Fuzzy Inference-Enhanced VC-DRSA Model for Technical Analysis: Investment Decision Aid

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Title:	Fuzzy Inference-Enhanced VC-DRSA Model for Technical Analysis: Investment Decision Aid
Authors:	Shen, Kao-Yi Tzeng, Gwo-Hshiung
Contributors:	Dept Banking & Finance
Keywords:	Fuzzy inference system (FIS) Technical analysis (TA) Investment decision Knowledge retrieval Rough set theory (RST) Variable consistency dominance-based rough set approach (VC-DRSA) Soft computing
Date:	2015-09
Issue Date:	2015-10-19 11:17:01 (UTC+8)
Abstract:	To support investment decision based on technical analysis (TA), this study aims to retrieve the knowledge or rules of various indicators by a hybrid soft computing model. Although the validity of TA has been examined extensively by various statistical methods in literature, previous studies mainly explored the effectiveness of each technical indicator separately; therefore, a practical approach that may consider the inconsistency of various technical indicators simultaneously and the down-side risk of an investment decision is still underexplored. Thus, a hybrid model-by constructing a variable consistency dominance-based rough set approach (VC-DRSA) information system with the fuzzy inference-enhanced discretization of signals-is proposed, to retrieve the imprecise patterns from commonly adopted technical indicators. At the first stage, the trading signals (i.e., buy, neutral, or sell) are preprocessed in two groups: straight-forward signals and complicated signals. The straight-forward technical indicators (i.e., for signals that are decided by precise rules) are suggested by domain experts, and the buy-in signals are simulated by several trading strategies to examine the outcomes of each indicator. As for those complicated signals (i.e., for signals that require imprecise judgments with perceived feeling of domain experts to identify patterns), a fuzzy inference technique is incorporated to enhance the discretization of signals; those signals are also simulated by the aforementioned trading strategies to obtain the corresponding results. At the second stage, the trading signals generated by each technical indicator and their pertinent results from the previous stage are combined for VC-DRSA modeling to gain decision rules. To illustrate the proposed model, the weighted average index of the Taiwan stock market was examined from mid/2002 to mid/2014, and a set of decision rules with nearly 80 % classification accuracy (both in the training and the testing sets) were obtained in this empirical case. The findings suggest that several technical indicators should be considered simultaneously, and the retrieved rules (knowledge) have practical implications for investors.
Relation:	INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 卷: 17 期: 3 頁碼: 375-389
Appears in Collections:	[Department of Banking & Finance ] periodical articles

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