Identification of financial statement fraud in Greece by using computational intelligence techniques

The consequences of financial fraud are an issue with far-reaching for investors, lenders, regulators, corporate sectors and consumers. The range of development of new technologies such as cloud and mobile computing in recent years has compounded the problem. Manual detection which is a traditional method is not only inaccurate, expensive and time-consuming but also they are impractical for the management of big data. Auditors, financial institutions and regulators have tried to automated processes using statistical and computational methods. This paper presents comprehensive research in financial statement fraud detection by using machine learning techniques with a particular focus on computational intelligence (CI) techniques. We have collected a sample of 2469 observations since 2002 to 2015. Research gap was identified as none of the existing researchers address the association between financial statement fraud and CI-based detection algorithms and their performance, as reported in the literature. Also, the innovation of this research is that the selection of data sample is aimed to create models which will be capable of detecting the falsification in financial statements

Board independence and market reactions around news of stock option backdating

Board Independence, Option Backdating, G3, G34,

Board of director gender and corporate tax aggressiveness: An empirical analysis

This study examines the impact of board of director gender diversity on corporate tax aggressiveness. Based on a sample of 418 U.S. firms covering the 2006–2009 period (1672 firm-year observations), our ordinary least squares regression results show a negative and statistically significant association between female representation on the board and tax aggressiveness after controlling for endogeneity. Our results are consistent across several measures of tax aggressiveness and additional robustness checks