Developing a Hybrid Model for Predicting Financial Performance of Iranian Construction Companies Based on Genetic Algorithm and Adaptive Neuro-Fuzzy Inference System

Analyzing financial ratios over consecutive years is beneficial for evaluating the financial performance of construction companies. However, such an analysis can be tedious due to the vast number of the ratios. Therefore, developing an expert system based on artificial intelligence algorithms to identify and predict factors influencing the construction companies' financial performance is essential. To this end, a hybrid model based on Genetic Algorithm (GA) and Adaptive Neuro-Fuzzy Inference System (ANFIS) was introduced in this research to predict the financial performance of construction companies in Iran. This research is applied as descriptive and in terms of methodology well developed; also conducted cross-sectionally. The statistical population included all active construction companies in the construction sector in Tehran. Due to time and resource constraints, a random sampling technique was used. A questionnaire was utilized for data collection and data analysis, factor analysis methods and neuro-fuzzy system combined with GA were employed. The ANFIS combined with GA can evaluate the construction companies' financial performance with the minimum error. The findings ultimately resulted development of a model that forecasts the financial performance of Iranian construction companies, allowing them to concentrate on factors that improve financial performance.