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Construction of Development Momentum Index of Financial Technology by Principal Component Analysis in the Era of Digital Economy

机译:数字经济时代金融科技发展动能指数主成分分析

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摘要

The purpose is to study applying mathematical analysis in financial technology (FinTech) development in the era of digital economy. An Evaluation Index System (EIS) for the current situation of Chinese FinTech enterprises is established by considering the impact of the era of the digital economy on the development of FinTech. Specifically, the Principal Component Analysis (PCA) is introduced to construct the principal component prediction model based on functional data. Then, six Chinese State-owned Enterprises (SOEs) are selected. Their stock prices are predicted using the proposed model through an empirical study. The results show that selecting three principal components to evaluate the financial situations of six SOEs is reasonable. The accumulated variance values of the first three principal components of the stock's closing price and opening price are all greater than 85. Thus, the selected three principal components can obtain the potential information of the original data. The gap between the actual value and the proposed model-predicted value of the stocks of the six SOEs is relatively small. The Root Mean Square Error (RMSE) of China National Petroleum Corporation (CNPC) is 0.105, more than 10. The predicted values of Huadian Energy and China Shenhua are 9.4 and 8.5, respectively, second only to CNPC. Therefore, the proposed principal component prediction model based on functional data can predict the closing price of stocks well. The accuracy is relatively high and matches well with financial data analysis. This research has important implications for the development of FinTech.
机译:目的是研究在数字经济时代金融科技(FinTech)发展中应用数学分析。结合数字经济时代对金融科技发展的影响,建立了中国金融科技企业现状评价指标体系(EIS)。具体而言,引入主成分分析(PCA)构建基于函数数据的主成分预测模型。然后,选出六家中国国有企业。通过实证研究,使用所提出的模型预测他们的股票价格。结果表明,选择3个主成分对6家国有企业财务状况进行评价是合理的。股票收盘价和开盘价前三个主成分的累计方差值均大于85%。因此,所选的三个主成分可以获得原始数据的潜在信息。6家国有企业股票的实际值与模型预测值的差距相对较小。中国石油天然气集团公司(CNPC)的均方根误差(RMSE)为0.105,超过10%。华电能源和中国神华的预测值分别为9.4%和8.5%,仅次于中石油。因此,所提出的基于函数数据的主成分预测模型能够较好地预测股票的收盘价。准确率相对较高,与财务数据分析匹配性好。本研究对金融科技的发展具有重要意义。

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