Using the Committee Machine Method to Forecasting on the FOREX

Nikonov, Oleg I.; Medvedeva, Marina A.; Chernavin, Fedor P.

doi:10.1109/mcsi.2015.59

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…There is also interesting research into committee machine optimization by its confidence in the decision chosen that was conducted by . Considerable reduction of the problem of committee machine construction to the task of linear partial-integer programming was studied by Chernavin and Nikonov [19][20][21][22].…”

Section: Data Analysis and Intelligence Systemsmentioning

confidence: 99%

Application of the committee machine method to analysis of stock market technical indicators

Chernavin¹

2019

Bus. Inform.

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In this article we study problems of the committee machine method when applied to decisionmaking when there are many signals from different technical indicators of a stock exchange market. The committee machine method is a data classification method which can find non-linear data dependencies by construction of several linear classifiers. In the framework of this research, the basis for committee machine construction is a unified partially integer programming model, within which various logics of committee structures can be implemented. The subject of the research is the interrelation of indicators of technical indicators of a stock exchange market with pricing for financial instruments of stock exchange trading. Accordingly, the goal of the research is to show the efficiency of committee structures for solving the problems of forecasting the future value of financial instruments listed on stock exchange markets. To accomplish this goal, basic stock exchange data on Sberbank shares were collected from the Moscow Stock Exchange for the period from 2010 to 2019. On the basis of this, the technical indicators and interrelated parameters were calculated. They were used as data for the committee machine models with different numbers of committee members and voting logics. The result of the calculation was to obtain definitive rules, which when applied in speculative trading on the stock exchange market can generate stable profits. For comparison, we show the solutions of a similar problem by classical classification methods. The comparison shows that methods which work with the non-linear data dependencies provide results in terms of classification quality similar to committee machine results. This research may be interesting to the professional traders, investment analysts, specialists in data science and students with a mathematical and/or financial specialization.

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Section: Data Analysis and Intelligence Systemsmentioning

confidence: 99%

Application of the committee machine method to analysis of stock market technical indicators

Chernavin¹

2019

Bus. Inform.

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“…In this paper, the application of such machine learning methods as "logistic regression" and "neural network" is considered to solve the problem of text classification. Nowadays, these methods are widely used in economic, environmental, medical and other studies [3][4][5][6]. The task is solved on the example of data of the site Kaggle.com (machine learning site: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge, 16.03.2018), which has released recently a competition on the classifying of online comments into six rankings and set a prize for top three contenders.…”

Section: Introductionmentioning

confidence: 99%

Classification of online toxic comments using the logistic regression and neural networks models

Saif

Медведев

Atanasova

2018

AIP Conference Proceedings

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The paper addresses the questions of abusive content identification in the Internet. It is presented the solving of the task of toxic online comments classification, which was issued on the site of machine learning Kaggle (www.Kaggle.com) in March of 2018. Based on the analysis of initial data, four models for solving the task are proposed: logistic regression model and three neural networks models-convolutional neural network (Conv), long shortterm memory (LSTM), and Conv + LSTM. All models are realized as a program in Python 3, which has simple structure and can be adapted to solve other tasks. The results of the classification problem solving with help of proposed models are presented. It is concluded that all models provide successful solving of the task, but the combined model Conv + LSTM is the most effective, so as it provides the best accuracy.

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Analysis and modeling of dynamic changes in the Russian economy macroeconomic indices (2002 - 2016)

Ilysheva

Lapshina

Detkov

et al. 2018

AIP Conference Proceedings

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Using the Committee Machine Method to Forecasting on the FOREX

Cited by 8 publications

References 4 publications

Application of the committee machine method to analysis of stock market technical indicators

Application of the committee machine method to analysis of stock market technical indicators

Classification of online toxic comments using the logistic regression and neural networks models

Analysis and modeling of dynamic changes in the Russian economy macroeconomic indices (2002 - 2016)

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