Non-numerical Bankruptcy Forecasting Based on Three Trends Values - Increasing, Constant, Decreasing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F24%3APU150984" target="_blank" >RIV/00216305:26510/24:PU150984 - isvavai.cz</a>

Výsledek na webu

<a href="https://mnje.com/sites/mnje.com/files/currentissue/Komplet%20MNJE%20Vol.%2020,%20No.%202.pdf" target="_blank" >https://mnje.com/sites/mnje.com/files/currentissue/Komplet%20MNJE%20Vol.%2020,%20No.%202.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14254/1800-5845/2024.20-2.11" target="_blank" >10.14254/1800-5845/2024.20-2.11</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Non-numerical Bankruptcy Forecasting Based on Three Trends Values - Increasing, Constant, Decreasing

Popis výsledku v původním jazyce

There is a broad spectrum of different BM (Bankruptcy Models). However, complex bankruptcies are unique, vaguely known, interdisciplinary and multidimensional. These are the key reasons why sufficiently large sets of examples are not available It is therefore often prohibitively difficult to make forecasts using numerical quantifiers and traditional statistical methods. BMs development suffer from IS (Information Shortage). IS eliminates straightforward application of traditional statistical methods based on information rich environment; that is on the law of large numbers. Artificial Intelligence has developed different tools to minimise IS related problems. Trend reasoning is one of them. It is based on the least information intensive quantifiers There are four different trends i.e. qualitative values and their derivatives: plus/increasing; zero/constant; negative/decreasing; any value / any trend. The paper studies BMs represented by models based on EHE (Equationless Heuristics). An bankruptcy example of EHE is - If Selling of Assets is increasing then Satisfaction of Creditors is increasing. Such verbal knowledge items cannot be incorporated into a traditional numerical model. No quantitative quantifiers, e.g. numbers, are used in this paper. The solution of a trend model M(X) is a set S of scenarios where X is the set of n variables quantified by the trends. All possible transitions among the scenarios S are generated. An oriented transitional graph G has as nodes the set of scenarios S and as arcs the transitions T. An oriented G path describes any possible future and past time behaviour of the bankruptcy system under study. The G graph represents the complete list of forecasts based on trends. An eight -dimensional model serves as a case study. Difficult to measure variables are used, e.g. Level of Greed, Political Influence. There are 65 scenarios S and 706 transitions T among them. A priory knowledge of trend reasoning is not required.

Název v anglickém jazyce

Non-numerical Bankruptcy Forecasting Based on Three Trends Values - Increasing, Constant, Decreasing

Popis výsledku anglicky

There is a broad spectrum of different BM (Bankruptcy Models). However, complex bankruptcies are unique, vaguely known, interdisciplinary and multidimensional. These are the key reasons why sufficiently large sets of examples are not available It is therefore often prohibitively difficult to make forecasts using numerical quantifiers and traditional statistical methods. BMs development suffer from IS (Information Shortage). IS eliminates straightforward application of traditional statistical methods based on information rich environment; that is on the law of large numbers. Artificial Intelligence has developed different tools to minimise IS related problems. Trend reasoning is one of them. It is based on the least information intensive quantifiers There are four different trends i.e. qualitative values and their derivatives: plus/increasing; zero/constant; negative/decreasing; any value / any trend. The paper studies BMs represented by models based on EHE (Equationless Heuristics). An bankruptcy example of EHE is - If Selling of Assets is increasing then Satisfaction of Creditors is increasing. Such verbal knowledge items cannot be incorporated into a traditional numerical model. No quantitative quantifiers, e.g. numbers, are used in this paper. The solution of a trend model M(X) is a set S of scenarios where X is the set of n variables quantified by the trends. All possible transitions among the scenarios S are generated. An oriented transitional graph G has as nodes the set of scenarios S and as arcs the transitions T. An oriented G path describes any possible future and past time behaviour of the bankruptcy system under study. The G graph represents the complete list of forecasts based on trends. An eight -dimensional model serves as a case study. Difficult to measure variables are used, e.g. Level of Greed, Political Influence. There are 65 scenarios S and 706 transitions T among them. A priory knowledge of trend reasoning is not required.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Montenegrin Journal of Economics

ISSN

1800-6698

e-ISSN

—

Svazek periodika

20

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

ME - Černá Hora

Počet stran výsledku

14

Strana od-do

131-144

Kód UT WoS článku

001208306400011

EID výsledku v databázi Scopus

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