Since the past decades, prediction of stock price has been an important and challenging task to yield the most significant profit for a company. In the era of big data, predicting the stock price using machine learning has become popular among the financial analysts since the accuracy of the prediction can be improved using these techniques. In this paper, auto-regressive integrated moving average (ARIMA), neural network (NN) and long short-term memory network (LSTM) have been used to predict Bursa Malaysia’s closing prices data from 2/1/2020 to 19/1/2021. All the models will be evaluated using root mean square errors (RMSE) and mean absolute percentage errors (MAPE). The results showed that LSTM able to generate more than 90% of accuracy in predicting stock prices during this pandemic period.
Non-Muslims in Malaysia have a lower participation rate in takaful insurance. The purpose of this paper is to examine the factors that influence acceptance level of takaful insurance products among non-Muslims in Malaysia. In this paper, independent variables: awareness, attitude, service quality and relative advantage were examined using the Multiple Linear Regression (MLR) model to analyze the effect of dependent variable: non-Muslim acceptance level. The methodology used was a purely quantitative survey using convenience sampling with data collected from a sample of non-Muslims participants in Malaysia. The findings revealed that attitudes and relative advantage are found to be the significant factors that influence the acceptance level on takaful insurance products while awareness and service quality do not account for acceptability on takaful. It was also discovered that attitude is the most significant factor towards acceptance level on takaful insurance products among non-Muslims in Malaysia. The paper provides insight for understanding the factors that lead to consumers’ purchase intention of takaful insurance products in Malaysia. Furthermore, this study gives valuable ideas for takaful insurance companies to develop appropriate takaful insurance and build marketing strategies to enhance takaful insurance participation in Malaysia.
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