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The primary objective of this study is to investigate whether the inclusion of convertible bond prices as important inputs into artificial neural networks can lead to improved accuracy in predicting Chinese stock prices. This novel approach aims to uncover the latent potential inherent in convertible bond dynamics, ultimately resulting in enhanced precision when forecasting stock prices.

The authors employed two machine learning models, namely the backpropagation neural network (BPNN) model and the extreme learning machine neural networks (ELMNN) model, on empirical Chinese financial time series data.

The results showed that the convertible bond price had a strong predictive power for low-market-value stocks but not for high-market-value stocks. The BPNN algorithm performed better than the ELMNN algorithm in predicting stock prices using the convertible bond price as an input indicator for low-market-value stocks. In contrast, ELMNN showed a significant decrease in prediction accuracy when the convertible bond price was added.

This study represents the initial endeavor to integrate convertible bond data into both the BPNN model and the ELMNN model for the purpose of predicting Chinese stock prices.

Social Network Analysis has been posited as a useful technique to determine if leadership development programs are an effective intervention in developing social ties and enhancing connectivity among leaders in an organization. Evaluations can examine the extent to which the leadership development programs create and catalyze peer networks. This study used Social Network Analysis to evaluate the development of a peer leadership network and resulting relationships among leaders participating in a leadership development program. Several predictions were made about the development of participants’ task, career, and social networks, generally predicting enhanced “esprit de corps” with their peer leaders over time. Thirty top executives in local public health were selected to participate in a 12-month national leadership development training program. Peer network development was documented at three time points across the programmatic year at 6-month intervals. The results demonstrated that while leaders’ social networks increased over time, friendship networks increased more slowly than did acquaintance networks. The task-related networks involving interactions to solve problems, and career networks for seeking advice and support increased over time, with task-related and advice-related networks stabilizing by the end of the second workshop. Implications for developing peer leadership networks are discussed.

The authors would like to acknowledge the Robert Wood Johnson Foundation and the National Association for County and City Health Officials and for their support of this research.

The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.

The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.

The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.

This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).

Despite the wide availability of internet banking, levels of intention to use such facilities remain variable between countries. The purpose of this paper is to focus on e-banking in a country with low intention to use e-banking – Jordan – and to explain the slow uptake.

A quantitative method employing a cross-sectional survey was used as an appropriate way of meeting the research objectives. The survey was distributed to bank customers in Amman, Jordan, collecting a total of 328 completed questionnaires. SPSS and AMOS software were used, and multiple regression and artificial neural networks were applied to determine the relative impact and importance of e-banking predictors.

The statistical techniques revealed that several major factors, including perceived ease of use, perceived usefulness, security and reasonable price, stand out as the barriers to intention to use e-banking services in Jordan.

This study theorizes a series of implications on intention to use e-banking. It draws the attention of Jordanian banks to the full functionality of their e-banking systems, emphasizing positive safety features, which could contribute to changing negative customer perceptions. It also contributes to eliciting the theory of customer value among banks by focusing on how they should properly enhance their use of shared value. Moreover, it will present to managers how e-banking predictors can send meaningful and timely information to customers.