No abstract

FDI, Income inequality, Globalization, Trade volume, Panel cointegration, C10, F01, F15, J30,

Summary Only in the U.S. Stock Exchanges, the daily average trading volume is about 7 billion shares. This vast amount of trading shows the necessity of understanding the hidden insights in the data sets. In this study, a data mining technique, clustering based outlier analysis is applied to detect suspicious insider transactions. 1,244,815 transactions of 61,780 insiders are analysed, which are acquired from Thomson Financial, covering a period of January 2010–April 2017. In order to detect outliers, similar transactions are grouped into the same clusters by using a two‐step clustering based outlier detection technique, which is an integration of k‐means and hierarchical clustering. Then, it is shown that outlying transactions earn higher abnormal returns than non‐outlying transactions by using event study methodology.

No abstract

Currently, a great deal of empirical research has been done to test and verify Kahneman and Tversky's (1979) prospect theory. Nevertheless, the research only addresses private or institutional investors in the financial industry and has neglected specific tests in the real sector. In this paper, our goal is to examine whether the implications of prospect theory and the house money effect also hold for the power traders in the energy sector. The results show that the reflection and house money effects do not hold. Our results indicate the possible influence of the recent global crisis on prospect theory.