Certain investigations on fuzzy inference systems and fuzzy mcdm methods for stock trading and performance analysis

Abstract

In the financial market context, the decision-making process in stock newlinetrading is a more complicated and nonlinear dynamic system. As a result, newlinemultiple studies gave rise to various decision support systems to provide traders newlinewith optimal buy and sell signals. In this context, technical analysis seeks to newlineforecast future prices by exploiting the securities historical price and volume newlinedata. Yet, the effectiveness of technical analysis is heavily dependent on the newlineability of stock traders to comprehend trading signals. As a result, human newlineknowledge and experience are essential for traders to spot price trend reversal newlinesignals to make buy or sell decisions. Mastering in technical analysis is newlinea time-consuming process and requires a hard effort. Hence, to ease the newlinetradersâAand#728; Z efforts, a fuzzy inference system (FIS) was proposed in the earlier ´ newlineliterature. A FIS has the capacity to incorporate human experience into trading newlinealgorithms and is used to forecast future market price changes as well as timing newlinethe market. To assess alternatives based on their many criteria, multi-criteria newlinedecision-making (MCDM) and hybrid MCDM procedures are extensively newlineemployed. In general, MCDM selects the optimal choice by considering both newlinequalitative and quantitative information. In many cases, decision-makers will newlinechoose linguistic phrases when formulating the stock selection problem. The newlinefuzzy set theory has been frequently utilized to deal with these issues. newline