The application of text mining in accounting

By facilitating the derivation of knowledge and qualitative measures from textual data, text mining techniques have come into vogue in various domains and industries. Namely in accounting, text mining outputs can elucidate, complement, and validate the customary quantitative data. This study creates an up-to-date view of text mining applications in accounting practice. Through a critical review of text mining literature, insight is given into the stages of a typical text mining process, contemporary text mining techniques that have been named valuable in an accounting context, and the information that can be obtained by applying these techniques.     

Cross-sector comovements and policy impact in the COVID-19 stock market: A dynamic factor approach

This study investigates the remarkable comovements in U.S. equity returns during the COVID-19 pandemic. It constructs a dynamic factor model (DFM) to illuminate the sources of the comovements and their implications. Using the Markov Chain Monte Carlo (MCMC) estimation method, the study finds that the comovements had a weak daily oscillation pattern during the pandemic. With that pattern, the study also finds significant monetary policy effects on the equity returns of several key sectors. In addition, it estimates the impact of news shocks, including monetary policy news, fiscal stimulus news, and unemployment news, on cross-sector equity returns. For any given sector, the conventional and unconventional monetary policy news shocked the sector in opposite directions. Among the positive monetary news shocks, the strongest were from interest rate policy surprises. Conversely, fiscal stimulus news had the most substantial positive impact and triggered all sectors to rebound from the bear market at the end of March 2020. Furthermore, by applying Natural Language Processing (NLP) sentiment analysis, this study sheds light on the positive correlation between comovements and news sentiment.     

Feasibility analysis of machine learning for performance-related attributional statements

We investigate the feasibility of machine learning methods for attributional content and framing analysis in corporate reporting. We test the performance of five widely-used supervised machine learning classifiers (naïve Bayes, logistic regression, support vector machines, random forests, decision trees) in a top-down three-level hierarchical setting to (1) identify performance-related statements; (2) detect attributions in these; and (3) classify the content of the attributional statements. The training set comprises manually coded statements from a corpus of management commentary reports of listed companies. The attributions include both intra- and inter-sentential attributional statements. The results show that for both intra- and inter-sentential attributions, F1-scores of our most accurate classifier (i.e., support vector machines) vary in the range of 76% up to 94%, depending on the identification, detection and classification levels and the content characteristics of attributions. Additionally, we assess the hierarchical performance of classifiers, providing insights into a more holistic classification process for attributional statements. Overall, our results show how machine learning methods may facilitate narrative disclosure analysis by providing a more efficient way to detect and classify performance-related attributional statements. Our findings contribute to the accounting and management literature by providing a basis for implementing machine learning methodologies for research investigating attributional behavior and related impression management.     

Digitalization,internationalization, and firm performance: A resource-orchestration perspective on new OLI advantages

Digital forces and digital global connection weaken traditional ownership, location and internalization (OLI) advantages and intensify new OLI advantages (open resources, linkages and integration). However, by building on the resource-orchestration theory, we raise the question of how digitalization (utilization and orchestration of digital resources) and internationalization (firm-level outward internationalization and country-level inward internationalization) affect firm performance. We introduce the degree of outward internationalization and home-country inward foreign direct investment (FDI) inflows as moderators in achieving firm performance as a result of digitalization. Using a panel dataset of 571 U.S. manufacturing firms, we find a curvilinear relationship between digitalization and performance. The top quartile of digitalization efforts is rewarded by significant profitability. Moreover, high levels of outward internationalization and high net-FDI inflows increase the performance gains attributable to high levels of digitalization. Overall, the resource-orchestration theory complements new OLI advantages in explaining firm performance in the digital world.