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The stock market is often seen as a sentiment indicator which can indirectly impact the GDP (gross domestic product) of the nation either negatively or positively. At the personal level, many may have invested their life savings in retirement funds which are managed by professional fund managers. Thus if fund managers performed badly in the stock market, the performance of stock market will indirectly impact the returns of the retirement fund under their care. However, due to the huge amount of data that they are dealing with, it can be very challenging if not humanly impossible to analyze all the data to come up with an accurate model of the performance as well as the potential of the stocks. Artificial intelligence can help improve the effectiveness of fund managers. This can then allow a fund manager to manage multiple funds at a time, which in turn lowers the fund management fees, effectively giving the client a bigger capital return. This paper describes a novel approach of using financial news sentiment and genetic algorithm to predict the performance of Malaysian stock prices. The performance of the final optimized model is also compared with other popular predictive models. [ABSTRACT FROM AUTHOR]

Engineering schools and faculties in Malaysia normally adopt analytical tools to assess students' attainment of program outcomes. These tools are often sophisticated and emphasized only on the tabulation and display of data. The process stops short in promoting meaningful continual quality improvement actions for their programs. Literature review revealed that schools and faculties often failed to perform a meaningful analysis of students' outcomes and presented ambiguous plans on the utilization of data for continual quality improvement. In this study, the authors would demonstrate some practices in the measurement and evaluation of outcomes, which results in effective continual quality improvement actions with sustainable efforts from program owners. Analysis and discussion were carried out on the documents related to quality improvement from two higher learning institutions complemented with an analytical tool known as Engineering Outcome Analytics developed by Tunku Abdul Rahman University College. It was demonstrated that an effective outcomes-based education measurement system focuses on practicality where educators are more readily to expend efforts on assessment and evaluation of outcomes, creating a culture of continual quality improvement (CQI) rather than having a sophisticated measurement system that requires enormous data entries. The study is expected to benefit engineering educators and program owners in designing or improving their existing analytical tools to address the continual quality improvement requirements stipulated by the Engineering Accreditation Council (EAC) by highlighting the shortcomings that can be avoided and best practices that can be adopted. [ABSTRACT FROM AUTHOR]