Abstract
By combining the similarity matching (SM) method with the utilities additives discriminates (UTADIS) method, we propose a hybrid SM-UTADIS approach to detect falsified financial statements (FFS) of listed companies. To evaluate the performance of this hybrid approach, we conduct experiments using the annual financial ratios of listed traditional Chinese medicine (TCM) companies in China. There are three stages in the detection procedure. First, we use the cosine similarity matching method to select matched companies for each considered company, derive the deviation data of each considered company as a sample dataset to capture the intrinsic law of the financial data, and further divide these into training and testing datasets for the next two stages. Second, we put the training dataset into the UTADIS to train the SM-UTADIS model. Finally, we use the trained SM-UTADIS model to classify the testing dataset and evaluate the performance of the proposed method. Furthermore, we use other approaches, such as single UTADIS and logistic and SM-logistic regression models, to detect FFS. By comparing these results to those of the hybrid SM-UTADIS approach, we find that the proposed hybrid approach greatly improves the accuracy of FFS detection.
Highlights
Falsified financial statements (FFS) are deliberate misstatements of material facts by management in a company’s accounts with the aim of deceiving investors and creditors
We introduce the cosine similarity algorithm to help us select companies most similar to the matched companies and use their financial data to compute the deviation of the considered company. en, the deviation data are used for utilities additives discriminates (UTADIS) classification. e merit of the financial deviation data is that they reflect the intrinsic law of a considered company, making it easier to detect FFS with UTADIS. is is the main contribution of this paper, that is, based on the UTADIS method, we combine the similarity algorithm with UTADIS and formulate an integrated method, similarity matching (SM)-UTADIS, for detecting FFS
There are about 150 companies listed in the TCM sector in China, but most are involved in mixed business areas, and the main profit of some is not earned through traditional Chinese medicine
Summary
Falsified financial statements (FFS) are deliberate misstatements of material facts by management in a company’s accounts with the aim of deceiving investors and creditors. FFS primarily consist of overstating profit, sales, or assets or understating liabilities, expenses, or losses [1,2]. Such illegitimate behaviours have a severe effect on the global economy because they significantly undermine the confidence of investors and creditors. Falsified financial statements have become a serious problem worldwide, especially in some fast-growing countries like China, where FFS often cause investor failure, such as huge losses. Detecting FFS has attracted considerable attention from investors, creditors, regulators, academic researchers, etc
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