Application of New A Priori Algorithm MDNC to Exchange Traded Fund

How to utilize a variety of derivatives so as to obtain positive investment return in different economies concerns the general investors. The present research utilizes a modified APRIORI algorithm called Multi-Dimension Non-Continuous (MDNC), an algorithm by eliminating the limitations imposed by traditional pattern matching of continuous data, to mine the associated rules in the cross-day discrete trading data and find out some valuable information. This paper further capitalizes on low cost and tax and trading flexibility characteristics of ETF along with a successful and effective data mining methodology to develop a day-trade strategy with high probability of positive investment return. The current approach outperforms Random Walk Transaction strategy with superior investment return and lower risk level, as evidenced by a 95-percent confidence interval. In other words, the investment strategy proposed by the present research is applicable in any economy situation for positive investment return.