Recognizing the Pattern of Systematic Risk Based on Financial Ratios and Rough Set-Neural Network System

Systematic risk that is presented by beta is the avoidless risk on the stock market. Beta is calculated by linear analysis between the daily prices of stocks and the security index of stock market. However, many studies have showed there are stronger relationships between beta and financial ratios. In this paper, a hybrid intelligent system is applied to recognize the clusters of beta with financial ratios, combining rough set approach and BP neural network. We can get reduced information table with no information loss by rough set approach. And then, this reduced information is used to develop classification rules and train network to infer appropriate parameters. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and BP neural network for one that dose not match any of them. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach