# Zscore Model Tutorial > 来源：https://uqer.io/community/share/54ab4407f9f06c276f6519ec ## 1. 什么是 Zscore Model 信用风险评分方法通常包括定性法、单变量法、多变量法，目前广泛采用多变量方法包括如判别分析(discriminant analysis)、逻辑回归(logit regression)和非线性模型如神经网络等。奥特曼博士于1968年发表的Z-score模型基于多变量判别分析方法。 Z-score模型基于各变量加权得分对企业是否破产进行判断，在Z-score原始模型中，得分高于2.99的属于“安全”区域、低于1.80的属于“困境”区域，两个得分之间的属于“灰色”区域。 Z-score模型在美国企业如柯达、通用汽车的破产预测得到了很好的结果。 ## 2. 本模块提供的 Zscore Model 下面我们从原理，模型公式，划分区间三个方面来简单介绍一下本模块中的两个Z-score模型： ### 2.1 All Corporate Bonds 模型原理 + No equity prices are needed + Uses discriminant analysis methodology + Coefficients are obtained from China distressed firm dataset from historical periods 模型公式 ``` ZScore = 0.517 - 0.460*TotalLiabilities/TotalAssets + 9.320*NetProfit/0.5*(TotalAssets+TotalAssets[last]) + 0.388*WorkingCapital/TotalAssets + 1.158*RetainedEarnings/TotalAssets = 0.517 - 0.460*x1 + 9.320*2/x2 + 0.388*x3 + 1.158*x4 ``` 划分区间 + `Z-score < 0.5`: 已经违约 + `0.5 < Z-score < 0.9`: 有违约的可能性 + `Z-score > 0.9`: 财务健康，短期内不会出现违约情况 ### 2.2 Corporate Bonds with Equity Listings 模型原理 + Uses Equity Prices: Information from equity set + Uses discriminant analysis methodology + Coefficients are obtained from China distressed firm dataset from historical periods 模型公式 ``` ZScore = 0.2086*x1 + 4.3465*x2 + 4.9601*x3 x1: market value/book value of TotalLiabilities x2: total sales/TotalAssets x3: (TotalAssets-TotalAssets[last])/TotalAssets[last] coef = [0.2086, 4.3465, 4.9601] ``` 划分区间 + `Z-score < 1.5408`: 已经违约 + `Z-score > 1.5408`: 财务健康，短期内不会出现违约情况 ## 3. 如何实用本模块提供的 Zscore Model 接口 本模块目前提供了四个Z-score模型接口，分别如下： ### 3.1 `zscore_ACB` ``` --- Interface for calculating zscore using ACB. parameter: ticker[string]: ticker code parameter[opt]: begin[int]: year begin, default 2010 end[int]: year end, default 2014 coef[list of int]: coefficients for Zscore Model, default [0.517, -0.460, 18.640, 0.388, 1.158] --- when success, will return factors and status code, as a dict; when failed for some reason, will return error message and error code, as a dict; --- Model ACB: All Corporate Bonds ZScore = 0.517 - 0.460*TotalLiabilities/TotalAssets + 9.320*NetProfit/0.5*(TotalAssets+TotalAssets[last]) + 0.388*WorkingCapital/TotalAssets + 1.158*RetainedEarnings/TotalAssets = 0.517 - 0.460*x1 + 9.320*2/x2 + 0.388*x3 + 1.158*x4 coef = [0.517, -0.460, 18.640, 0.388, 1.158] ``` ### 3.2 `zscore_ACB_List` ``` --- Interface for calculating zscore using ACB. parameter: ticker[list]: ticker code parameter[opt]: begin[int or list]: year begin, default 2010 end[int or list]: year end, default 2014 coef[list of int]: coefficients for Zscore Model, default [0.517, -0.460, 18.640, 0.388, 1.158] --- ``` ### 3.3 `zscore_ACBEL` ``` --- Interface for calculating zscore using ACBEL. parameter: ticker[string]: ticker code parameter[opt]: begin[int]: year begin, default 2010 end[int]: year end, default 2014 coef[list of int]: coefficients for model, default [0.2086, 4.3465, 4.9601] --- when success, will return factors and status code, as a dict; when failed for some reason, will return error message and error code, as a dict; --- Model ACB: All Corporate Bonds with Equity Listings ZScore = 0.2086*x1 + 4.3465*x2 + 4.9601*x3 x1: market value/book value of TotalLiabilities x2: total sales/TotalAssets x3: (TotalAssets-TotalAssets[last])/TotalAssets[last] coef = [0.2086, 4.3465, 4.9601] --- ``` ### 3.4 `zscore_ACBEL_List` ``` --- Interface for calculating zscore using ACBEL. parameter: ticker[list]: ticker code parameter[opt]: begin[int or list]: year begin, default 2010 end[int or list]: year end, default 2014 coef[list of int]: coefficients for Zscore Model, default [0.2086, 4.3465, 4.9601] --- ``` ## 4. 一个 Zscore Model 实例 相关步骤说明如下： + 4.1 导出该模块 + 4.2 输入股票代码，可选择性输入计算时间区间，以年为单位 + 4.3 得到计算结果 + 4.4 作图分析 ```py # 4.1 & 4.2 GZMT = zscore_ACB('600519') # 4.3 factors = GZMT['factors'] # 4.4 from matplotlib.pylab import plot plot(factors['zscore']) [<matplotlib.lines.Line2D at 0x4eda750>] ```  ## 5. 本模块未来版本规划 为适应光大金融人士更加方便地使用 Z-Score Model，本模块将在以下几个方面对本模块的未来版本进行规划： + 更加简单：提供上传模板，用户只需按照一定格式上传自己持仓的 excel 表格，即可在零编码的情况下得到相应持仓债券、股票发行人的 Z-Score 情况； + 更加灵活：将会允许用户在我们定义的两个 Z-Score 模型的相关系数； + 更加智能：预计提供给用户将近50个公司财务相关的因子，由用户自定义因子和相关的系数来计算 Z-Score 值；