Determinant Factors of Capital Structure of Firms-An Empirical Analysis Based on Evidence From Chinese Listed Retail Companies

This paper investigates the effectiveness of various factors upon the capital structure decisions of Chinese firms by conducting an empirical analysis of Chinese-listed retail companies.An unbalanced panel dataset was formed with a sample of 110 companies observed for 12 years(2010~2021).Each observation is measured quarterly.Traditional explanatory variables are adopted in the study,including profitability,company size,tangibility of assets,internal financing ability,tax ratio,growth opportunities,and volatility.By employing the Fama-Macbeth approach,the regression results are interpreted to determine the impact of independent variables upon the leverage a company takes on.To solve the reverse causality problem,we include the lag term(last quarter’s data)of the debt-to-equity ratio as control variables.Consistent with previous theoretical and empirical studies,firms’leverage ratio is positively related to size,tangibility,tax ratio,and last quarter’s debt level.Companies’profitability and internal financing ability are negatively correlated with their debt-to-equity ratio.Firms’earning volatility and growth opportunities do not show significant relationship with the debt-to-equity ratio.The study has provided more empirical evidence on capital structure theories regarding emerging financial markets.

Research on text fault recognition for on-board equipment of a C3 train control system based on an integrated XGBoost algorithm

The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train.A fault diagnostic model of on-board equipment is built utilizing the integrated learning XGBoost(eXtreme Gradient Boosting)algorithm to help technicians assess the malfunction category of high-speed train control on-board equipment accurately and rapidly.The XGBoost algorithm iterates multiple decision tree models to improve the accuracy of fault diagnosis by lifting the predicted residual and adding regular terms.To begin,the text features were extracted using the improved TF-IDF(Term Frequency-Inverse Document Frequency)approach,and 24 fault feature words were chosen and converted into weight word vectors.Secondly,considering the imbalanced fault categories in the data set,the ADASYN(Adaptive Synthetic sampling)adaptive synthetically oversampling technique was used to synthesize a few category fault samples.Finally,the data samples were split into training and test sets based on the fault text data of CTCS-3train control on-board equipment recorded by Guangzhou Railway Group maintenance personnel.The XGBoost model was utilized to realize the automatic fault location of the test set after optimized parameter tuning through grid search.Compared with other methods,the evaluation index of the XGBoost model was significantly improved.The diagnostic accuracy reached 95.43%,which verifies the effectiveness of the method in text fault diagnosis.