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This study seeks to identify the impact of sustainable development goals (SDGs) in predicting corporate financial performance (CFP) in the information communications technology (ICT) industry. Data over the period of 2016–2020 that are relevant to financial reporting and corporate social responsibility (CSR) reporting have been extracted for 208 firms in the ICT industry. Important variables have been identified to help predict the financial performance in the following years upon the publication of CSR reports. Drawing on resource‐based view and stakeholder theory, the purpose of this study is to find the quintessential variables that influence the prediction accuracy of financial performance. To better forecast earnings per share (EPS), machine learning feature selection methods have been implemented. The findings suggest that certain variables such as return on total assets, SDGs adoption and whether the firm has established KPI for SDGs achievements can help enhance EPS prediction. With the various predictive models, the artificial neural network model is the most effective in predicting CFPs. Most importantly, the adoption of SDGs can be utilized to sharpen the forecast on financial performance as it enables firms to bolster stakeholder engagement and evaluate environmental, social, and corporate governance efforts. [ABSTRACT FROM AUTHOR]

Data mining is a very popular technique and has been widely applied in different areas these days. The artificial neural network has become a very popular alternative in prediction and classification tasks due to its associated memory characteristics and generalization capability. However, the relative importance of potential input variables and the long training process have often been criticized and hence limited its application in handling classification problems. The objective of the proposed study is to explore the performance of data classification by integrating artificial neural networks with the multivariate adaptive regression splines (MARS) approach. The rationale under the analyses is firstly to use MARS in modeling the classification problem, then the obtained significant variables are used as the input variables of the designed neural networks model. To demonstrate the inclusion of the obtained important variables from MARS would improve the classification accuracy of the networks, diagnostic tasks are performed on one fine needle aspiration cytology breast cancer data set. As the results reveal, the proposed integrated approach outperforms the results using discriminant analysis, artificial neural networks and multivariate adaptive regression splines and hence provides an efficient alternative in handling breast cancer diagnostic problems. [Copyright &y& Elsevier]

Credit scoring has become a very important task as the credit industry has been experiencing double-digit growth rate during the past few decades. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the decision of network''s topology, importance of potential input variables and the long training process has often long been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring by integrating the backpropagation neural networks with traditional discriminant analysis approach. To demonstrate the inclusion of the credit scoring result from discriminant analysis would simplify the network structure and improve the credit scoring accuracy of the designed neural network model, credit scoring tasks are performed on one bank credit card data set. As the results reveal, the proposed hybrid approach converges much faster than the conventional neural networks model. Moreover, the credit scoring accuracies increase in terms of the proposed methodology and outperform traditional discriminant analysis and logistic regression approaches. [Copyright &y& Elsevier]

This study investigates the information content of SGX-DT Nikkei 225 and MSCI Taiwan index futures prices during the non-cash-trading (NCT) period. The lead–lag relationship between the futures market during the NCT period and the cash market during its opening period is first investigated by the generalized methods of moments. The obtained leading futures and previous day''s cash market closing index are then used as the input variables to predict the opening cash price index by the backpropagation neural network model. Sensitivity analysis is first employed to address and solve the issue of finding the appropriate setup of the topology of the networks. Extensive studies are then performed on the robustness of the constructed network by using different training and testing sample sizes. To demonstrate the effectiveness of our proposed method, the 5-min intraday data of spot and futures index from a 6-month historical record were evaluated using the designed neural network model. Analytic results demonstrate that the proposed neural network model outperforms the neural network model with previous day''s closing index as the input variable, the random walk and GARCH model forecasts. It, therefore, indicates that there is valuable information involved in the futures prices during the NCT period that can be used to forecast the opening cash price index. Besides, the neural network model provides better forecasting results than the commonly discussed GARCH model. [Copyright &y& Elsevier]

The study investigates the information content of SGX-DT Nikkei 225 futures prices during the non-cash-trading (NCT) period using an artificial neural network model. The cash market closing index, the futures prices from a period in the same trading day and on the following trading day are utilized to determine the appropriate input nodes of a back propagation neural network model in forecasting the opening cash price index. Sensitivity analysis is first employed to address and solve the issue of finding the appropriate network topology. Extensive studies are then performed on the robustness of the constructed network by using different training and testing sample sizes. The effectiveness of the method is demonstrated on data from a 6-month historical record (1998–99). Analytic results demonstrate that the proposed neural network model outperforms a neural network model with the previous day's closing index as the input node and the random walk model forecasts. It, therefore, indicates that there is valuable information involved in futures prices during the NCT period that can be used to forecast the opening cash market price index. [ABSTRACT FROM AUTHOR]