PurposeThis study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method.Design/methodology/approachIn this context, data sets containing daily opening and closing prices between 2001 and 2019 have been created for 11 stock market indexes in the world. The association rule algorithm, one of the data mining techniques, is used in the analysis of the data.FindingsIt is observed that the US stock market indices take part in the highest confidence levels between association rules. The XU100 stock index co-movement with both the European stock market indices and the US stock indices. In addition, the Hang Seng Index (HSI) (Hong Kong) takes part in the association rules of all stock market indices.Originality/valueThe important issue for data sets is that the opening/closing values of the same day or the previous day are taken into account according to the open or closed status of other stock market indices by taking the opening time of the stock exchange index to be created. Therefore, data sets are arranged for each stock market index, separately. As a result of this data set arranging process, it is possible to find out co-movements of the stock market indexes. It is proof that the world stock indices have co-movement, and this continues as a cycle.

This study aims to provide a roadmap for research dealing with the tourism sector. In this context, by conducting a study in the form of a literature review, researchers are informed about what has been done and what is missing. In the study, articles that have been accepted from scientific journals indexed in the SCOPUS database before January 18, 2021 and dealing with COVID-19 and tourism issues are examined. The study was carried out in two stages. In the first stage, descriptive statistics were given in terms of the region studied in the articles, the journal in which the articles were published, and the methods used in the articles from a general perspective. In the second stage, articles are divided into sections such as title, keywords, abstract, and conclusion. Each article section has been analyzed separately with text mining and clustering analysis, taking into account both single and double-word groups. As a result of analysis, it was determined that theoretical studies were carried out and quantitative methods were used in most of the studies.