DETECTING FALSE FINANCIAL STATEMENTS FOR CHINESE COMPANIES LISTED IN THE UNITED STATES

Abstract

This study constructs a False Financial Statement (‘FFS’) detection model for Chinese companies traded in the United States. The sample contains 86 Chinese Reverse Merger companies, 77 U.S. Reverse Merger companies, 95 Chinese IPO companies, and 85 U.S. IPO companies. After carefully selecting the sample companies in each group to resemble the group of Chinese Reverse Mergers, this study uses logit case-control regression analysis to examine differences in financial ratios between FFS and non-FFS companies, using class action lawsuits filed with the Securities and Exchange Commission to identify fraud. The empirical results reveal that certain indicators do exist, and these indicators reveal a different kind of fraud than has previously been detected. Consequently, the results suggest that not all U.S.-listed Chinese companies issue lowquality financial statements, and it is possible to distinguish between fraudulent and nonfraudulent Chinese firms. Furthermore, the recent underperformance of this group may be the result of initial Sino-phobic investor sentiment. This study then uses the model to develop a hypothetical investment strategy, based solely on fraudulent characteristics. The two variations of the investment strategy are both profitable in 2012, adding another angle to analyze the Negative Spillover Effect. *Commonly Used Acronyms RM: Reverse Merger IPO: Initial Public Offering CRM: Chinese Reverse Merger USRM: U.S.-based Reverse Merger USIPO: U.S. IPO Company CHIPO: Chines IPO Company FFS Falsified Financial Statement(s)