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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 purpose of this paper is to propose a mobile and social-based question-and-answer (Q&A) system that analyzes users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers.

This research applies first-order logic (FOL) inference calculation to generate question/interest ID that combines a users' social information, interests and social network intimacy to choose the nodes that can provide high-quality answers. After receiving a question, a friend can answer it, forward it to their friends according to the number of TTL (Time-to-Live) hops, or send the answer directly to the server. This research collected data from the TripAdvisor.com website and uses it for the experiment. The authors also collected previously answered questions from TripAdvisor.com; thus, subsequent answers could be forwarded to a centralized server to improve the overall performance.

The authors have first noticed that even though the proposed system is decentralized, it can still accurately identify the appropriate respondents to provide high-quality answers. In addition, since this system can easily identify the best answerers, there is no need to implement broadcasting, thus reducing the overall execution time and network bandwidth required. Moreover, this system allows users to accurately and quickly obtain high-quality answers after comparing and calculating interest IDs. The system also encourages frequent communication and interaction among users. Lastly, the experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.

This paper proposes a mobile and social-based Q&A system that applies FOL inference calculation to analyze users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers. The experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.

In this article we design an econometric test for monotone comparative statics (MCS) often found in models with multiple equilibria. Our test exploits the observable implications of the MCS prediction: that the extreme (high and low) conditiona l quantiles of the dependent variable increase monotonically with the explanatory variable. The main contribution of the article is to derive a likelihood-ratio test, which to the best of our knowledge is the first econometric test of MCS proposed in the literature. The test is an asymptotic “chi-bar squared” test for order restrictions on intermediate conditional quantiles. The key features of our approach are: (1) we do not need to estimate the underlying nonparametric model relating the dependent and explanatory variables to the latent disturbances; (2) we make few assumptions on the cardinality, location, or probabilities over equilibria. In particular, one can implement our test without assuming an equilibrium selection rule.

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 this chapter we explore different ways to obtain decompositions of rank-dependent indices of socioeconomic inequality of health, such as the Concentration Index. Our focus is on the regression-based type of decomposition. Depending on whether the regression explains the health variable, or the socioeconomic variable, or both, a different decomposition formula is generated. We illustrate the differences using data from the Ethiopia 2011 Demographic and Health Survey (DHS).

This study documents that high book‐to‐market (value) and low book‐to‐market (glamour) stock prices react asymmetrically to both common and firm‐specific information. Specifically, we find that value stock prices exhibit a considerably slow adjustment to both common and firm‐specific information relative to glamour stocks. The results show that this pattern of diferential price adjustment between value and glamour stocks is mainly driven by the high arbitrage risk borne by value stocks. The evidence is consistent with the arbitrage risk hypothesis, predicting that idiosyncratic risk, a major impediment to arbitrage activity, amplifies the informational loss of value stocks as a result of arbitrageurs’ (informed investors) reduced participation in value stocks because of their inability to fully hedge idiosyncratic risk.

This study aims to exploit Tobin’s Q model of investment to examine the relationship between corporate governance and green innovation.

The study is based on a sample of 3,896 firms from 2002 to 2021, covering 45 countries worldwide. The authors adopt Tobin’s Q model to conceptualize the relationship between corporate governance and investment in green research and development (R&D). The authors argue that agency costs and financial market frictions affect corporate investment and are fundamental factors in R&D activities. By limiting agency conflicts, effective governance favors efficiency, facilitates access to external financing and encourages green innovation. The authors analyzed the causal effect by using the system-generalized method of moments (system-GMM).

The results reveal that the better the corporate governance, the more the firm invests in green R&D. A 1%-point increase in the corporate governance ratings leads to an increase in green R&D expenses to the total asset ratio of about 0.77 percentage points. In addition, an increase in the score of each dimension (strategy, management and shareholder) of corporate governance results in an increase in the probability of green product innovation. Finally, green innovation is positively related to firm environmental performance, including emission reduction and resource use efficiency.

The findings provide implications to support managers and policymakers on how to improve sustainability through corporate governance. Governance mechanisms will help resolve agency problems and, in turn, encourage green innovation.

Understanding the impact of corporate governance on green innovation may help firms combat climate change, a crucial societal concern. The present study helps achieve one of the precious UN’s sustainable development goals: Goal 13 on climate action.

This study goes beyond previous research by adopting Tobin’s Q model to examine the relationship between corporate governance and green R&D investment. Overall, the results suggest that effective corporate governance is necessary for environmental efficiency.

The purpose of this paper is to identify a measure of intellectual capital (IC) value which offers new research opportunities for empirical investigations and to examine the determinants of IC value.

In total, 4,488 firm years of German companies are investigated to compare three measures of IC value: market-to-book, Tobin’s q, and long-run value-to-book (LRVTB).

LRVTB is observed to be the IC value measure with the highest explanatory value. This measure provides an approach to empirically test previously untested hypotheses on IC value. The results on testing determinants of IC value indicate that IC value is positively related to leverage and motivational payments to employees and negatively associated with company size. In contrast, recognised intangible assets, research and development (R & D), company age and concentrated ownership show no significant effects.

The findings on IC value measures contribute to IC research as they offer a way to estimate IC value for testing IC-related hypotheses. The findings on IC determinants have implications for IC management as the relevant determinants can be considered for IC value creation.

This paper responds to the challenge posed by previous IC research to develop more creative quantitative approaches to estimate IC value (Marr et al., 2003; Mouritsen, 2006) in order to test IC-related hypotheses by innovatively applying a measure from mergers and acquisitions research to IC.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.