Implementation of Fuzzy Rule Based Technical Indicator in Share Market

Shahjalal, M.; Sultana, Abeda; Mitra, Nirmal Kanti; Khan, Aftab

doi:10.3923/ijaef.2012.53.63

Cited by 3 publications

(1 citation statement)

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“…Several technical indicator signals and rule-based trend signals have been developed. There are fuzzy logicbased approaches that use linguistic variables to exploit technical indicators, such as Ijegwa et al (2014), Shahjalal et al (2012). The main aim of this work is to exploit technical indicator signals in a data mining approach-rather than simply using them as trading signals-to predict the future trend of the financial instrument.…”

Section: Introductionmentioning

confidence: 99%

A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms

2016

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This paper addresses problem of predicting direction and magnitude of movement of currency pairs in the foreign exchange market. The study uses Support Vector Machine with a novel approach for input data and trading strategy. The input data contain technical indicators generated from currency price data (i.e., open, high, low and close prices) and representation of these technical indicators as trend deterministic signals. The input data are also dynamically adapted to each trading day with genetic algorithm. The study incorporates a currency strength-biased trading strategy which selects the best pair to trade from the available set of currencies and is an improvement over the previous work. The accuracy of the prediction models are tested across several different sets of technical indicators and currency pair sets, spanning 5 years of historical data from 2010 to 2015. The experimental results suggest that using trend deterministic technical indicator signals mixed with raw data improves overall performance and dynamically adapting the input data to each trading period results in increased profits. Results also show that using a strength-biased trading strategy among a set of currency pair increases the overall prediction accuracy and profits of the models.

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Section: Introductionmentioning

confidence: 99%

A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms

2016

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Analysis of some indicators by means of fuzzy logic on the example of Azerbaijani energy enterprises

Rzayeva

Guliyeva

Jafarova³

2021

E3S Web Conf.

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One of the most promising areas of scientific research in the field of analysis, forecasting and modelling of economic phenomena and processes is fuzzy logic. In the article with the use of the fuzzy logic apparatus (Fuzzy Logic toolkit of the MATLAB software package) a model for assessing the development of manufacturing indicators is developed. Key indicators for evaluating the activities of energy enterprises are analysed. The model is based on microeconomic indicators of the enterprises’ efficiency and development. The presented indexes pass through the stage of fuzzification, at which each measure is assigned the membership function; then comes the decision stage; finally, the task ends by the stage of defuzzification, i.e., bringing the resulting fuzzy value to precise ones. The application of the Mamdani fuzzy inference algorithm allows getting an exact value of the consolidated indicator of enterprise development. The resulting value allows making operational management decisions in the energy enterprises in real-time.

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Performance evaluation of the distance education system with fuzzy logic

Armağan¹,

Yi̇ği̇t²

2017

AIP Conference Proceedings

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Implementation of Fuzzy Rule Based Technical Indicator in Share Market

Cited by 3 publications

References 1 publication

A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms

A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms

Analysis of some indicators by means of fuzzy logic on the example of Azerbaijani energy enterprises

Performance evaluation of the distance education system with fuzzy logic

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