The main aim of the present study was to investigate the relationship between liquidity, funds resources, and risk-taking in Iran's banking industry with evidence from banks admitted to the Tehran Stock Exchange. The statistical population studied in this research included banks admitted to the Tehran Stock Exchange from 2010 to 2019, selected and tested using systematic sampling. The statistical sample of the research consisted of 24 banks. The research method was one of survey descriptive. As such, correlation analysis method and multivariate regression model were used to test the hypotheses and estimate the model. Also, Excel and SPSS software were used for the tests related to the research hypotheses and EViews was used for the estimation calculations of the current research models. In general, the results of the first regression model test showed that apart from the variables of loan-to-total-asset ratio, equity-to-total-asset ratio, and deposit-to-total-asset ratio, other independent variables of the model have a significant effect on liquidity creation power. The results of the second regression model test revealed that apart from the variables of deposit-to-total-asset ratio and bank profit margin, other independent variables of the model have a significant effect on the bank's risk-weighted asset ratio. The results of the third regression model test showed that apart from the variables of loan-to-total-asset ratio, equity-to-total-asset ratio and deposit-to-total-asset ratio, other independent variables of the model have a significant effect on the bank's overall risk. The test results of the fourth regression model of the research were not available due to the impossibility of empirical investigation and estimation according to the data collected in the selected time period, and no results have been presented for it. The results of the fifth regression model test also indicated that apart from the variables of GDP growth rate and interbank market operations and the ratio of deposit-to-total assets, other independent variables of the model have a significant effect on the standard deviation of stock returns.