Decision making on capital markets using nonnumerical model based on qualitative trends

One of the main objectives of this study is to develop a qualitative model that will serve the
decision makers' CFOs (chief financial officers), where, as a rule, it is decided without deeper
processing of information many factors that affect each other significantly. Lack of appropriate
statistical information in connection with turbulently changing environments suggests that further
research is needed to extend existing IPO models based on statistical analyzes. The paper is using basic
qualitative research of trends. A qualitative trend model can be developed under conditions when the
relevant quantitative model must be heavily simplified. The key information input into IPO is expert
knowledge. 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 IPO system under study. The case study
presents a model based on integration of equationless relations using 8 variables e.g. Market condition,
Recognisability or Liquidity risk. There are 17 scenarios S and 41 transitions T among them. All pairs
of relationships are based on trends, either increasing, constant, or decreasing. The key input of the
correct IPO timing analysis is based on the knowledge of experts traced from qualitative heuristics. The
transition graph is a qualitative interpretation of all possible quantitative time series of all variables used
in our IPO timetable and should be used as an effective tool to support CFO decisions.