Predicting he Interbank Capital Adequacy Level Based n Financial Data Analysis

• Autores: Yaojun Du, Fangjun Wang, Liping Shen
• Localización: Intelligent environments 2019: Workshop Proceedings of the 15th International Conference on Intelligent Environments / coord. por Andrés Muñoz Ortega, Sofia Ouhbi, Wolfgang Minker, Loubna Echabbi, Miguel Navarro Cía, 2019, ISBN 9781614999836, págs. 36-45
• Idioma: inglés
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• Resumen

  • The adequacy of interbank capital is affected by many factors, includingthe tightness of the entire financial market and the ups-and-downs of interest rates.It has great significance for commercial banks and other non-bank institutions todiagnose the disturbance factors of interbank capital, and predict future capital ad-equacy level in advance to deploy appropriate countermeasures accordingly. Thispaper attempts to analyze the relevant factors affecting the interbank capital andto predict the adequacy level of interbank capital based on structured and unstruc-tured financial data. For unstructured data, we crawl the texts from Sina FinancialNews and then make pre-processing, including word segmentation, emotional wordextraction and word-to-vector transformation. For structured data the preprocess-ing includes padding missing value, data normalization, feature selection and da-ta dimensionality reduction. The prediction models we tried include GBDT, XG-Boost, LSTM, SVM, and Perceptron. Experiments show that two-category (looseand tight) average accuracy of the overall adequacy level of interbank capital canachieve more than 94.5%.