Fuzzy prediction based on regression models: Evidence from the Belgrade Stock Exchange - prime market and graphic industry

In the conventional regression model, deviations between the observed values and the estimated values are supposed to be due to measurement errors. Here, taking a different perspective, these deviations are regarded as the fuzziness of the system's parameters. Thus, these deviations are reflected in a linear function with fuzzy parameters. Using linear programming algorithm, this fuzzy linear regression model might be very convenient and useful for finding a fuzzy structure in an evaluation system. In this paper, the details of the fuzzy linear regression concept and its applications in an uncertain environment are shown and discussed on data of the Belgrade Stock Exchange. In addition, the prediction of stock market prices is performed using fuzzy linear trend. Having in mind the characteristics of trading methods, fuzzy linear trend is used for prediction of stock market prices based on historical data, which are not precisely given within a trading day. Results of the research indicate the significance of fuzzy prediction based on regression models, i.e. fuzzy linear trend.