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

Abstract

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.