Annual report narrative disclosures, information asymmetry and future firm performance: evidence from Vietnam

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ly Thi Hai Tran ◽  
Thoa Thi Kim Tu ◽  
Tran Thi Hong Nguyen ◽  
Hoa Thi Lien Nguyen ◽  
Xuan Vinh Vo
Keyword(s):  
Firm Performance ◽  
Information Asymmetry ◽  
Textual Analysis ◽  
Annual Report ◽  
Emerging Market ◽  
Bayesian Algorithm ◽  
Content Type ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Regulatory Reforms

PurposeThis paper examines the role of the annual report’s linguistic tone in predicting future firm performance in an emerging market, Vietnam.Design/methodology/approachBoth manual coding approach and the naïve Bayesian algorithm are employed to determine the annual report tone, which is then used to investigate its impact on future firm performance.FindingsThe study finds that tone can predict firm performance one year ahead. The predictability of tone is strengthened for firms that have a high degree of information asymmetry. Besides, the government’s regulatory reforms on corporate disclosures enhance the predictive ability of tone.Research limitations/implicationsThe study suggests the naïve Bayesian algorithm as a cost-efficient alternative for human coding in textual analysis. Also, information asymmetry and regulation changes should be modeled in future research on narrative disclosures.Practical implicationsThe study sends messages to both investors and policymakers in emerging markets. Investors should pay more attention to the tone of annual reports for improving the accuracy of future firm performance prediction. Policymakers should regularly revise and update regulations on qualitative disclosure to reduce information asymmetry.Originality/valueThis study enhances understanding of the annual report’s role in a non-Western country that has been under-investigated. The research also provides original evidence of the link between annual report tone and future firm performance under different information asymmetry degrees. Furthermore, this study justifies the effectiveness of the governments’ regulatory reforms on corporate disclosure in developing countries. Finally, by applying both the human coding and machine learning approach, this research contributes to the literature on textual analysis methodology.

Impact of ownership structure and dividend on firm performance and firm risk

2019 ◽  
Vol 45 (8) ◽  
pp. 1041-1061
Author(s):  
Abhinav Kumar Rajverma ◽  
Arun Kumar Misra ◽  
Sabyasachi Mohapatra ◽  
Abhijeet Chandra
Keyword(s):  
Firm Performance ◽  
Information Asymmetry ◽  
Ownership Structure ◽  
Corporate Performance ◽  
Emerging Market ◽  
Idiosyncratic Risk ◽  
Firm Risk ◽  
Content Type ◽  
Family Control ◽  
Concentrated Ownership

Purpose The purpose of this paper is to examine the influence of ownership structure and dividend payouts over firm’s profitability, valuation and idiosyncratic risk. The authors further investigate if corporate performance is sector dependent. Design/methodology/approach The study employs signaling and bankruptcy theories to evaluate the influence of ownership structure and dividend payout over a firm’s corporate performance. The authors use a panel regression approach to measure the performance of family owned firms against that of widely held firms. Findings The study confines to firms operating out of emerging markets. The results show that family owned firms are dominant with concentrated ownership. The management pays lower dividend leading to lower valuation and higher idiosyncratic risk. The study further illustrates that family ownership concentration and family control both influence firm performance and level of risk. The findings indicate that information asymmetry and under diversification lead to increased idiosyncratic risk, resulting in the erosion of firm’s value. Results also confirm that firms paying regular dividends are less risky and, hence, command a valuation premium. Originality/value The evidence supports the proposition that information asymmetry plays a significant role in explaining dividend payouts pattern and related impacts on corporate performance. The originality of the paper lies in factoring idiosyncratic risk while explaining profitability and related valuation among emerging market firms.

Does investment in innovation impact firm performance in emerging economies? An empirical investigation of the Indian food and agricultural manufacturing industry

2021 ◽  
Vol 13 (2) ◽  
pp. 233-248
Author(s):  
Manogna R.L. ◽  
Aswini Kumar Mishra
Keyword(s):  
Firm Performance ◽  
Manufacturing Industry ◽  
Emerging Market ◽  
Developed Countries ◽  
Emerging Economy ◽  
Content Type ◽  
Agricultural Industry ◽  
Indian Food ◽  
The Impact ◽  
The Relationship

Purpose The study aims to analyze the impact of Research & Development (R&D) intensity on the firm’s performance, measured by growth of sales in the emerging market like India. Innovation strategy and its outcomes for firms may be different in developing countries as compared to developed countries. Thus, a study that focuses on the emerging economy like India, with a majority of the population dependent on agriculture, is of prime importance to the firm performance in the food and agricultural manufacturing industry. For this study, the broader focus will be on one widely recognised factor which may influence the growth rate of firms, i.e. investment in innovations which is in terms of R&D expenditure. Design/methodology/approach The paper investigates the relationship between the R&D efforts and growth of firms in the Indian food and agricultural manufacturing industry during 2001–2019. To empirically test the relationship between firm’s growth (FG) and R&D investments, system generalised method of moments technique has been used, hence enabling to avoid problems related to endogeneity and simultaneity. Findings The findings reveal that investments in innovations have a positive effect on the growth of firms in the Indian food and agricultural manufacturing industry. Investment in R&D also enables the firms to reap benefits from externalities present in the industry. Further analysis reveals that younger firms grow faster when they invest in R&D. More specifically, this paper finds evidence in the case of the food and agricultural industry that import of raw materials negatively affects the FG and export intensity positively affects the growth in the case of R&D firms. Research limitations/implications This study suggests that the government should encourage the industries to invest optimally in R&D projects by providing favourable fiscal treatments and R&D subsidies which are observed to have positive effects in various developed countries. Originality/value To the best of the author’s knowledge, the current paper is the first to analyse the impact of innovation in food and agricultural industry on firm’s performance in an emerging economy context with the latest data. This paper agrees that a government initiative to increase private R&D expenditure would have favourable effects on FG as growing investments in R&D lead to further growth of the firms.

scholarly journals Classification of Imbalanced Malaria Disease Using Naïve Bayesian Algorithm

2018 ◽  
Vol 7 (2.7) ◽  
pp. 786 ◽  
Author(s):  
T Sajana ◽  
M R.Narasingarao
Keyword(s):  
Comparative Study ◽  
Class Imbalance ◽  
Machine Learning Algorithms ◽  
Bayesian Algorithm ◽  
Naive Bayesian ◽  
Class Distribution ◽  
Naïve Bayesian ◽  
R Programming ◽  
The Impact

Malaria disease is one whose presence is rampant in semi urban and non-urban areas especially resource poor developing countries. It is quite evident from the datasets like malaria, dengue, etc., where there is always a possibility of having more negative patients (non-occurrence of the disease) compared to patients suffering from disease (positive cases). Developing a model based decision support system with such unbalanced datasets is a cause of concern and it is indeed necessary to have a model predicting the disease quite accurately. Classification of imbalanced malaria disease data become a crucial task in medical application domain because most of the conventional machine learning algorithms are showing very poor performance to classify whether a patient is affected by malaria disease or not. In imbalanced data, majority (unaffected) class samples are dominates the minority (affected) class samples leading to class imbalance. To overcome the nature of class imbalance problem, balancing the data samples is the best solution which produces the better accuracy in classification of minority samples. The aim of this research is to propose a comparative study on classifying the imbalanced malaria disease data using Naive Bayesian classifier in different environments like weka and using an R-language. We present here, clinical descriptive study on 165 patients of different age group people collected at medical wards of Narasaraopet from 2014-17. Synthetic Minority Oversampling Technique (SMOTE) technique has been used to balance the class distribution and then we performed a comparative study on the dataset using Naïve Bayesian algorithm in various platforms. Out of balanced class distribution data, 70% data was given to train the Naive Bayesian algorithm and the rest of the data was used for testing the model for both weka and R programming environments. Experimental results have indicated that, classification of malaria disease data in weka environment has highest accuracy of 88.5% than the Naive Bayesian algorithm accuracy of 87.5% using R programming language. The impact of vector borne disease is very high in medical applications. Prediction of disease like malaria is an hour of the need and this is possible only with a suitable model for a given dataset. Hence, we have developed a model with Naive Bayesian algorithm is used for current research.    

Is the MD&A of US REITs informative? A textual sentiment study

2020 ◽  
Vol 38 (3) ◽  
pp. 181-201
Author(s):  
Marina Koelbl
Keyword(s):  
Firm Performance ◽  
Real Estate ◽  
Fixed Effects ◽  
Textual Analysis ◽  
Real Estate Industry ◽  
Content Type ◽  
The Real Estate ◽  
Domain Specific ◽  
Panel Fixed Effects ◽  
Fixed Effects Regression

PurposeThis study examines whether language disclosed in the Management Discussion and Analysis (MD&A) of US Real Estate Investment Trusts (REITs) provides signals regarding future firm performance and thus generates a market response.Design/methodology/approachThis research conducts textual analysis on a sample of approximately 6,500 MD&As of US REITs filed by the SEC between 2003 and 2018. Specifically, the Loughran and Mcdonald (2011) financial dictionary, and a custom dictionary for the real estate industry created by Ruscheinsky et al. (2018), are employed to determine the inherent sentiment, that is, the level of pessimistic or optimistic language for each filing. Thereafter, a panel fixed-effects regression enables investigating the relationship between sentiment and future firm performance, as well as the markets’ reaction.FindingsThe empirical results suggest that higher levels of pessimistic (optimistic) language in the MD&A predict lower (higher) future firm performance. Hereby, the use of a domain-specific real estate dictionary, namely that developed by Ruscheinsky et al. (2018) leads to superior results. Corresponding to the notion that the human psyche is affected more strongly by negative than positive news (Rozin and Royzman, 2001), the market responds solely to pessimistic language in the MD&A.Practical implicationsThe results suggest that the market can benefit from textual analysis, as investigating the language in the MD&A reduces information asymmetries between US REIT managers and investors.Originality/valueThis is the first study to analyze exclusively US REITs, whether language in the MD&A is predictive of future firm performance and whether the market responds to textual sentiment.

Institutional investors’ investment preference and monitoring: evidence from Malaysia

2019 ◽  
Vol 45 (9) ◽  
pp. 1327-1346
Author(s):  
Chwee Ming Tee
Keyword(s):  
Firm Performance ◽  
Institutional Investors ◽  
Capital Expenditure ◽  
Emerging Market ◽  
Firm Valuation ◽  
Profit Margin ◽  
Institutional Setting ◽  
Content Type ◽  
Net Profit ◽  
Different Types

Purpose The purpose of this paper is to examine the investment preference of various types of institutional investors in Malaysia, and its influence on firm valuation, operating performance and capital expenditure. Design/methodology/approach This study employs ordinary least squares model to examine: investment preference according to different types of institutional investors; the association between various types of institutional investors and firm valuation; the association between various types of institutional investors and firm performance; and the association between various types of institutional investors and capital expenditure. Findings The result shows that different types of institutional investors exhibit different investment preference. From the domiciles perspective, local institutional investors (LII) are found to be associated with higher Tobin’s Q, ROA and net profit margin. When viewed from business relationship perspective, “pressure-resistant” institutional investors (PRII) are positively associated with Tobin’s Q, ROA and net profit margin. Both LII and PRII are also associated with higher capital expenditure. Originality/value This study reveals the investment preferences of various types of institutional investors in an emerging market economy. The results show that institutional monitoring is associated with higher firm valuation, higher firm performance and higher capital expenditure. However, the effect is largely driven by local and PRII, particularly government-controlled institutional funds. These evidence suggest that different firm outcomes between emerging and advanced economy can be explained by variation in institutional setting.

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