Economic Crisis Early Warning of Real Estate Companies Based on PSO-Optimized SVM

Abstract

For the possible economic crisis of real estate companies, by analyzing the shortcomings of support vector machine (SVM) model, after optimization with particle swarm optimization (PSO), the PSO-SVM model was established by changing economic condition parameters and using data to warn the real estate economic crisis. Then, this model is used to warn the economic capacity of four real estate companies in Beijing. The results show that this model can further predict and analyze the solvency index, operating ability index, development ability index, profitability index, cash flow index, and comprehensive financial index of real estate companies and come to the conclusion that profitability index is the most important, and higher profitability can resist the harm brought by economic crisis. Finally, it analyzes the economic crisis that real estate companies may encounter and puts forward specific suggestions.

Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining

“…The feature extraction module is mainly composed of continuous convolution and pooling layers, and the processing (prediction) module is mainly composed of fully connected layers. However, a major disadvantage of CNN is overfitting and local overoptimization in which overfitting refers to the analysis results that are too closely or precisely corresponding to a particular data set and therefore may not be able to fit other data or reliably predict future observations [ 14 ]. Then the nonlinear SVM model can effectively avoid the disadvantages of CNN by introducing relaxation variables and finally get the optimal solution.…”

Integrated Evaluation of Financial Risk in Coal Industry Restructuring: A Model of Linear Regression and PCA

“…e six independent variables in formula (1) can be calculated according to the financial statement data of listed companies. Financial crisis index W has no actual corresponding index in reality, so it is necessary to specify an alternative index [26][27][28][29][30][31]. After multiple weighing, the logistic function of the quick ratio is selected as the approximate alternative index of W, which is…”