Ranik Raaen Wahlstrøm

Auditing originated as a voluntary response to agency conflicts but later became legally mandated in many economies. Whether market incentives alone are sufficient to sustain auditor independence therefore remains a fundamental question. We examine this issue using a 2011 Norwegian reform that exempted small private firms from mandatory audits. In a differencein-differences design, we find that auditors are less likely to issue going-concern opinions to financially distressed firms once auditing becomes voluntary. This decline is attenuated when firms have stronger agency-related demand for assurance. Consistent with reduced auditor conservatism, we observe fewer Type I errors but more Type II errors, while finding no evidence that distressed firms disproportionately terminate audits. Our findings show that voluntary demand for auditing does not necessarily sustain the same level of auditor independence as a legal mandate and that market demand for assurance can partially offset this effect.

Auditing originated as a voluntary response to agency conflicts but later became legally mandated in many economies. Whether market incentives alone are sufficient to sustain auditor independence therefore remains a fundamental question. We examine this issue using a 2011 Norwegian reform that exempted small private firms from mandatory audits. In a differencein-differences design, we find that auditors are less likely to issue going-concern opinions to financially distressed firms once auditing becomes voluntary. This decline is attenuated when firms have stronger agency-related demand for assurance. Consistent with reduced auditor conservatism, we observe fewer Type I errors but more Type II errors, while finding no evidence that distressed firms disproportionately terminate audits. Our findings show that voluntary demand for auditing does not necessarily sustain the same level of auditor independence as a legal mandate and that market demand for assurance can partially offset this effect.

1. We investigate how analyst questions during the Q&A sessions of earnings conference calls (ECCs) influence information asymmetry among investors. Specifically, we examine whether greater variati...

We examine the association between heterogeneity in analysts' questions during earnings conference calls (ECCs) and investor information asymmetry. Using a dataset of U.S. ECC transcripts from 2007 to 2020, we construct five measures of question heterogeneity capturing variation in the volume of questions, analysts' tone, thematic diversity, linguistic dissimilarity, and analyst turnover. Greater question heterogeneity is associated with higher unexplained trading volume, our proxy for investor information asymmetry. We attribute this to increased information processing costs, allowing investors with higher processing ability to transform public information into private advantages. Consistently, the association between analyst question heterogeneity and investor disagreement grows when processing burdens are higher, such as following negative earnings surprises or in firms with low institutional ownership. Conversely, it weakens when analysts revise forecasts positively in real time and when managers address a broader range of topics or engage in casting analyst participation. Our analyses control for firm-, analyst-, and call-level characteristics, including lagged unexplained trading volume, and are robust to a battery of alternative explanations. Our results highlight the complex role of analyst interactions during ECCs and suggest that more heterogeneous questions, while informative, can also exacerbate disparities in investor interpretation.

The literature on Venture Capital (VC) has produced a rich and diverse range of studies investigating both theoretical and empirical aspects of VC funding. We navigate this vast literature through a practitioner review, placing special emphasis on three topical areas: 1) Environment, Social, and Governance (ESG), Corporate Social Responsibility (CSR), and sustainability; 2) Innovation; and 3) Exit Strategies. Our aim is to provide an updated overview of the main contributions, identify the gaps and offer guidance for future investigations. We selected 1,264 papers for review, sourced from five major database libraries published between 2010 and 2022 through a NPL toolkit including the keywords related to VC financing. Although not encompassing the whole scientific production on VC, the sample provides a sufficiently com-prehensive overview of the main findings on VC financing in the three topic areas. Findings are summarized through tables and figures and show a substantial volume of papers focusing on VC, with an increase over time. A major highlight of this study is that most of the studies employ traditional methods and are mainly focused on the US and Chinese markets. Therefore, despite the extensive literature, there seems to be a research gap in the area of emerging markets. The review confirms that innovation is essential for VC performance and its funding plays a crucial role. Evidence is used in the paper to suggest how both researchers and practitioners can bridge these findings in future research to make more informed investment and commercialization decisions.

We investigate how narrative disclosures during earnings conference calls (ECCs) provide context for quantitative numeric disclosures in quarterly earnings releases, enhancing their informativeness. Drawing on a large sample of 34,918 quarterly ECCs from 1621 US‐listed firms from 2007 to 2020, we extract interpretable textual attributes—such as tone, specificity, novelty, and thematic content—from introductions, analyst questions, and management answers. Using stochastic gradient boosting, we model the nonlinear and interactive effects of these attributes with firm fundamentals and financial metrics. Our results reveal that narrative disclosures substantially improve the ability of firm fundamentals and financial metrics to explain market responses, both immediately and over longer horizons. We complement our analysis with Shapley additive explanations (SHAP) to identify which textual features most influence investor reactions and how their effects vary across contexts. Immediate market responses are primarily attributable to direct cues, such as the tone, while longer term responses increasingly reflect more complex content, such as strategic themes and support activities. These effects are especially pronounced when earnings surprises are either very negative or close to zero, and for firms with lower institutional ownership. They have also become more salient over time, suggesting that shifts in investor attention and analytical sophistication may be reshaping how financial narratives are processed. Our results contribute to understanding the contextual role of narrative disclosures in financial communication and illustrate the value of explainable AI in accounting research.

We utilize machine learning methods to model the credit risk of mortgages in a significant emerging market. For this purpose, we investigate a multitude of variables that explain the characteristics of the loans, the demographics of the borrowers, and macroeconomic factors. We employ SHapley Additive exPlanations (SHAP) values in conjunction with five different tree-based machine learning methods, as well as the least absolute shrinkage and selection operator (LASSO) in conjunction with logistic regressions. Our findings, which are robust across two sampling schemes, reveal that while demographic variables are significant and important, loan-specific and macroeconomic variables are the most crucial in explaining mortgage defaults. As existing literature on mortgage default has primarily focused on advanced markets, we aim to bridge this gap by concentrating on emerging market data. We also share our code, which we hope will encourage others to utilize the methods we have applied.