to test Google Translate to see how well it is capable of translating idioms and next to provide means to improve the quality of translation of idioms by Google Translate.