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We examine firm value and performance implications of the growing trend of nontechnology companies engaging in activities relating to digital technologies. We measure digital activities in firms based on the disclosure of digital words in the business description section of 10-Ks. Digital activities are associated with a market-to-book ratio 8%–26% higher than industry peers, and only 25% of the differences in market-to-book is explained by accounting capitalization restrictions. To control for selection bias, we implement lagged dependent variable and IV regressions, and our market-to-book findings are robust to these specifications. Portfolios formed on digital activity disclosure earn a Daniel et al. The Journal of Finance 52 (3): 1035–1058 ( 1997 )-adjusted return of 30% over a three-year horizon and a monthly alpha of 44-basis-points. On the other hand, we find weak evidence of near-term, positive improvements in fundamental performance, as we find some evidence of interim productivity increases but declines in sales growth conditional on digital activities.

This paper examines the effect of the mandatory adoption of International Financial Reporting Standards (IFRS) by the European Union on financial analysts' information environment. To control for the effect of confounding concurrent events, we use a control sample of firms that had already voluntarily adopted IFRS at least two years prior to the mandatory adoption date. We find that analysts' absolute forecast errors and forecast dispersion decrease relative to this control sample only for those mandatory IFRS adopters domiciled in countries with both strong enforcement regimes and domestic accounting standards that differ significantly from IFRS. Furthermore, for mandatory adopters domiciled in countries with both weak enforcement regimes and domestic accounting standards that differ significantly from IFRS, we find that forecast errors and dispersion decrease more for firms with stronger incentives for transparent financial reporting. These results highlight the important roles of enforcement regimes and firm-level reporting incentives in determining the impact of mandatory IFRS adoption.

Electricity is currently one of the most popular sources of energy. Considering such widespread use of electric energy, we may ask, what is the economic cost of producing and supplying it? The climate crisis and the social pressure associated with it have triggered the necessity to make further investments in renewable and low-emission energy sources, while the COVID-19 pandemic has abruptly limited electricity consumption in industry. All these factors can have an impact on disruptions or loss in the liquidity of companies responsible for supplying electricity to end users. Guaranteeing cash flow for energy sector entities is a prerequisite for energy supply continuity. In this context, the selection and application of reliable sources of information are vital for the management of the financial liquidity of energy sector entities. The aim of this article is to prove the value of the financial information of individual (IFR) and consolidated financial statements (CFR) essential for the indicative liquidity assessment of Polish energy groups in 2018–2021. The hypothesis of this study is that individual and consolidated statements do not offer coincident analytical data due to the diversified role of their parent undertakings. We have applied indicative liquidity assessment analysis from a static and dynamic perspective to 2018–2021, on the basis of individual and consolidated financial statements. The results clearly show high dysfunction in the application of indicative liquidity assessment in the case of the individual financial statement of the parent company. This is mainly due to the role parent companies play in Polish energy sector groups, as they are mainly responsible for support processes.

This paper examines the information content of the forward-looking statements (FLS) in the Management Discussion and Analysis section (MD&A) of 10-K and 10-Q filings using a Naïve Bayesian machine learning algorithm.

Kim, J.; Kim, T.; Chang, J.-I.; Lim, B., and Kim, G., 2023. Evaluating the change in performance of ocean and fishery businesses during the COVID-19 pandemic. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 363-367. Charlotte (North Carolina), ISSN 0749-0208. Due to recent social and economic changes, the business performance of the ocean and fisheries industry has deteriorated, and the performance gap between businesses has expanded. The proportion of mid-sized ocean and fishery businesses has been falling annually, whereas that of small-sized businesses has increased, revealing bias in the industrial structure. The government has generated evidence-based data to support policymaking through statistical surveys. However, these data possess limitations in analyzing the structure of the industry and the status of related businesses. This study collected data on the financial statements of businesses from 2017 to 2020, divided them into pre-COVID-19 (2017–2019) and COVID-19 (2020), and conducted a business performance analysis and performance gap analysis by industry and business size. The business performance analysis was conducted using financial statement analysis indicators of stability, profitability, productivity, and activity, and the performance gap with an income polarization index of the Gini coefficient and quintile share ratio. Compared to the previous three years (2017–2019), ocean and fishery businesses maintained external growth in 2020, with five indicators showing a positive Compound Annual Growth Rate (CAGR), including the number of employees, revenue, operating profit, capital, and assets. In addition, indicators of stability, productivity, and activity improved, whereas profitability indicators deteriorated in 2020. The 2020 CAGR of the Gini coefficient and quintile share ratio increased by 0.04% and decreased by 11.4%, respectively, representing a wider performance gap. This study found that, on average, the larger the business size, the better the productivity and activity, and the smaller the business size, the higher the stability and profitability. Therefore, specific policies are needed for the small but highly profitable businesses to increase productivity and for large companies with high productivity and activity to improve profitability.

Kim, T.; Kim, J.; Kim, G.; Chang, J.-I.; Lim, B., and Kim, J. 2023. Analysis of employment elasticity in the ocean and fisheries industry. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 408-412. Charlotte (North Carolina), ISSN 0749-0208. This study investigates the employment elasticity between the economic growth rate and employment changes in the ocean and fisheries industry and analyzes the determinants of such employment elasticity. The panel data is drawn from industry surveys conducted in 2017–2020. The panel data regression on companies with sales of more than 8 billion KRW shows that their total sales are inversely proportional to employment elasticity, implying that large-scale companies may not necessarily offer high employment. The panel data regression on companies with sales of less than 8 billion KRW shows that their debt ratio is inversely proportional to employment elasticity, meaning that employment in small-scale companies is vulnerable to the companies' financial status. Panel data regression by industrial classification is also conducted. The debt ratio is inversely proportional to employment elasticity in some sub-industries, such as the building and repair of ships and offshore plants and marine and fisheries equipment manufacturing. However, the total sales and employment rate during COVID-19 are proportional to employment elasticity in the sub-industries of both marine leisure and tourism and marine and fisheries services. This implies that the companies in these sub-industries have attempted to increase sales by expanding or maintaining employment during the COVID-19 pandemic. Future analyses with more detailed industrial classifications are necessary to establish useful bases for policies supporting sustainable employment in the ocean and fisheries industry.

This study proposes an innovative financial statement analysis model combining deep neural networks with an adaptive weighting algorithm. The model includes five hidden layers with neuron counts of 128, 256, 128, 64, and 32, and applies an adaptive weighting mechanism that dynamically adjusts feature importance using the coefficient of variation and Pearson correlation. The dataset consists of 8,500 financial records from companies across eight industries, spanning from 2005 to 2023, and includes over 20 key indicators from the balance sheet, income statement, and cash flow statement. The model was evaluated against traditional approaches, including support vector machines (SVM), random forest, and Transformer-based models. Results demonstrate that the proposed model achieves 90% accuracy in financial risk prediction, outperforming SVM by 12%, with an F1 score of 87% and RMSE reduced to 0.06. This highlights the model’s effectiveness and robustness in handling complex financial data. In financial risk prediction (a classification task), the model achieved an average accuracy of 88%, recall of 85%, and F1 score of 86.5% across 50 experimental runs. For profitability analysis (a regression task), the model reduced RMSE to 0.045. These results outperform traditional baselines such as logistic regression and SVM, and approach the performance of emerging Transformer-based models, demonstrating both predictive effectiveness and generalizability across industries.

DuPont analysis, a common form of financial statement analysis, decomposes return on net operating assets into two multiplicative components: profit margin and asset turnover. These two accounting ratios measure different constructs and, accordingly, have different properties. Prior research has found that a change in asset turnover is positively related to future changes in earnings. This paper comprehensively explores the DuPont components and contributes to the literature along three dimensions. First, the paper contributes to the financial statement analysis literature and finds that the information in this accounting signal is in fact incremental to accounting signals studied in prior research in predicting future earnings. Second, it contributes to the literature on the stock market's use of accounting information by examining immediate and future equity return responses to these components by investors. Finally, it adds to the literature on analysts' processing of accounting information by again testing immediate and delayed response of analysts through contemporaneous forecast revisions as well as future forecast errors. Consistent across both groups of market participants, the results show that the information is useful as evidenced by associations between the DuPont components and stock returns as well as analyst forecast revisions. However, I find predictable future forecast errors and future abnormal returns indicating that the information processing does not appear to be complete. Taken together, the analysis indicates that the DuPont components represent an incremental and viable form of information about the operating characteristics of a firm.

Fundamental analysis is an approach for evaluating a firm for its investment-worthiness whereby the firm's financial statements are subject to detailed investigation to predict future stock price performance. In this paper, we propose an approach to combine financial statement data using Data Envelopment Analysis to determine a relative financial strength (RFS) indicator. Such an indicator captures a firm's fundamental strength or competitiveness in comparison to all other firms in the industry/market segment. By analysing the correlation of the RFS indicator with the historical stock price returns within the industry, a well-informed assessment can be made about considering the firm in an equity portfolio. We test the proposed indicator with firms from the technology sector, using various US industries and report correlation analyses. Our preliminary computations using RFS indicator-based stock selection within mean-variance portfolio optimization demonstrate the validity of the proposed approach.

In recent years, large language models (LLMs) have shown impressive performance across various natural language processing tasks and are increasingly adopted in high-stakes fields such as financial analysis. However, their effectiveness in Chinese financial contexts is hindered by the scarcity of high-quality, domain-specific datasets. To bridge this gap, we present the Chinese Financial Question Answering (CFQA) dataset, a novel resource designed to advance research in financial analysis. CFQA is constructed from publicly available annual reports of multiple Chinese listed companies, paired with corresponding questions and human-annotated answers. Evaluation results reveal that existing QA methods perform poorly on this dataset. CFQA introduces several unique challenges: (1) source documents are in PDF format with complex tabular structures, making information extraction difficult; (2) the length and intricacy of financial reports complicate answer retrieval; and (3) the questions are tightly focused on domain-specific financial content.