Stock market investors and traders are making their decisions trying to analyze all relevant factors. Indeed, a good decision should be based on a sufficient number of criteria taking into consideration many company’s fundamentals, its stock characteristics, and external factors. During this process the market participants dealing with selecting sufficient volume of data and converting quantitative and qualitative parameters into valuable information. How to deal with a huge amount of data? How to choose the right data to analyze? What is the best way to process all info inputs?
One of the parts in decision-making chain that is often overlooked is a proper interpretation of data. There are different risks of converting data into valuable information. One of them occurs when some of the factors are exceedingly overvalued and others are neglectfully undervalued and, therefore, this might significantly diminish the quality of output signals for making decision.
One of the solutions is to use a computer program Investment Analyzer InvAn-3/4. It processes automatically all input data converting them into one number, i.e., composite rating. For better analysis, important company-stock characteristics are divided into homogeneous groups as components for further calculation of the composite rating. These components are: fundamental, technical, and timing ratings (FTTR). Finally, the program combines FTTR that allows measuring the quality of a company-stock by a single number.
The composite rating is a result of harmonic averaging of FTTR that initially transformed by Quality Auto-Function (HAQF method) with different weights. Applying HAQF to FTTR allows modeling stock and its company quality very realistically. It enables to reflect accurately the quality of company-stock as a system. In other words, this composition algorithm of the components ensures the following three principles:
- System reliability depends on reliability of its weakest part(s)
- Too good some system’s component(s) cannot mask the rest of components
- The composite result is formed by all components accordingly to each component’s importance
The synthesis of ratios and parameters is performed on the basis of HAQF method that allows modeling company-stock state and dynamics very close to reality. Quality Function (QF) transforms these ratios and parameters (factors) with different critical and sufficient values. The main purpose of Quality Function is to model system quality by normalizing with two characteristic parameters – sufficient and critical. When a certain parameter has its value less than critical, the function approaches to zero. If some parameter exceeds the sufficient value, the function ceases increasing. The following example of non-symmetrical Quality Function (critical value is 30, sufficient value is 250) is to help to understand QF: