Classifying Firms’ Performance Using Data Mining Approaches

Superior prediction and classification in determining company’s performance are major concern for practitioners and academic research in providing useful or important information to the shareholders and potential investors for investment decision. Generally, the normal practice to analysed firm’s performance are based on financial indicators reported in the company’s annual report including the balance sheet, income and cash flow statements. In this work, a few popular and important benchmarking machine learning techniques for the data mining including neural networks, support vector machine, rough set theory, discriminant analysis, logistic regression, decision table, sequential minimal optimization and decision tree have been tested as to classify firm’s performance. The data mining techniques produce high classification rate that is more than 92%. This work also has reduced total number of ratios to be evaluated due to long processing time and large processing resources. Finally, the CA/TA, S/TA, E/TA, GM, FC, PBT/TA, and EPS have been considered for of the final reduced financial ratios. The results show that the 7 reduced ratios are comparable as the common 24 ratios. And to the still produce high classification rate and able classify the firm’s performance

Prediction stock price movement using subsethood and weighted subsethood fuzzy time series models

Forecasting is the prediction process for the future value. The closing price is usually used to forecast stock price movement in the next period. Predicted stock prices in the investment world become an important thing for stock trading activities. The forecasting process can be the most challenging problems due to difficulty and uncertainty of stock market because stock markets are essentially complex, dynamic, and usually in a nonlinear pattern. One of the novel forecasting methods in this area is fuzzy time series (FTS). This paper proposed stock price movement forecasting using first order and high order weighted subsethood fuzzy time series (WeSuFTS) and subsethood fuzzy time series (SuFTS) methods. A set of secondary data gained from the Kuala Lumpur Stock Exchange (KLSE) website. We chose Malaysian Resources Corp Bhd and we collected the historical data for two months, which is on a day-to-day basis. The performance of four models was analyzed using absolute percentage error (APE), mean square error (MSE), mean absolute percentage error (MAPE) and root mean squared error (RMSE). From the evaluation part of data, the results revealed second order SuFTS is the best model to forecast stock price movement with forecasting error from 0.66% - 6.44% (APE), 2.43% (MAPE), 0.00042 (MSE) and 0.0205 (RMSE)

A study on factors influencing student loyalty towards higher learning institution

Student loyalty is referred as student willingness to provide positive appraise about their institution and give good recommendation to other people such as friends, family, employers, and organizations. It is an important issue for university authorities in working on long-term strategic planning. In order to ensure student loyalty towards university, their planning should include strategies on providing the best services despites budget constraints, student accommodation placement, competition with other universities and lack of student enrolment. Previous studies have revealed that student loyalty is affected by various factors namely student satisfaction, student trust, service quality and university image. Typically, these factors are inter-correlated with each other. Hence, statistical method such as multiple linear regression which frequently used method in this type of study is inappropriate since it is very susceptible to inter-correlation between variables. The Partial Least Square (PLS) modelling is more suitable for constructing predictive model in the situation. The results indicate that students’ choices on university may highly depends on the services provided by university and the university image. It also shows that the most important service quality that students emphasizes is on instructor quality and social environment. Since university image also one of the significant factors that influences student loyalty, it is crucial for university to retain a good reputation in the public by providing good value of mone

Trend of Labor Force Participation in Malaysia

Labor force is one of important criterion that contribute to the success of a country. Higher education level among labor force indicates their ability to absorb certain knowledge and skills that are needed in developing country. As Malaysia’s current goal is to become a developed country and high-income nation in the near future, efficient used of labor with understanding of their current standing in skills and expertise is critical. In this study, we aim to explore the trend of labor force participation in Malaysia. Descriptive analysis was conducted to the data of number of labor force and their education attainment from 1982 to 2016, extracted from Department of Statistics Malaysia website. Further, regression analysis was employed to estimate the number of labor force and education level for the next 10 years. The finding indicates that the number of labor force with higher education level is increasing over time, while labor force with primary education decreasing, and we should expect zero labor force with no formal education from 2019 onward. This preliminary exploration on labor trend in Malaysia can improve understanding of Malaysia’s current labor force to further improve education system that correspond to the job creation that of relevance in the futur

Relationship between Student Perception on Self-achievements and Attitudes toward Statistics: A Spearman’s Correlation Analysis

Students’ perception on statistics is an important element in making sure that they love or do not love the subject. When students interested in learning statistics, their performance in that subject will be good and vice versa. In this study, the relationship between students’ perception on their self-achievement in mathematics and their attitude toward elementary statistics course was investigated. The respondents were selected from different courses, ethnic and religious background, gender, and age group. Sample respondents of 274 were chosen from 18 different classes for elementary statistics course in 2015–2016 academic years. Every class size ranged from 41 to 57 students. A Spearman’s correlation analysis was adopted to examine the relationship. A preliminary study on the data shows that there are seven factors contributing to students’ attitude toward elementary statistics course. Students’ perception on their selfachievement in mathematics and their attitude toward statistics course are related to each other. This study found that students with a good mathematics achievement tend to have a more positive attitude toward statistic

PLS equation model of student loyalty based on gender in IR 4.0 environment

Students loyalty and attrition is an important issue for university authorities. Earlier studies have discovered that many factors influencing student loyalty towards their higher learning institutions such as student satisfaction, university image, student trust, and service quality. However, these factors have high relationship correlated with each other. Therefore, this study used the Partial Least Square (PLS) to create a path model which shows the relationship between all factors related to student loyalty. The results from this study revealed that student satisfaction is the most important factors that influence student loyalty, followed by image of university and student commitment. Analysis based on gender shows that female student model have a same pattern with overall student loyalty model but the male student loyalty model is simpler just consist student satisfaction. On the other hand, factors technology, social environment and quality of instructor gave a great influence towards student satisfaction. Therefore, in order to improve student loyalty, university should keep on improving to satisfy students’ requirement

Identifying volunteers’ motivation: a factor analysis study

This paper focuses on the motivational factors that influence the volunteering acts of Universiti Utara Malaysia (UUM) students from the Foundation Management course by using factor analysis. The volunteering programme at the university are vastly expanded in the form of charity works, funding, sports activities, events, and other aspects by which the main and primary volunteers are students. This study was conducted to identify students’ volunteer motivation that complement the programme organiser goal and objectives. This is a quantitative research by using factor analysis method and questionnaire survey is conducted on the UUM students (n=204). In this study, only variables with factor loading of greater than 0.4 is included in the analysis. From extraction sums of squared loading, 5 factors with eigenvalues higher than one were extracted. The volunteer’s motivation of UUM students may be summarized in 5 factors which represents 61.49% of total variance explained. Research findings reveal only four main reasons that influenced students’ volunteer motivation. The main factor that influenced students’ volunteer motivations was self-enhancement. The other factors that follows were expression of values, career orientation, and interpersonal contacts. In the future, any volunteering programme that is planned needs to consider the students interests in order to fully maximize the students’ participation which concurrently would help the organiser to achieve the desired objective and outcome of the programme itself

A Factor Analysis of Students’ Attitudes Towards Statistics in Higher Learning Institution Malaysia

Attitudes toward statistics have received a great attention by academic and researcher in statistics and mathematics education. This paper investigates students’ attitudes towards statistics in one of the Malaysia higher education institution and determined the factors that explain the attitude of students toward statistics. Stratified sampling method was used to select 274 respondents from 18 classes Elementary Statistics course. The attitudes toward statistics were measured using the Survey Attitude Towards Statistics (SAT-36) scale. Factor analysis was applied to identify the factors on students’ attitude toward statistics. Findings from factor analysis revealed that seven factors that explain the attitude of the students were self-determination, cognitive competence, effort, value of statistics in professional life, difficulty, statistical solving and value of statistics in everyday life. The results suggested that statistics should be demonstrated in more practical way for developing more positive attitude towards statistics. This could further promote the learning process and students’ ability to apply the statistical concept

Relative satisfaction index on students’ satisfaction towards hostel facilities

This study aims to determine the level of satisfaction among undergraduate students based on Relative Satisfaction Index (RSI) on hostel facilities. The RSI on its facilities will be compared between students who stay outside the campus and students who stay inside the campus, and also among the 2genders. The study uses a survey based on the questionnaire administered to280 respondents (undergraduate students) who stayed at a hostel provided by a university in Malaysia. By using descriptive analysis, the levels of satisfaction among the respondents (undergraduate students) on the hostel facilities are identified and examined. The data analysis indicates that the levels of satisfaction between undergraduate students are mixed. Nevertheless, on average the students’ satisfaction levels are considered fair towards the facilities provided by the university based on the values of RSI (0.51 and 0.79). The data analysis and research results will assist the university to enhance its facilities to improve the students’ satisfaction