stepwise regression

Analysts have found that securities prices do not change randomly. Rather, change is based on a reproducible and recognizable process. Investors are faced with a decision-making process when selecting and buying corporate stocks. In this process, they seek to select stocks that have the maximum return for them and are also liquid. Liquidity in the stock market is very important. Identifying the factors affecting liquidity helps to predict the stock liquidity situation and thus stock risk management. The purpose of this study is to find the factors affecting the liquidity of stocks. For this purpose, in the first stage, using the research literature and experts, the effective factors are identified and using the stepwise regression method, the effective variables are selected. Variable selection (feature selection) is an important step in model construction for prediction. A good feature selection algorithm should always provide benefits such as better data display, better classification model, increased generalization, and identification of irrelevant features. Also, reducing the number of variables to understand the relationship between features and target variables, reducing computational load and increasing accuracy in high-dimensional data sets where the number of observations is less than the number of features, improves predictor performance and increases efficiency in terms of cost and time. Finally, the extracted variables were selected using the Step Wise regression model, including firm size, risk-taking, return on equity, and free float ratio.