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The purpose of this study is to examine the relationship between explanatory language, audit fees and audit hours to demonstrate that auditors use explanatory language in audit reports to explain perceived audit risk.

The authors construct the sentiment value, a novel audit risk proxy derived from audit reports, using big data analysis. The relationship between sentiment value and explanatory language is then investigated. The authors present the validity of their new metric by examining the relationship between sentiment value and accounting quality, taking audit fees and hours into account.

The authors first find that reporting explanatory language is positively related to audit fees. More importantly, the authors provide an evidence that explanatory language in audit reports is indicative of increased audit risk as it is negatively correlated with sentiment value. As a positive (negative) sentimental value means that the audit risk is low (high), the results indicate that auditors describe explanatory language in a negative manner to convey the inherent audit risk and receive higher audit fees from the risky clients. Furthermore, the relationship is strengthened when the explanatory language is more severe, such as reporting the multiple numbers of explanatory language or going-concern opinion. Finally, the sentiment value is correlated with accounting quality, as measured by the absolute value of discretionary accruals.

Contrary to previous research, the authors’ findings suggest that auditors disclose audit risks of client firms by including explanatory language in audit reports. In addition, the authors demonstrate that their new metric effectively identifies the audit risk outlined qualitatively in audit report. To the best of the authors’ knowledge, this is the first study that establishes a connection between sentiment analysis and audit-related textual data.

The purpose of this paper is to examine the roles of earnings and book value (BV) in equity valuation.

The authors apply model’s explanatory power to analyze the roles of accounting data and test the hypotheses empirically with a sample of Chinese listed companies between 2004 and 2010.

The authors find that impact of accounting data on equity value is also dependent on profitability, but the behavior is non-monotonic. In the intermediate-profitability range, explanatory power of both earnings capitalization model and balance sheet model reach the peak, there are no significant differences between them. In the low-profitability range (small or negative profitability), explanatory power of balance sheet model is larger than earnings capitalization model. In the high-profitability range, explanatory power of balance sheet model is less than earnings capitalization model.

The results support that the role of BV is more stable in equity valuation. Moreover, this outcome provides reference for improving existing valuation model and setting accounting standard, and provides some empirical evidence for the practical application of BV in equity valuation.

Existing studies treat earnings as main variable of equity valuation, and BV is only added as a supplement. This paper compares roles of accounting earnings and BV in equity valuation, especially investigates the influence of BV in equity valuation, and fills up the deficiency in the related literature.

In community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.

In the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.

The GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.

The proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.

A novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.

This study aims to examine several hypotheses, in conjunction with fundamental accounting concepts, to explain variations in the explanatory power of earnings for returns.

The authors explore three factors for their impact on the explanatory power of earnings. First, the accounting period preceding the earnings report is characterized by distinct intratemporal subperiod behavior. Recognizing this intratemporal nonstationarity is hypothesized to increase the explanatory power of earnings. Second, disaggregation of earnings into operating components is hypothesized to increase the explanatory power of earnings. Moreover, joint consideration of these first two factors is investigated. Third, the authors hypothesize that recognizing fundamental accounting concepts such as timeliness, predictive value, objectivity and verifiability offer key insights into the explanatory power of earnings.

The authors explore a sample of firms with management forecasts, which yields natural intratemporal subperiods – preforecast, forecast and realization periods – to generate hypotheses rooted in fundamental accounting concepts. The empirical evidence shows that recognition of nonstationary intratemporal behavior and earnings disaggregation yields a significant increase in the explanatory power of earning for returns. These findings are linked to fundamental concepts of accounting information.

This study is unique as it examines the joint role of nonstationarity and disaggregation in assessing the information conveyed in earnings. Importantly, results on these factors are linked to fundamental accounting concepts of timeliness, predictive value, objectivity and verifiability, along with their inherent trade-offs.

This paper aims to examine whether information search frequency of accounting information is related to the explanatory power of accounting information for firm market value. It also examines whether information content and state of nature can have an impact on this relationship.

The paper is an empirical study using Web search volume data collected from Google Trends and financial and market data collected from Compustat.

This paper finds that investors use Web search engines as an alternative way to search for information they need, search frequency of accounting information is positively related to the explanatory power of accounting information for firm market value, the relationship is found differential between statements and categories within a statement depending on the information content and the relationship is found stronger during economic upturns.

This paper examines 59 accounting items that are cross-firm commonly reported and that have data availability in Compustat. The external validity might be an issue.

This paper is of interest to standard setters, corporate management and academics who wish to understand and improve the value of accounting information in the capital market.

This paper is the first study which provides a comprehensive examination of the impact of investors’ information search volumes on the explanatory power of accounting information. It is also the first paper that intrudes Google Trends search volume data into accounting research.

As one of the main purposes of financial statements is to provide relevant information for investors, relationships between share prices and accounting variables have been widely researched. Early studies focus mainly on earnings, but attention has turned in recent years to valuation models that include the book value of the equity. Many of these studies cite the residual income model as their theoretical base and, with the growing emphasis on shareholder value, residual income measures are more commonly used in the business community to track financial performance. Given such trends, the purpose of this paper is to review the theoretical background of the residual income model and discuss results of empirical studies that use it.

The study seeks an understanding of how published accounting information relates to share prices in the developed market in Asia, outside Japan. More specifically, the study aims to extend the international literature in market based accounting research by examining empirical evidence on relationships between share prices and the two summary accounting variables of equity book value and earnings for firms listed on the stock exchange in Malaysia.

The findings imply that, the two accounting variables summarising the balance sheet and the income statement, respectively, are significant factors in the valuation process, and that managers are justified in using the accounting system as a primary source of information for monitoring financial performance.

These findings should be of interest to other researchers, and to managers and investors who currently use or are planning to use residual income to monitor business performance.

This study investigates the efficiency of value relevance and faithful representation when stock market price derivates from its firm value to the investigated IT companies listed in FTSE Taiwan 50. The empirical investigation reveals one financial indicators: Return on equity (ROE) has explanatory ability among seven financial indicators, earnings per share (EPS), book value (BV), dividend yield (Div.), price–earnings ratio (P/E), ROE, return on assets (ROA), and return on operating asset (ROOA) to both sampled companies, United Microelectronics Corporation, UMC, (2303) and Taiwan Semiconductor Manufacturing Company Limited, TSMC, (2330). Furthermore, the empirical results indicate that the higher order moments, skewness and kurtosis, of price deviation do not provide a reliable prediction or explanatory power for stock price trends.

The diversity of social forms both regionally and historically calls for a paradigmatic reassessment of concepts used to map human societies comparatively. By differentiating “social analytics” from “explanatory narratives,” we can distinguish concept and generic model development from causal analyses of actual empirical phenomena. In so doing, we show how five heuristic models of “modes of social practices” enable such paradigmatic formation in sociology. This reinforces Max Weber’s emphasis on the irreducible historicity of explanations in the social sciences.

Explanatory narrative.

A paradigmatic consolidation of generalizing concepts, modes of social practices, ideal-type concepts, and generic models presents a range of “theoretical tools” capable of facilitating empirical analysis as flexibly as possible, rather than cramping their range with overly narrow conceptual strictures.

To render social theory as flexible for practical field research as possible.

Develops a way of synthesizing diverse theoretical and methodological approaches in a highly pragmatic fashion.

This article aims to explore the so-called illusion of explanatory depth (IOED) of managers regarding their understanding of digital technologies and examines the effect of knowledge visualization one’s current understanding and decision making. Its purpose is to show that managers think they know more than they do and that this affects decision making but can be reduced through knowledge visualization.

In two experiments with experienced managers, the authors investigate the size and impact of the IOED bias in decision making and examine if sketched self-explanations are as effective as written self-explanations to reduce the bias.

The findings show that experienced managers suffer from a significant illusion concerning their explanatory understanding of digital technologies and that sketching one’s current level of explanatory understanding of these technologies supports the accurate calibration of one’s knowledge. The findings indicate that sketching knowledge is a helpful modality for the detection and subsequent recalibration of biased knowledge in domain-dependent decision making.

This article is the first to explore the effect of sketched knowledge externalization on the calibration of explanatory knowledge of managers. It extends the literature on both, the IOED and on knowledge visualization as an instrument of knowledge calibration.