Cash flow versus accrual expectations management to meet or beat analyst cash flow and earnings forecasts

Prior literature shows firms manage analysts’ earnings expectations downward to avoid negative earnings surprises. Recent studies find an increasing number of analysts forecast both cash flow and earnings, providing two explicit targets managers seek to achieve. Nonetheless, the literature is unclear on whether firms manage analysts’ operating cash flow and/or accrual components of expected earnings to meet or beat analysts’ cash flow and earnings forecasts, and which firm characteristics motivate firms to engage in the expectations management strategies. This study decomposes earnings expectations management into its two mutually exclusive and collectively exhaustive parts: cash flow expectations management and accrual expectations management, and examines whether cross-sectional differences in the likelihood of firms engaging in downward cash flow or accrual expectations management depend on firm-specific characteristics. Overall, I find firms with lower cash flow growth and firms that miss prior-period cash flow forecasts engage in downward cash flow expectations management, and these firms engage in downward cash flow expectations management over and above downward accrual expectations management. I also find firms with better financial health, larger market shares, lower institutional ownership and less bloated balance sheets are likely to walk down both analysts’ cash flow and accrual forecasts

Initial Implementation of Data Analytics and Audit Process Management

To answer the call for more evidence on the adoption and effectiveness of Big Data Analytics in auditing, this study investigates auditors’ use of data analytic tools in audit-process management, including audit planning, testing, and conclusions. The analysis, which is performed as a qualitative study, is based on twenty-eight semi-structured interviews with Big 4 and non-Big 4 audit professionals in Thailand to gain insights into their experience implementing audit data analytic tools in the initial stage. Findings suggest that auditors primarily use data analytic tools in audit planning and substantive testing. Nevertheless, auditors do not perceive a need to use these tools to test internal controls and conclude audit opinions. In addition, we find that auditors tend to apply audit data analytic tools for anomaly detection and testing management assertions. Overall, auditors perceive the benefits of audit data analytic tools in improving their audit process management. Findings present practical implications for audit firms and audit professionals, including how to initially implement data analytic tools effectively in auditing and as guidelines for regulators on how to develop auditing standards that govern the use of Big Data and data analytic tools. We note some limitations in this study, such as the generalizability of the results, auditors’ personal biases, and the different tools and techniques used by each audit firm

Initial Implementation of Data Analytics and Audit Process Management

To answer the call for more evidence on the adoption and effectiveness of Big Data Analytics in auditing, this study investigates auditors’ use of data analytic tools in audit-process management, including audit planning, testing, and conclusions. The analysis, which is performed as a qualitative study, is based on twenty-eight semi-structured interviews with Big 4 and non-Big 4 audit professionals in Thailand to gain insights into their experience implementing audit data analytic tools in the initial stage. Findings suggest that auditors primarily use data analytic tools in audit planning and substantive testing. Nevertheless, auditors do not perceive a need to use these tools to test internal controls and conclude audit opinions. In addition, we find that auditors tend to apply audit data analytic tools for anomaly detection and testing management assertions. Overall, auditors perceive the benefits of audit data analytic tools in improving their audit process management. Findings present practical implications for audit firms and audit professionals, including how to initially implement data analytic tools effectively in auditing and as guidelines for regulators on how to develop auditing standards that govern the use of Big Data and data analytic tools. We note some limitations in this study, such as the generalizability of the results, auditors’ personal biases, and the different tools and techniques used by each audit firm