Pacific-Basin Finance Journal

Final sample data and code (STATA) for examine whether a firm’s trust and social capital, built up through corporate social responsibility (CSR) activities, pays off during the crisis period. US sample (Replication work of Lins, Servaes and Tamayo (2017)): August 2008 to March 2009 with 1390 firms. Chinese sample: June 2015 to March 2016 with 512 firms.

This data is from CSMAR and WIND

We are submitting the updated data and codes for replicating the analysis in the revised manuscript, "Does Average Skewness Matter? Evidence from the Taiwanese Stock Market".

Data structure explanation for Does cash-based operating profitability explain the accruals anomaly in China? February 25, 2020 We provide the final data used in our test and the SAS code to generate all the tables in our paper. The data for the U.S. study is titled “data_pbfj_us”, the data for the Chinese study is titled “data_pbfj_cn”. The SAS code used to generate the results is titled “pbfj_acc_code”. Data structure for the U.S. sample Variable names: 1) permno: stock identifier 2) date: date of observation 3) ret: monthly stock return 4) mep: market cap in previous month 5) lnmep: logarithm of mep 6) micro: microcap stock indicator 7) str: short-term reversal, defined as return in previous month. 8) mom: momentum effect (MOM) 9) at: total assets 10) lnbtm: logarithm of firm’s book-to-market equity 11) op_raw: operating profitability (OP) 12) accat: accruals (ACC) 13) opcat: cash-based operating profitability (Cash-OP) Data structure for the Chinese sample Variable names: 1) permno: stock identifier, same as “stkcd” in CSMAR database 2) date: date of observation 3) ret: monthly stock return 4) mep: market cap in previous month 5) lnmep: logarithm of mep 6) shell: shell stock indicator 7) str: short-term reversal, defined as return in previous month. 8) mom: momentum effect (MOM) 9) at: total assets, same as “A001000000” in CSMAR database 10) lnbtm: logarithm of firm’s book-to-market equity 11) op_raw: operating profitability (OP) 12) accat: accruals (ACC) 13) opcat: cash-based operating profitability (Cash-OP)

2 data files - US and China 2 code files - US and China All files use Stata statistical software

The empirical analysis explores annual panel data for 23 developed economies vis-à-vis 21 developing ones, over the period 2002-2017. For each country, we collect the US-denominated price levels of both conventional and Islamic equity market indices, as well as the levels of corruption. Stock market index series are retrieved from the MSCI Barra database, whereas country corruption risk ratings are sourced from the widely adopted International Country Risk Guide (ICRG), which is produced by the Political Risk Services (PRS) Group.

To address the issues in question, the empirical analysis explores annual panel data for 23 developed economies vis-à-vis 21 developing ones, over the period 2002-2017. The time span of the sample and country selection are constrained by data availability.

In this results file we include transmission and vulnerability indices estimated using DY12 rolling analysis. We also include 10 base and 900 base crisis classifications indices gauged from Kohonens self organizing cluster analysis, using Diebold and Yilmaz rolling estimates as input data.

This data consists raw equity prices collected from datastream

Data include (1) firm-level stock price crash risk and risk expectation data; (2) bank loans terms, including borrowing date, maturity, borrowing amount, borrowing type (i.e., credit, collateral, guarantee), and other contract-level loan information; (3) marketization index across Chinese provinces; (4) firms' ownership and political connection data; (5) types of bank; and, (6) other firm-level data for the period 2003–2014. The data include 996 contracts of 293 Chinese listed companies. Bank loan data and firm-level financial data are retrieved from the China Stock Market and Accounting Research (CSMAR) database and processed where necessary. The marketization index is taken from NERI INDEX of China's Provinces 2011 Report (Fan et al., 2011) and Marketization Index of China's provinces NERI Report 2016 (Wang et al., 2016). Ownership and political connection data are manually collected from annual reports of Chinese listed firms.