供应链共同审计是指供应链上多个企业共享同一审计机构或同一审计资源。由于供应链上下游企业之间存在高度的依赖性和协作需求，选择共享审计可以降低审计成本、提高审计效率。此外，相关研究表明，供应链共同审计在减少审计风险与审计费用、降低公司财务重述与提升审计质量、提高资本配置效率以及提升劳动收入份额等方面发挥着积极的影响作用。

供应链共同审计的定义有两种角度，一种是供应链上两家企业年报审计时选择同一家会计师事务所（杨清香，2015），另一种是签字注册会计师为同一人。第一种定义的要求相对更宽松，能获得更多的样本，大多数研究也都选择第一种。本文也选择第一种定义来构造共同审计的样本。

本文选取的样本期间为 2001-2023 年，以披露了前五大客户（供应商）的 A 股上市公司为初始样本。在此基础上，剔除金融行业的样本、剔除交易状态异常的样本、剔除上市未满一年的样本、剔除变量缺失的样本，借鉴杨清香等（2015）的研究，若目标企业在一个会计年度内与其前五大供应商或客户中至少一家聘用同一家会计师事务所，则判定为共同审计。最终获得与上游供应商共同审计 98 个样本，与下游客户共同审计 153 个样本，混合样本 243 个。

前五大供应商数据

数据来源：国泰安-公司研究系列-供应链研究-上市公司供应链信息-前五大供应商信息表，选择剔除 ST、金融业（证监会 2012 版分类）的全部 A 股

年份整理：2001-2023
剔除客户为非上市公司的样本。非上市公司未披露审计机构相关信息，无法判断是否共同审计

import pandas as pd
import numpy as np

raw_data = pd.read_excel(
    "./Data/前五大供应商采购信息表2001-2023/SC_TopFivePurchaseInfo.xlsx",
    dtype={"Symbol": str, "BusinessSymbol": str},
)

raw_data.head()

	Symbol	EndDate	StateTypeCode	Rank	BusinessInstitutionID	InstitutionName	IsListed	BusinessSymbol	ProportionOfTotalValue
0	000002	2001-12-31	1	6	NaN	合计	N	NaN	27.48
1	000002	2002-12-31	1	6	NaN	合计	N	NaN	22.67
2	000002	2003-12-31	1	6	NaN	合计	N	NaN	26.96
3	000002	2004-12-31	1	6	NaN	合计	N	NaN	26.46
4	000002	2005-12-31	1	6	NaN	合计	N	NaN	21.70

# 选择供应商为上市公司的样本，供应商为上市公司才能获取审计信息
data_cleaned_by_supplier = raw_data.query("IsListed == 'Y'")
# 考虑合并报表
data_cleaned_by_stateTypeCode = data_cleaned_by_supplier.query(
    "StateTypeCode == 1"
).reset_index(drop=True)

data_cleaned_by_stateTypeCode = data_cleaned_by_stateTypeCode.loc[
    :, ["Symbol", "EndDate", "BusinessSymbol", "ProportionOfTotalValue"]
]

# 个股信息数据
stock_info = pd.read_excel(
    "./Data/上市公司基本信息年度表2000-2023/STK_LISTEDCOINFOANL.xlsx",
    dtype={"Symbol": str, "PROVINCECODE": str, "CITYCODE": str},
)

stock_info.head()

	Symbol	ShortName	EndDate	ListedCoID	IndustryName	IndustryCode	PROVINCECODE	CITYCODE	LISTINGSTATE
0	000002	深万科A	2000-12-31	101775	房地产开发与经营业	J01	440000	440300	正常上市
1	000002	深万科A	2001-12-31	101775	房地产开发与经营业	J01	440000	440300	正常上市
2	000002	万科A	2002-12-31	101775	房地产开发与经营业	J01	440000	440300	正常上市
3	000002	万科A	2003-12-31	101775	房地产开发与经营业	J01	440000	440300	正常上市
4	000002	万科A	2004-12-31	101775	房地产开发与经营业	J01	440000	440300	正常上市

stock_info = stock_info.loc[:, ["Symbol", "EndDate", "IndustryCode", "LISTINGSTATE"]]
# 合并 data_cleaned_by_stateTypeCode 与 stock_info 获取行业和上市情况信息
sample_merged_with_info = data_cleaned_by_stateTypeCode.merge(
    stock_info, how="left", on=["Symbol", "EndDate"]
)

sample_merged_with_info.head()

	Symbol	EndDate	BusinessSymbol	ProportionOfTotalValue	IndustryCode	LISTINGSTATE
0	000004	2012-12-31	301093	4.30	C27	正常上市
1	000004	2014-12-31	002099	2.08	C27	正常上市
2	000004	2016-12-31	301093	1.09	C27	正常上市
3	000009	2013-12-31	600998	2.48	S90	正常上市
4	000010	2012-12-31	300820	8.04	C35	正常上市

# 剔除 ST *ST
sample_excluded_ST = sample_merged_with_info.query(
    "LISTINGSTATE == '正常上市'"
).reset_index(drop=True)

# 处理上游供应商的股票代码
# 部分供应商同时在香港和内地上市、发行 B 股、在美国上市等等，BusinessSymbol 代码需要处理
def filter_ticker(ticker):
    return len(ticker) == 6

# 查看 BusinessSymbol 非 6 位的样本
business_symbol = sample_excluded_ST.loc[
    ~sample_excluded_ST.BusinessSymbol.apply(lambda ticker: filter_ticker(ticker)), :
].BusinessSymbol.unique()

business_symbol

array(['000898;00347', '02318;601318;82318', '00338;600688;SHI', '01606',
       '01099', '01066', '000338;02338', '01898;601898', '01072;600875',
       '01786', '06869;601869', '000761;200761', '02600;601600;ACH',
       '02088', '00570', '688223;JKS', '01171;600188', '002202;02208',
       '00857;601857;PTR', '000030;200030', '01727', '01800;601800',
       '01033;600871', '00386;600028;SNP', '01133', '000581;200581',
       '02607;601607', '02196;600196', '00564;601717', '02170', '04333',
       '600819;900918', '000012;200012', 'SGOC', '02899;601899',
       '06655;600801', 'CCIH', '00358;600362', 'JASO', '000553;200553',
       '03988;04619;601988', '000726;200726', '002594;01211;81211',
       '03996;601868', '02727;601727', '600776;900941', 'LDK', '00438',
       '01055;600029;ZNH', '00753;601111;AIRC', '00670;600115;CEA',
       '600221;900945', '00981;688981;SMI', '01385;688385',
       '00728;601728;CHA', '600663;900932', 'JD', '01527', '002460;01772',
       'VNET', '5FI', '01053;601005', '00323;600808', '06881;601881',
       '870800;920016', '06127;603127', '01088;601088', '000157;01157',
       '01354', '01398;04620;601398', '09988;89988;BABA', '00992;80992',
       '06066;601066', '01186;601186', '002466;09696', '00568;002490',
       '000333;00300', '000756;00719', '06188', '000521;200521', '04335'],
      dtype=object)

# 获取 A 股列表
stocks_array = stock_info.Symbol.unique()


# 如果客户 BusinessSymbol 列中的代码在股票列表中，则返回 True
def filter_customer(symbol):
    symbol_list = symbol.split(";")
    for s in symbol_list:
        if s in stocks_array:
            return True
    return False

sample_excluded = sample_excluded_ST.loc[
    sample_excluded_ST.BusinessSymbol.apply(lambda symbol: filter_customer(symbol)), :
].reset_index(drop=True)

sample_excluded.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1791 entries, 0 to 1790
Data columns (total 6 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   Symbol                  1791 non-null   object 
 1   EndDate                 1791 non-null   object 
 2   BusinessSymbol          1791 non-null   object 
 3   ProportionOfTotalValue  1773 non-null   float64
 4   IndustryCode            1791 non-null   object 
 5   LISTINGSTATE            1791 non-null   object 
dtypes: float64(1), object(5)
memory usage: 84.1+ KB

# 处理 BusinessSymbol
def customer(symbol):
    symbol_list = symbol.split(";")
    for s in symbol_list:
        if s in stocks_array:
            return s

sample_excluded.loc[:, "BusinessTicker"] = sample_excluded.BusinessSymbol.apply(
    lambda symbol: customer(symbol)
)

final = sample_excluded.drop(columns=["BusinessSymbol"])

final.to_csv("./Result/上市公司前五大供应商数据.csv", index=False)

前五大客户数据

raw_data = pd.read_excel(
    "./Data/前五大客户销售信息表2001-2023/SC_TopFiveSaleInfo.xlsx",
    dtype={"Symbol": str, "BusinessSymbol": str},
)

# 选择客户为上市公司的样本，客户为上市公司才能获取审计信息
data_cleaned_by_customer = raw_data.query("IsListed == 'Y'")

# 考虑合并报表
data_cleaned_by_stateTypeCode = data_cleaned_by_customer.query(
    "StateTypeCode == 1"
).reset_index(drop=True)

data_cleaned_by_stateTypeCode = data_cleaned_by_stateTypeCode.loc[
    :, ["Symbol", "EndDate", "BusinessSymbol", "ProportionOfTotalValue"]
]

# 合并 data_cleaned_by_stateTypeCode 与 stock_info 获取行业和上市情况信息
sample_merged_with_info = data_cleaned_by_stateTypeCode.merge(
    stock_info, how="left", on=["Symbol", "EndDate"], validate="m:1"
)

# 剔除 ST *ST
sample_excluded_ST = sample_merged_with_info.query(
    "LISTINGSTATE == '正常上市'"
).reset_index(drop=True)

sample_excluded = sample_excluded_ST.loc[
    sample_excluded_ST.BusinessSymbol.apply(lambda symbol: filter_customer(symbol)), :
].reset_index(drop=True)

sample_excluded.loc[:, "BusinessTicker"] = sample_excluded.BusinessSymbol.apply(
    lambda symbol: customer(symbol)
)

final = sample_excluded.drop(columns=["BusinessSymbol"])
final.to_csv("./Result/上市公司前五大客户数据.csv", index=False)

与上游供应商共同审计

audit_data = pd.read_excel(
    "./Data/审计意见表文件1990-2023/FIN_Audit.xlsx",
    dtype={"Stkcd": str, "DadtunitID": str},
)

audit_data.rename(
    columns={"Stkcd": "Symbol", "Dcost": "auditFee", "DadtunitID": "auditID"},
    inplace=True,
)

# 原数据中有半年报的数据，需要剔除
audit_data.loc[:, "month"] = (
    audit_data.Accper.str.split("-", expand=True).iloc[:, 1].astype("int64")
)
audit_data_dropna = (
    audit_data.query("month == 12").dropna(subset=["auditID"]).reset_index(drop=True)
)

# 看看是否有重复值
audit_data_dropna.loc[audit_data_dropna.loc[:, ["Symbol", "Accper"]].duplicated(), :]

	Symbol	Stknme	Accper	Annodt	Dadtunit	auditFee	auditID	month

audit = audit_data_dropna.loc[:, ["Symbol", "Accper", "auditID", "auditFee"]]

audit.rename(columns={"Accper": "EndDate"}, inplace=True)
audit.head()

	Symbol	EndDate	auditID	auditFee
0	000002	1992-12-31	107286	NaN
1	000002	1993-12-31	107286	NaN
2	000002	1994-12-31	107286	NaN
3	000002	1995-12-31	107286	NaN
4	000002	1996-12-31	107286	NaN

sample_raw = pd.read_csv(
    "./Result/上市公司前五大供应商数据.csv",
    dtype={
        "Symbol": str,
        "BusinessTicker": str,
    },
)

# 给上市公司样本添加审计师信息
sample_with_audit = sample_raw.merge(
    audit, how="left", on=["Symbol", "EndDate"], validate="m:1"
)

# 给供应商添加审计师信息
data_with_audit = sample_with_audit.merge(
    audit,
    how="left",
    left_on=["BusinessTicker", "EndDate"],
    right_on=["Symbol", "EndDate"],
    suffixes=("_sample", "_supplier"),
)

# 剔除审计师事务所信息缺失的样本
data_excluded_na = data_with_audit.dropna(
    how="any", subset=["auditID_sample", "auditID_supplier"], axis=0
).reset_index(drop=True)

data_excluded_na.loc[:, "audit_common_temp"] = (
    data_excluded_na.auditID_sample == data_excluded_na.auditID_supplier
)

sample = data_excluded_na.groupby(["Symbol_sample", "EndDate", "IndustryCode"]).agg(
    {
        "audit_common_temp": "sum",
    }
)

sample.reset_index(inplace=True)
sample.loc[:, "共同审计"] = [0 if i == 0 else 1 for i in sample.audit_common_temp]
sample.drop(columns=["audit_common_temp"], inplace=True)

sample.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1188 entries, 0 to 1187
Data columns (total 4 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   Symbol_sample  1188 non-null   object
 1   EndDate        1188 non-null   object
 2   IndustryCode   1188 non-null   object
 3   共同审计           1188 non-null   int64 
dtypes: int64(1), object(3)
memory usage: 37.2+ KB

sum(sample.loc[:, "共同审计"])

sample.to_csv("./Result/与上游供应商共同审计.csv", index=False)

与下游客户共同审计

sample_raw = pd.read_csv(
    "./Result/上市公司前五大客户数据.csv",
    dtype={
        "Symbol": str,
        "BusinessTicker": str,
    },
)

# 给上市公司样本添加审计师信息
sample_with_audit = sample_raw.merge(
    audit, how="left", on=["Symbol", "EndDate"], validate="m:1"
)

# 给客户添加审计师信息
data_with_audit = sample_with_audit.merge(
    audit,
    how="left",
    left_on=["BusinessTicker", "EndDate"],
    right_on=["Symbol", "EndDate"],
    suffixes=("_sample", "_customer"),
)

# 剔除审计师事务所信息缺失的样本
data_excluded_na = data_with_audit.dropna(
    how="any", subset=["auditID_sample", "auditID_customer"], axis=0
).reset_index(drop=True)

data_excluded_na.loc[:, "audit_common_temp"] = (
    data_excluded_na.auditID_sample == data_excluded_na.auditID_customer
)

sample = data_excluded_na.groupby(["Symbol_sample", "EndDate", "IndustryCode"]).agg(
    {
        "audit_common_temp": "sum",
    }
)

sample.reset_index(inplace=True)
sample.loc[:, "共同审计"] = [0 if i == 0 else 1 for i in sample.audit_common_temp]
sample.drop(columns=["audit_common_temp"], inplace=True)

sample.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1576 entries, 0 to 1575
Data columns (total 4 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   Symbol_sample  1576 non-null   object
 1   EndDate        1576 non-null   object
 2   IndustryCode   1576 non-null   object
 3   共同审计           1576 non-null   int64 
dtypes: int64(1), object(3)
memory usage: 49.4+ KB

sum(sample.loc[:, "共同审计"])

sample.to_csv("./Result/与下游客户共同审计.csv", index=False)

混合样本

sales_sample = pd.read_csv(
    "./Result/与下游客户共同审计.csv", dtype={"Symbol_sample": str}
)
supplier_sample = pd.read_csv(
    "./Result/与上游供应商共同审计.csv", dtype={"Symbol_sample": str}
)

# 将与上游供应商共同审计的样本和下游客户共同审计的样本合并在一起，涉及到一个问题：有的样本既与上游供应商共同审计，又与下游客户共同审计。
sample = sales_sample.merge(
    supplier_sample,
    how="outer",
    on=["Symbol_sample", "EndDate", "IndustryCode"],
    suffixes=["_customer", "_supplier"],
)

sample.fillna(0, inplace=True)

sample

	Symbol_sample	EndDate	IndustryCode	共同审计_customer	共同审计_supplier
0	000004	2014-12-31	C27	0.0	0.0
1	000006	2014-12-31	K70	0.0	0.0
2	000006	2015-12-31	K70	1.0	0.0
3	000007	2017-12-31	K70	0.0	0.0
4	000007	2018-12-31	K70	0.0	0.0
...	...	...	...	...	...
2552	873223	2023-12-31	C34	0.0	0.0
2553	873305	2022-12-31	C33	0.0	0.0
2554	873305	2023-12-31	C33	0.0	0.0
2555	873593	2023-12-31	C38	0.0	0.0
2556	873726	2023-12-31	C35	0.0	0.0

2557 rows × 5 columns

sample.loc[:, "auditCom_temp"] = (
    sample.loc[:, "共同审计_customer"] + sample.loc[:, "共同审计_supplier"]
)
sample = sample.loc[:, ["Symbol_sample", "EndDate", "IndustryCode", "auditCom_temp"]]

sample.loc[:, "auditCom"] = [0 if i == 0 else 1 for i in sample.auditCom_temp]
sample.query("auditCom == 1")

	Symbol_sample	EndDate	IndustryCode	auditCom_temp	auditCom
2	000006	2015-12-31	K70	1.0	1
5	000009	2012-12-31	S90	1.0	1
6	000009	2013-12-31	S90	2.0	1
7	000009	2014-12-31	S90	1.0	1
12	000011	2019-12-31	K70	1.0	1
...	...	...	...	...	...
2409	688737	2021-12-31	C26	1.0	1
2441	831961	2022-12-31	C40	1.0	1
2467	833580	2023-12-31	C30	1.0	1
2493	835892	2023-12-31	C35	1.0	1
2495	835985	2023-12-31	C38	1.0	1

243 rows × 5 columns

final = sample.drop(columns=["auditCom_temp"])
final.to_csv("./Result/混合样本.csv", index=False)