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|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|16725||2012||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 8, 15 June 2012, Pages 7458–7470
Investors in futures market used to employ trading system which depends on reference pattern (template) to detect real-time buy or sell signal from the market. Indeed they prepare in advance a number of reference patterns that market movement might follow, and then match the current market with one of reference patterns. One popular way to prepare templates is to fix a relatively small number of them which represent possible market movements efficiently. The underlying assumption of this approach is of course that the current market movement is close enough to one of the templates. However, there is always a calculated risk that the current market is close to none of them sufficiently. In this article we investigate the issue of appropriate number of templates (or template cardinality I) in terms of profitability. We will show that one may improve profitability by increasing I and that random pattern sampling plays a key role in such case. An empirical study is done on the Korean futures market.
Developing a more accurate and more profitable investment strategy for the futures market equipped with real-time data has been a challenging issue among investors and professional analysts (Gay and Jung, 1999, Veld-Merkoulova, 2003 and Wang and Chan, 2007). Challenge is that real-time data add versatility as well as unpredictability to the futures market (Dhar, Chou, & Provost, 2000) and thus it is hard to build consistently profitable strategies. Quite a few researchers use artificial intelligence expert systems to develop trading system based on a relatively small set of templates (or reference patterns) characterizing the stock market movements (Chang et al., 2004, Choudhry and Garg, 2008, Chung et al., 2004, Kim and Shin, 2007, Li and Kuo, 2008 and Oh and Kim, 2002) but their performances usually turn out to be rather limited when they are applied to the futures market in practice. Major hindrance to their approach seems that frequently their templates go awry or fail to match the current market accurately enough. One obvious solution to this hindrance is to enhance template variety by increasing template cardinality I (or the number of reference patterns prepared). This study aims to implement and investigate such idea. In particular we employ random pattern sampling to enhance template variety with an increased I. Our study reveals relation of template cardinality and trading system profitability. In fact we will show that one may improve profitability by increasing I, but such improvement might become relatively stable for I too large. For our study, we develop a procedure called expandedreal-time rule-based trading system (eRRTS) which expands the set of templates of RRTS (real-time rule-based trading system) developed by Lee, Ahn, Oh, and Kim (2010). Notice that RRTS (and hence eRRTS) attaches trading rule to each reference pattern and then activates the corresponding trading rule when it finds a reference pattern close to the current movement. Lee et al. (2010) argues that a relatively small number of templates for RRTS is unavoidable since with enormous amount of real-time data it is usually hard to figure out the current pattern from the entire past patterns. A major technical reformation of RRTS to eRRTS is that it enhances template variety with an increased I. The rest of the paper is organized as follows. Section 2 briefly reviews the technical background of this research and Section 3 describes the eRRTS construction procedure. In Section 4 empirical studies are discussed to illustrate the eRRTS construction procedure and address our proposition. Concluding remarks are presented in Section 5.