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2021-2022学年“龙马之星”博士生论坛(第四期)

作者: | 发布日期:2021-12-13| 浏览次数:

时间:20211214日(周二)   地点:腾讯会议

 

报告1

时间:8:30-9:30

报告:Big 4 Audit Expertise: Attracting and Developing Human Capital

报告人:王洁璇(新加坡国立大学)

摘要:How do Big 4 firms attract and develop audit expertise? We use de-identified employment and compensation data to compare attrition and cohort-pay profiles in Big 4 and non-Big 4 firms. Big 4 auditors have substantially lower turnover, across the board, than similar-sized peers. Starting pay, cohort raises, and cohort pay dispersion also suffers in non Big 4 relative to Big 4 firms. Auditor fees are consistently related to compensation structure but not auditor size. Big 4 auditors focus on extended personnel evaluation rather than quickly sorting through each cohort, which gives them the most experienced audit teams.

报告人简介:王洁璇,新加坡国立大学会计学博士研究生(2017至今), SAP认证顾问师。2017年本科毕业于新加坡国立大学工程学院,获卓越荣誉工程学位。曾通过全额奖学金项目保送至新加坡国立大学就读本科。研究方向主要为审计与劳动经济学的结合。

 

报告2

时间:9:30-10:30

报告:FinBERT—A Deep Learning Approach to Extracting Textual Information

报告人:王慧(香港科技大学)

摘要:We develop FinBERT, a deep learning algorithm that incorporates the contextual relations between words in the finance domain. First, FinBERT significantly outperforms the Loughran and McDonald (LM) dictionary and other algorithms in sentiment classification, primarily because of its ability to uncover sentiment in sentences that other algorithms mislabel as neutral. Next, other approaches underestimate the textual informativeness of earnings conference calls by at least 32%. Last, textual sentiments summarized by FinBERT can better predict future earnings than the LM dictionary, especially after 2011, consistent with firms’ strategic disclosures reducing the information content of textual sentiments measured with LM dictionary.

报告人简介:王慧,香港科技大学会计学博士研究生(2017-至今)。研究生毕业于北京大学,获会计学硕士学位,本科毕业于南京大学,获会计学学士学位。研究方向为信息披露、文本分析、贷款合约。

 

报告3

时间:10:30-11:30

报告:Enemy at the Gates: IPOs and Peer Firms’ Voluntary Disclosure

报告人:凌晓旭(香港理工大学)

摘要:We examine the impact of completed initial public offerings (IPOs) on industry peers’ voluntary disclosure. Prior research suggests that IPO issuers obtain improved financing capability and risk tolerance, and are thus able to engage in more aggressive product market competition. The improved competitive position expands the set of actions that IPO firms can potentially take, allowing them to exploit peer firms’ information that they are not able to take advantage of before the IPOs. We predict that peer firms will reduce their information disclosure in response to the greater competitive threat of the IPO firm. Employing a difference-in-differences specification that uses withdrawn IPOs as benchmarks and controls for firm-level time-varying characteristics as well as event-firm and year fixed effects, we find a significant decrease in the likelihood and frequency of public incumbents’ management guidance around IPO completion in their industries. The results are robust to using NASDAQ fluctuation as an instrument for IPO completion. The decrease in peer voluntary disclosure is more pronounced when IPOs are large or successful, when the peers are financially constrained, and when strategic actions of peers and issuers are likely to be substitutes. Additional analyses find a decrease in the flow of industry-level information from public incumbents after IPO completion in their industries. Consequently, peer stock prices become a less useful signal for investment opportunities in public incumbents’ investment decisions post-IPO-completion. Overall, we provide new evidence on the disclosure response of public incumbents to completed IPOs and the resulting changes in the information environment.

报告人简介:凌晓旭,香港理工大学博士后研究员(20218月至今)。博士毕业于香港理工大学会计与金融学院(2017-2021)。研究方向为盈余公告、高管盈利预测等。