A good price-earnings ratio is a result of excellent corporate performance; nevertheless, reaching a desirable price-earnings ratio in typical Nigerian manufacturing enterprises is complex and difficult. Meeting price-earnings ratio expectation of shareholder as companies were faced with complexities and unethical non-compliances issues. Studies have suggested that effective environmental disclosure has the capacity to enhance the price-earnings ratios in manufacturing companies. This study was to examine the effect of environmental disclosure on the price-earnings ratio of listed manufacturing companies listed in Nigeria. The study employed an ex-post facto research design. The population of the study was the 66 listed manufacturing companies listed on the Nigerian Exchange Group as of 31st December 2021. Using purposive sampling technique 29 manufacturing companies were selected. Validated data, covering a period of 16 years (2006 -2021) were extracted from published financial statements of the selected manufacturing companies. The reliability of the data was premised on the statutory audit of the financial statements. Descriptive and inferential (multiple regression) statistics were used to analyze the data at a 5% significant level. The findings revealed that environmental disclosure affected the price-earnings ratio of manufacturing companies in Nigeria (Adj.R2 = 0.218, F (5, 458) = 22.87, p < 0.05) . The study recommended that the management of companies should embrace sustainable environmental disclosure to ensure an effective price-earnings ratio for manufacturing companies in Nigeria
The return on assets indicator informs existing and potential investors how effectively management has optimally utilized its resources from investments. Studies have advanced that manufacturing companies’ level of commitment has the ability to enhance earnings and corporate return on assets. Lack of optimal utilization of corporate assets and non-compliance with environmental requirements has weakened the earnings of manufacturing companies. Evidence has revealed that not many manufacturing coming have integrated environmental disclosure into their operations. This study examined the effect of environmental disclosure on the return on assets of manufacturing companies listed in Nigeria. The study adopted an ex-post facto research design. The population consisted of the 66 listed manufacturing companies listed on the Nigerian Exchange Group as of December 31, 2021. Purposive sampling was used to select 29 manufacturing businesses. Validated data were taken from the public financial statements of the selected manufacturing enterprises for 16 years (2006-2021). A statutory audit of the financial accounts was used to ensure the data's veracity. Descriptive and inferential (multiple regression) statistics were utilized to assess the data. The study revealed that environmental disclosure affects the return on assets of manufacturing companies listed in Nigeria (Adj.R2 = 0.017, F(5, 458) = 8.333, p < 0.05). The study recommended that managers of manufacturing companies should integrate environmental disclosure as a critical duty as evidence of policies in practice in protecting the environment towards gaining legitimacy and patronage in deepening the return on assets of the companies.
Globally, when various channels of revenue available to the government fail to yield adequate resources to handle government expenditure or financial responsibilities, the government resorts to borrowing as an alternative source to complement revenue from taxes and other sources. However, the inability to optimally utilize borrowed funds had resulted in a high public debt profile and had retarded the economic growth of the Nigerian economy over the years. Consequently, this study investigated the effect of public debt management on economic growth in Nigeria. An ex-post facto research design was employed, while time-series data on the relevance of macroeconomic variables to public debt management and economic growth were sourced from secondary sources. The sample population purposively was chosen from data available from the 2020 edition of the Central Bank of Nigeria’s (CBN) Statistical Bulletin, which covers 40 years (1981-2020). Results revealed that public debt management RGDP) had a positive significant effect on economic growth in Nigeria (AdjR2 = 0.995; F (5, 31) = 99.562; p-value = 0.000). The conclusion validated that effective public debt management tends to have a positive significant effect on economic growth in Nigeria. It is therefore recommended that adequate measures be put in place to ensure optimal investment of borrowed funds in productive ventures in Nigeria Also, the loans should be serviced when they are due to avoid sanctions and accumulation default charges.
This study investigated the effect of income smoothing and earning management on the credibility of accounting information of listed manufacturing companies in Nigeria. Data used were extracted from the annual reports and accounts of the selected sixteen (16) firms for a period of 10 years (2010-2019) while content analysis was adopted in measuring accounting information credibility. Multiple linear regression analysis (OLS) method was adopted for the analysis. The result revealed that income smoothing and earnings management had statistically insignificant effect on fundamental qualitative characteristics (FQC), while income smoothing and earnings management had statistically significant effect on enhancing qualitative characteristics (EQC). However, the study obtained that income smoothing and earnings management had positive and significant effect on the credibility of accounting information of the listed manufacturing companies in Nigeria. The study opined that managers should ensure that accounting information is credible, possesses desirable accounting information qualities of relevance, and faithful representation, also verifiable, comparable, understandability, and timeliness. Contribution/Originality:This study is one of very few studies which have investigated how earnings management practices impacted on objectivity of reported financial information and breach of investors' trust in the management due to information asymmetry. INTRODUCTIONCredibility of accounting information is highly desired because it has the potency and ability to add value to investment decision made, using credible accounting information since it is reliable and dependable. The lending credibility theory suggested that the primary function of the auditors is to add value and credibility to the financial statement prepared by the management. Credibility of accounting information is a priceless and inestimable commodity that can be offered by the auditor to the public. In other words, the value of accounting information is absolutely void and insignificantly valueless if the credibility of its information contents is lacking (Jung, Soderstrom, & Yang, 2013). Credibility and reliability of accounting numbers enhance users' confidence in using the financial statements, it is capable of adding value to investment decisions and a reflection of the absence of information asymmetry, a hallmark of transparency and accountability and virtue that every audited financial
Educational evaluation is a major factor in determining students’ learning aptitude and academic performance. The scoring technique that relies solely on human labour is time consuming, costly, and logistically challenging as this rating is usually based on the opinion of “biased” human. Several studies have considered using machine learning techniques with feature extraction based on Term Frequency (TF) - Part of Speech (POS) Tagging without consideration to global vectorization (GloVe). These solutions require the process of selecting deterministic features that are directly related to essay quality which is time-consuming and needs a great deal of linguistic knowledge. Gated Recurrent Unit (a variation of Recurrent Neural Network) deep learning technique with focus on morphological analysis of essays for content-based assessment has therefore shown the capability of addressing the challenges posed by other AES techniques by building more abstract and complete linkages among features. Deep learning algorithms such as Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were used to learn the model with performance evaluation on metrics such as validation accuracy, training time, loss function, and Quadratic Weighted Kappa. The performance results showed that MLP, LSTM and GRU had average Quadratic Weighted Kappa (QWK) values of 0.65, 0.86 and 0.88 respectively with each algorithm having an average training time of 61.4, 62.68 and 67.86 seconds respectively. The loss functions for MLP, LSTM and GRU were 0.296, 0.24 and 0.126. This meant that GRU had the best estimate of the difference between the actual and forecasted scores. MLP, LSTM, and GRU had average validation accuracy of 0.48, 0.537, and 0.511 respectively. GRU was shown to be the optimal classifier and was used in the development of the essay scoring model.
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