Applying rough sets to market timing decisions

Shen, Lixiang; Loh, Han Tong

doi:10.1016/s0167-9236(03)00089-7

Cited by 125 publications

(31 citation statements)

References 18 publications

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“…The concept of rough set originates assuming that some information is always associated with every object existing in the universe of discourse. There are various applications where the concept of rough set theory has been applied like finance, banking and investment fields [6,7,8]. Bin-sheng Liu et al proposed traffic flow prediction where rough set and genetic algorithm were applied for the selection of the relevant forecasting variable [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set

Deshpande¹,

Bajaj²

2017

IJCA

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Short term traffic flow prediction has become one of the important research fields in intelligent transportation system. The prediction of this traffic flow information quickly and accurately is important for traffic control and guidance to initiate the measuring steps well in advance. It makes the transport users better informed and makes the transport network smarter, safer and more coordinated. It plays a crucial role in individual dynamic route guidance, advance traffic information system (ATIS) and advance traffic management system (ATMS). This paper discusses the implementation of traffic flow prediction model using support vector machine. Rough set is used as a post processing tool to validate the prediction result. The objective is to improve traffic flow prediction performance. Data near Perungudi toll plaza in IT corridor in Chennai, India is used for the analysis. It is found that the use of rough set results in satisfactory performance improvement which is evaluated using mean square error as the performance measures.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set

Deshpande¹,

Bajaj²

2017

IJCA

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“…In [22] rough sets and classification trees are used, as well. Rough sets are also used in [26]. Support Vector Machines are used in [27].…”

Section: Data Mining Techniques Used With Time Series Data For Short-mentioning

confidence: 99%

Using Data Mining with Time Series Data in Short-Term Stocks Prediction: A Literature Review

Azevedo¹,

Almeida²,

Almeida³

2012

IJIS

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Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on shorttime stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

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“…24 the standard deviation of the difference [23]. Table 7 compares the average return rates of RB(t 0 , I 0 ) over 10 t 0 's with its Sharpe ratios for I 0 = 1, .…”

Section: Phase 3: Rule Base Constructionmentioning

confidence: 99%

“…Rough set analysis is known to be quite valuable for extracting trading rules from huge data such as real-time data since it can be used to discover dependences in the data while reducing the effect of superfluous factors in the data [24]. Through empirical studies, we will show that rough set analysis could yield an efficient RRTS against various situations of futures market.…”

Section: Introductionmentioning

confidence: 96%

Using rough set to support investment strategies of real-time trading in futures market

Lee

Ahn

et al. 2008

Appl Intell

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Finding proper investment strategies in futures market has been a hot issue to everyone involved in major financial markets around the world. However, it is a very difficult problem because of intrinsic unpredictability of the market. What makes things more complicated is the advent of real-time trading due to recent striking advancement of electronic communication technology. The real-time data imposes many difficult tasks to futures market analyst since it provides too much information to be analyzed for an instant. Thus it is inevitable for an analyst to resort to a rule-based trading system for making profits, which is usually done by the help of diverse technical indicators. In this study, we propose using rough set to develop an efficient real-time rulebased trading system (RRTS). In fact, we propose a procedure for building RRTS which is based on rough set analysis of technical indicators. We examine its profitability through an empirical study.

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Applying rough sets to market timing decisions

Cited by 125 publications

References 18 publications

Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set

Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set

Using Data Mining with Time Series Data in Short-Term Stocks Prediction: A Literature Review

Using rough set to support investment strategies of real-time trading in futures market

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