会计学院知识库

       With the slowdown of the global economy's growth rate, the domestic real estate industry is facing threefold pressures: shrinking domestic demand, supply shocks, and weakening consumer spending expectations. These pressures continue to evolve in a complex economic landscape, setting higher development standards for real estate enterprises. In September 2023, the financial distress incident of Evergrande Real Estate underscored the need for practitioners to strengthen real-time risk monitoring. In response, it is imperative to establish a scientific and rational financial risk early warning system that enhances risk prevention and control to avert a financial crisis. Nonetheless, current early warning systems still grapple with issues such as high interrelatedness of indicators and incomplete risk source coverage, especially insufficient monitoring of cash flow risks. The unique characteristics of the real estate industry often lead to a heightened risk of capital chain breaches, thus reinforcing the importance of managing and real-time monitoring of real estate firms' cash flows.
       Building on the analyses presented, this study integrates the concept of financial flexibility into the financial risk early warning system for enterprises, highlighting firms' capacity to adapt to changes and uncertainties in a volatile market environment. The construction of the early warning system begins by selecting relevant indicators from four dimensions of flexibility: financing, investing, operating, and profit distribution. Initially, after reviewing related literature and theory, and by examining annual reports and other relevant information, this study explores the current financial risks and early warning status of Cinda Real Estate as a case study, underscoring the need to construct a financial risk early warning system. Subsequently, using Cinda Real Estate's financial data from 2018 to 2022 and adopting the perspective of financial flexibility, the study constructs an indicator system, extracting key indicators using the entropy method and Pearson correlation coefficient analysis. Finally, by applying the improved efficacy coefficient method for comprehensive scoring of indicators, it calculates the total score for each dimension of flexibility and the overall early warning score, classifies the warning intervals, and provides an in-depth analysis of the early warning results.
       This paper aims to construct a financial risk early warning system from different perspectives, beginning with quantifiable financial risk early warning indicators to develop a system suitable for Cinda Real Estate. The goal is to enhance the enterprise's identification of potential risks, improve risk prevention capabilities, and thus promote continuous growth and stable operation of the business. Furthermore, the author hopes that this early warning system will have a certain level of universality within the real estate industry, and that its construction from the perspective of financial flexibility will help more real estate firms discern their own risks and safely navigate through downturns in the real estate market.