Intelligent Systems in Accounting, Finance and Management最新文献

Increasing computing power and access to the internet have amplified the role of social media and online news media on financial market outcomes. However, these two sources of information are intertwined in such a way that information flows between them. As a result, sentiment expressed in one source can affect stock market outcomes through the other source. This study examines this interplay between news media sentiment, social media sentiment and stock returns within the Dow Jones constituent companies from 2016 to 2023. Leveraging an extensive dataset, we adopt an approach that combines causal mediation models with robust statistical techniques to establish the mediation effects of one sentiment proxy on the relationship between the other proxy and stock returns. We also use a range of other methods like path analysis, panel vector autoregression and causal forests for robustness. The study finds that news sentiment is more influential in directly affecting stock returns than Twitter sentiment while the latter is more influential indirectly when mediated by news sentiment.

This paper examines the prediction of IPO withdrawal using machine learning methods (lasso and random forest) and conventional regression (logit). The dataset comprises 2444 US first-time IPOs from 1997 to 2014. Results show that random forest outperforms both logit and lasso in in-sample and cross-sectional out-of-sample predictions when the training and test sets are drawn from the same time period. However, when models are trained on past data and tested on future observations, all models fail to accurately predict IPO withdrawal. This failure is attributed to concept drift—a change in the relationship between predictors and IPO withdrawal over time. I show that concept drift occurs at multiple points in time, affects various predictors, and persists even when accounting for economic shocks, institutional changes, or different prediction horizons. These findings suggest that the generalizability of previous results on IPO withdrawal is limited, as the relationship between various predictors and IPO withdrawal seems to vary across time periods.

This study explores the wealth of information inherent in online employee reviews as an emerging resource in academic research. The focus is on the fields of finance, accounting, economics, and management, with an emphasis on how employee reviews contribute to our understanding of these areas. A systematic literature review (SLR) of 70 high-quality articles highlights the insights gleaned from employee reviews. Their data points, such as employee satisfaction, employee outlook, evaluation of culture, management, and colleagues, and text comments are mainly used in (1) explaining and predicting firm performance, (2) predicting and understanding performance and satisfaction of specific job groups, and (3) CSR- and ESG-related research. This SLR is important because the three main topics mentioned in which employee reviews are mainly used are spread across the fields of finance, accounting, economics, and management. This SLR therefore provides researchers with an important and necessary overview of the research already addressed across these fields. Furthermore, the SLR provides an overview of employer rating platforms utilized for academic research and methods used to harness employee reviews for research purposes. Here, a significant finding of this SLR is the predominant use of Glassdoor as a data source and the focus on US markets. The SLR concludes by proposing five potential avenues for future research, paving the way for a deeper understanding of the interplay between employee reviews (information) and organizational dynamics.

In recent years, research studies have been conducted on analyzing journal-entry data using advanced visualization techniques and machine learning models. However, because of their highly confidential nature, these data are not disclosed externally, which can limit research and business opportunities to analyze the rich organizational information they contain. To address these problems, this study utilized a variational autoencoder to generate synthetic journal-entry data with statistical properties similar to those of actual data. The synthetic journal-entry data we created adhered to the fundamental structure of double-entry bookkeeping and were quantitatively evaluated for quality.

This paper explores the application of causal discovery algorithms to factor investing, addressing recent criticisms of correlation-based models. We create novel causal network representations of the S&P 500 universe and apply them to three investment scenarios. Our findings suggest that causal approaches can complement traditional methods in areas such as stock peer group identification, factor construction, and market timing. While causal networks offer new insights and sometimes outperform correlation-based methods in terms of risk-adjusted returns, they do not consistently surpass traditional approaches. The causal method though shows promise in identifying unique market relationships and potential hedging opportunities. However, its practical implementation presents challenges due to computational complexity and interpretation difficulties. Our study demonstrates the potential value of causal discovery in factor investing, while also identifying areas for further research and refinement.

The evaluation of ESG ratings by ESG rating agencies is time-consuming and requires the participation of numerous human specialists. In this paper, we propose a method for creating proxies of ESG scores by collecting corporate ESG news and publicly available ESG-related data using data crawling techniques and deep learning-based classification technology while minimizing human involvement. To validate the effectiveness of the proposed approach, we suggest three hypotheses. Two of them are related to the connection between open-source information and ESG ratings, while one concerns the link between proxy ESG rating and firm performance. To validate the effectiveness of the proposed approach, we conduct an empirical analysis based on 976 unique companies listed by the Korean Corporate Governance Agency (KCGS) from 2016 to 2019. Initially, we gather ESG indicators from open sources including disclosures and firms' news articles from a news portal site. We utilize Bidirectional Encoder Representations from Transformers (BERT) to classify news articles into environment, social, and governance categories and determine their sentiments. We confirm that ESG news sentiment and variables extracted from open-source data are related to ESG ratings. Furthermore, we find a significantly positive relationship between E, S, and G ratings predicted based on open-source data and Tobin's Q.