Abstract
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,analysingunit trusts'financial data and gaining valuable insights may not beas simplebecause 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., ExpectationMaximisation(EM) andApriori, 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 thenfollowed by finding the rules associated with each cluster by applyingApriori. Theresultedrules 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:Theyearlyaveragetotalreturnofthefinancial year2018and 2019was usedto evaluateunit trust funds'performanceinthe clusters. The evaluation results indicated that the profiling could provide valuable and insightful information to retail investors with varying risk appetites. Conclusions:This researchhasdemonstrated that the financial performance profiling of unit trust fundscould be acquired via data miningapproaches. Thisvaluable information iscrucialto unit trust investorsforselecting suitablefundsininvestment.
Highlights
In Malaysia, different investment securities or schemes are publicly available to investors
An amateur needs to pick the suitable funds to be included in their portfolio
The first step was to collect the financial data of the unit trust funds from annual reports
Summary
In Malaysia, different investment securities or schemes are publicly available to investors. 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. Retail investors can select their preferred unit trust funds based on the performance profile of the clusters. 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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