Knowledge that Transforms

This study aims to understand international rotations (“secondments”) as a brokerage mechanism within globally networked public accounting firms (GNFs). Through interviews with 29 secondees on tour to or from the U.S. (individuals on rotation from one member firm to another member firm in a specified role for a fixed period) and 11 firm leaders, we find that secondments are viewed as a critical brokerage function in performing global client service and global risk management. Specifically, secondees bridge gaps in the GNF structure by brokering network ties, technical knowledge, sociocultural knowledge, and language. Secondees embody different brokerage roles and can fulfill more than one brokerage role at a time, depending on the purpose of the secondment and the stage of deployment. Collectively, secondments are important mechanisms by which structural holes are reduced between participating member firms and proximal member firms, ultimately benefiting the global firm and the global clients they serve.

We examine the economic implications of digital traffic on firms’ financial performance, stock valuation, and financial surprises. Our analysis shows that timely flows of digital traffic are contemporaneous and leading indicators of firms’ revenue and profitability—both gross and operating. Moreover, we show that digital traffic contains novel information about firms’ future performance that is not entirely reflected in stock prices, analyst forecasts, or historical (i.e., time series) financial metrics. Notably, digital-traffic-based investment strategies are lucrative and generate substantial abnormal returns. Importantly, we also adduce evidence that corroborates our conjecture about the underlying economic mechanism that explains the valuation implications of digital traffic: These are driven by firms with consumer-oriented websites that facilitate sale transactions. Data Availability: Data are available from the sources cited in the text.

We study whether sell-side financial analysts’ social ties to firm management help them discern firms’ financial reporting frauds and, upon such detection, how the connected analysts disseminate the information. Using unique data from China, we find that, although unconnected analysts do not manifest a significant change in their likelihood of covering the fraud firms nor in issuing more downgrade stock ratings, connected analysts are significantly more likely to drop coverage right after these firms’ first annual reports containing fraudulent information. Meanwhile, mutual funds with a trading commission relationship to these connected analysts (i.e., client funds) are significantly more likely to unload their holdings of fraud firms than nonclient funds after these firms’ first fraudulent annual reports. Overall, the evidence suggests that analysts with social ties to firm management have early access to bad news and choose to privately communicate the negative information to their clients. Data Availability: All data used in this article are publicly available.

Companies, including public accounting firms, are integrating systems with advanced algorithms into decision-making processes to assist with developing and evaluating complex estimates. However, individuals may hesitate to rely on algorithmic output, particularly under conditions of uncertainty. We conduct two experiments examining whether and how a system’s ability to adapt—an emerging feature of machine learning—interacts with uncertainty to influence accounting professionals’ reliance on algorithmic advice. In Experiment 1, we find that auditors are more willing to rely on advice from learning algorithms than static algorithms when estimation uncertainty is relatively high. Experiment 2 replicates this result in a general accounting context where preparers develop their own estimates. Our findings demonstrate that accounting professionals’ reliance on algorithms is contextually dependent, and highlights algorithm adaptability as an important technological feature that can promote advice utilization, particularly when adaptability is likely important to the judgment context (e.g., when estimation uncertainty is high).

We investigate the link between the expanded audit report and firms’ financial disclosure decisions, focusing on auditors’ mentions of goodwill impairment as a key audit matter (KAM). Drawing from a sample of the United Kingdom Premium Listed companies with goodwill on their balance sheets during 2014–2019, we identify instances where goodwill impairment is flagged as a KAM and contrast firms’ disclosure levels on goodwill impairment using textual measures constructed from information in their annual reports. We find that firm disclosure on goodwill impairment increases (decreases) when auditors start (stop) mentioning goodwill impairment as a KAM. The increase in disclosure is more pronounced in the presence of stronger external information demand and better internal governance. Finally, firms are more likely to impair goodwill in the period following auditors’ mention of goodwill impairment as a KAM. Overall, this paper establishes the role of the expanded audit report for firm disclosure.

This study uses machine learning models to forecast future material misstatements. Using raw financial data, audit variables, qualitative features, and an efficient algorithm, we design a dynamic model that continuously updates with new information. Our model outperforms the benchmarks for both one-year-ahead and two-year-ahead predictions in terms of out-of-sample predictive power and economic impact on net income. Using Explainable Artificial Intelligence, we identify key predictive features, including comprehensive income, foreign firm status, and accrued interest and penalties from unrecognized tax benefits. Results show that investors achieve better outcomes using a proactive investment strategy based on our prediction models than reactive detection models. Furthermore, our prediction model can help managers prevent internal control weaknesses, assist auditors in assessing misstatement risks in advance, and enable regulators to allocate inspection resources proactively. Our study advances the literature by moving beyond the detection of past material misstatements to the forecasting of future misstatements. Data Availability: Publicly available.

Financial regulators considering the desirability of a new rule or regulation sometimes use pilot studies for evidence-based decision making. Although pilot studies can generate new knowledge, they also can be expensive and subject to serious selection biases, spillover problems, and the infeasibility of a blind design. Alternatively, regulators can often evaluate a proposed regulation’s impact by analyzing archival data or applying theory based on well-accepted economic principles. We discuss why pilot studies can be useful, but also why regulators and industry participants sometimes favor pilot studies with little scientific value. We illustrate these issues by discussing various SEC pilot studies.

Firms under common ownership have incentives to soften competition. I exploit unique data from Norway to document the economy-wide extent of common ownership, covering private and public firms and the universe of shareholders. Using exogenous variation in common ownership at the firm-household level due to marriages among individual shareholders, I show that firms experiencing an increase in common ownership due to a marriage increase profit margins by 7 to 16 percentage points, compared to firms affected by similar marriages that do not experience a change in common ownership.

Governance practices differ significantly across blockholder types. Compared with financial blockholders, nonfinancial blockholders are six times more likely to identify as activists. A textual analysis of regulatory filings shows that nonfinancial blocks govern through customized governance actions, while financial blocks follow generic performance metrics. Furthermore, blockholdings drive an important limitation in using Russell index thresholds as an identification strategy. Manipulation of index weights by Russell is strongly correlated with nonfinancial block ownership, confounding previous research on passive ownership. Using both reduced-form and structural estimates, we find that the market expects greater value creation from the entry of a nonfinancial blockholder.

This paper studies the role of scale inflexibility in explaining corporate credit spreads. We find robust evidence that firms with higher inflexibility have higher credit spreads. To mitigate the endogeneity concern, we employ a regression discontinuity design that uses the exogenous variations in labor adjustment costs resulting from close-call union elections. Furthermore, contraction inflexibility is more prominent in influencing credit spreads than expansion inflexibility is. Additionally, inflexibility increases credit spreads due to increased cash flow volatility and financial distress risk. Our findings highlight the importance of a firm’s ability to adapt to productivity shocks in fulfilling its debt obligations.