Prediction of Economic Crisis Period with Logistic Regression Analysis Based on the Trading Volume of Companies in the Stock Exchange Istanbul

Abstract

The prediction of an economic crisis is the most critical area of study for all actors related to the economy. Crises, a sign of uncertainty, do not have a specific timeline, but they can be predicted by analyzing particular indications. Studies on predicting the crisis are commonly related to macroeconomic variables. This study addresses an alternative approach to predicting crisis periods, which involves analyzing changes in the trading volumes of companies listed on Borsa Istanbul (BIST) instead of relying solely on macroeconomic variables. The study aims to examine the transaction volume data from 169 firms that regularly traded in BIST between 2000 and 2018. The predictability of economic crises in Türkiye has been investigated by applying binary logistic regression analysis, a methodology commonly employed in the literature as a signal approach for detecting economic crises. Some statistically significant parameters were discovered positive, and some were found negative in estimated logistic regression models, and the companies to which the statistically insignificant parameters belonged were evaluated as companies that did not give a signal for the economic crisis model. The findings suggest that changes in the trading volume of many companies, not just a few ones, can be a valuable predictor of crises.