我国的证券市场是一个年轻的市场，建立至今财务欺诈事件屡屡发生。各大财务欺诈事件给广大投资者带来巨大损失。因此，建立一个 A 股市场的财务欺诈识别模型，是近年来研究资本市场的学者们广泛关注的热点。 本文通过回顾国内外有关财务欺诈的文献，基于财务欺诈动因、财务欺 诈手段、财务欺诈的识别模型三部分进行了总结整理。笔者发现经典的 logistic 多元回归模型在国内外学者的研究中都得到了检验。同时，传统的 Beneish 模 型在 A 股市场并不具备普适性，且目前已有的关于财务识别模型的样本时间跨度较少。出于对于财务欺诈动机和手段的研究，本文选定了 6 种财务指标来作为研究变量。并且，利用 2009-2019 年年报中被证监会公开处罚的利润欺诈公司作为欺诈样本，并分别为其选择了同行业、同年度，且资产规模最接近的公司作为控制样本。在对各指标进行了独立样本 T 检验之后，笔者利用 stata 软件，在 logistic 多元回归模型的基础上进行了实证分析，最终得到了 A 股上市公司的财务欺诈识别模型，并进行了模型检验。最后，本文对同济堂进行了案例分析，并为监管部门提出了几点启示。

Financial fraud incidents have occurred repeatedly in A-share market, which greatly affects investor confidence. Therefore, establishing a financial fraud identification model for A-share market has been widely concerned by scholars in recent years.After wide reading papers on financial fraud, I found that the logistic model has been tested in the research of scholars at home and abroad. At the same time, Beneish model is not universal in the A-share market. To establish the model, I selected 6 financial indicators as variables, and companies criticized of profit manipulation are selected as samples. Finally, 66 fraud samples are obtained, and the companies in the same industry and year with the closest asset scale are selected as the control sample, that is, the non-fraud group. After independent sample t-test of each index and eliminating four insignificant indicators, I finished logistic multiple regression model with stata, and finally obtains the financial fraud identification model of A-share listed companies. Through this model, the probability of financial fraud of A-share listed companies can be calculated. The model checking is also carried out. Finally, I carried out an case study. Using this model, it can be calculated that the probability of financial fraud in Tongjitang is 74.84%. After that, I puts forward several inspirations for the regulatory authorities, including increasing the cash flow related indicators, increasing the punishment of financial fraud companies, and paying attention to the operating conditions of their affiliated companies.