Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data Warehouse: From Architecture to Implementation
Data Warehouse: From Architecture to Implementation
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Using information retrieval techniques for supporting data mining
Data & Knowledge Engineering
Mining market data: a network approach
Computers and Operations Research
Mining stock category association and cluster on Taiwan stock market
Expert Systems with Applications: An International Journal
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Mutual funds are an essential tool for investors looking to diversify their investments. Facing various mutual funds, it is necessary to evaluate their performances. This study uses association rules to understand the relationships among various mutual funds. First, equity funds are categorized into high, medium and low risk levels. This study then evaluates the co-movement among funds within the same risk level and among funds across different risk levels. This study concludes that within any given risk level, the performances of at least seven funds exhibit strong co-movement. This study also shows the influence of the global economy on the correlations among different funds. Finally, investment recommendations are provided based on the findings.