Fund Size and Performance of Unit Trust Funds in Kenya

Objective - Even though the unit trust industry witnessed high growth in portfolios size, performances and regulations, however, there is still insufficient understanding of various aspects of the industry such as the lack of information on the funds' unit price behavior and their trend in the long-term investment. This study, therefore, aims to investigate the long-run relationship between the chosen macroeconomic factors and the NAV of the Islamic equity unit trust funds as well as the effect of the (2007-2008) global financial crisis on the performance of Islamic equity unit trust funds and their unit prices. Methodology/Technique - The study utilized Vector Autoregression (VAR) framework in order to analyze this relationship over the period January 2006 to December 2012 using monthly data. Findings - The findings of the investigations confirmed that the selected macro-economic variables do share long-run relationship with the NAV of the Islamic equity unit trust funds in the Malaysian capital market. Novelty - A successful innovation of the relationship between the funds NAV and the selected macroeconomic variables will assist the entire interested group, such as, investors, management of the funds, government agencies, industry players and policy makers to estimate the future trend direction of the NAV and accordingly decide on the operational, managerial, and sustainable growth decisions. Type of Paper - Empirical Paper Keywords: , Islamic Equity Unit Trust Funds; Macroeconomic Variables; Malaysia

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Evaluation on international unit trust funds: A case for Malaysia

2011 International Conference on Business, Engineering and Industrial Applications ◽

10.1109/icbeia.2011.5994252 ◽

2011 ◽

Cited By ~ 1

Author(s):

Abdul Halmie Muhamad ◽

Abdul Halim Mohd Nawawi

Keyword(s):

International Unit ◽

Trust Funds ◽

Unit Trust

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Performance profiling of the unit trust funds in Malaysia with data mining techniques

F1000Research ◽

10.12688/f1000research.73467.1 ◽

2021 ◽

Vol 10 ◽

pp. 1272

Author(s):

Aida Farah Khairuddin ◽

Keng-Hoong Ng ◽

Kok-Chin Khor

Keyword(s):

Data Mining ◽

Risk Tolerance ◽

Financial Data ◽

Performance Profiling ◽

Data Mining Techniques ◽

Trust Funds ◽

Expectation Maximisation ◽

Yearly Average ◽

Retail Investors ◽

Unit Trust

Background: Millennials are exposed to many investment opportunities, and they have shown their interest in gaining more income via investments. One popular investment avenue is unit trusts. However, analysing unit trusts’ financial data and gaining valuable insights may not be as simple because not everyone has the required financial knowledge and adequate time to perform in-depth analytics on the numerous financial data. Furthermore, it is not easy to compile the performance of each unit trust available in Malaysia. The primary objective of this research is to identify unit trust funds that provide higher returns than their average peers via performance profiling. Methods: This research proposed a performance profiling on Malaysia unit trust funds using the two data mining techniques, i.e., Expectation Maximisation (EM) and Apriori, to assist amateur retail investors to choose the right unit trust based on their risk tolerance. EM clustered the unit trust funds in Malaysia into several groups based on their annual financial performances. This was then followed by finding the rules associated with each cluster by applying Apriori. The resulted rules shall serve the purpose of profiling the clustered unit trust funds. Retail investors can then select their preferred unit trust funds based on the performance profile of the clusters. Results: The yearly average total return of the financial year 2018 and 2019 was used to evaluate unit trust funds’ performance in the clusters. The evaluation results indicated that the profiling could provide valuable and insightful information to retail investors with varying risk appetites. Conclusions: This research has demonstrated that the financial performance profiling of unit trust funds could be acquired via data mining approaches. This valuable information is crucial to unit trust investors for selecting suitable funds in investment.

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