Decision Support System Based on Fuzzy Logic for Assessment of Expected Corporate Income Performance

被引:1
作者
Yosef, Arthur [1 ]
Shnaider, Eli
Palas, Rimona [2 ]
Baranes, Amos [3 ]
机构
[1] Tel Aviv Yaffo Acad Coll, Tel Aviv, Israel
[2] Coll Law & Business, Ramat Gan, Israel
[3] Peres Acad Ctr, Rehovot, Israel
关键词
Modeling corporate earnings; financial ratios; corporate performance evaluation; decision support; fuzzy logic; BANKRUPTCY PREDICTION; EXPLANATORY VARIABLES; RELATIVE IMPORTANCE; STOCK;
D O I
10.1142/S1469026821500097
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study presents a decision-support method to estimate the next year performance of corporate Operating Income Margin (OIM). It is based on a unique combination of cross-section model and the rules-based evaluation mechanism. The estimate is done in terms of broad categories, and not precise numerical values. The model is constructed as follows: its dependent variable (OIM) is one year ahead vs. the corresponding explanatory variables. This structure of the model allows us to view explanatory variables as reflecting financial potential of corporations. The evaluation component consists of a set of rules designed to identify the companies whose "potential" clearly points to an opportunity to invest. For the method presented here to succeed, it is necessary to utilize a highly reliable modeling method, even if it is "Fuzzy". We apply Soft Regression (SR), which is a Soft Computing modeling tool based on Fuzzy Logic, and utilize all available proxy variables by creating intervals of values. Advantages of utilizing SR, and the intervals'-based modeling are extensively discussed. Modeling results for five consecutive years are consistent and stable, thus indicating high degree of reliability. Testing indicates very high success rate for the stock market related domain, the lowest being 87.9%.
引用
收藏
页数:29
相关论文
共 36 条
[1]  
[Anonymous], 2015, PIONEER J THEOR APPL
[2]  
[Anonymous], 2018, FUZZY EC REV
[3]   Bankruptcy prediction for credit risk using neural networks: A survey and new results [J].
Atiya, AF .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2001, 12 (04) :929-935
[4]  
ATTIGERI GV, 2015, TENCON 2015 2015 IEE, P1
[5]  
Baranes A., 2019, Journal of Management Information and Decision Sciences, V22, P36
[6]  
Bernard V., 1997, Contemp. Account. Res., V14, P89
[7]  
Bird R., 2001, Journal of Asset Management, V2, P180
[8]  
Cakra YE, 2015, INT C ADV COMP SCI I, P147, DOI 10.1109/ICACSIS.2015.7415179
[9]  
Chandwani D., 2014, International Journal of Computer Application, V92, P8, DOI 10.5120/16051-5202
[10]   AN EMPIRICAL-ANALYSIS OF USEFUL FINANCIAL RATIOS [J].
CHEN, KH ;
SHIMERDA, TA .
FINANCIAL MANAGEMENT, 1981, 10 (01) :51-60