Authors:
Yuh-Long Hsieh
and
Don-Lin Yang
Affiliation:
Feng Chia University, Taiwan
Keyword(s):
Financial data mining, Association rule, Inter-transaction, Profit mining, Risk, Win rate, Trading simulation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Data Mining in Electronic Commerce
;
Foundations of Knowledge Discovery in Databases
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
In the past decade, association rule mining has been used extensively to discover interesting rules from large databases. However, most of the produced results do not satisfy investors in the financial market. The reason for this is because association rule mining simply uses confidence and support to select interesting patterns while the investor is more interested in the result- trading at high profit and low risk. We propose a novel approach called Profit Mining which provides investors with trading rules including information about profit, risk, and win rate. To show the feasibility and usefulness of our proposal, we use a simple trading model of an inter-day trading simulation. This mining approach works well not only in the stock market, but also in the futures and other markets.