Prediction Model for Inclusive Finance Development Considering the Impact of COVID-19: The Case of China

ABSTRACT

Models in previous studies about inclusive finance often include economic data while excludes public online statements. In this paper Random Forest Regression (RFR) model is trained on the annual influencing factors and annual financial inclusion index to predict quarterly financial inclusion index by the quarterly influencing factors to expand the size of data. Then, BOW model tf-idf algorithm is used to convert COVID-19 – loan related online statements into word vectors. Lastly, these influencing factors of different lag periods are passed into the RFR model to compare their performance. Result of models shows that there is impact the epidemic has on the development of inclusive finance, and the lag period of the impact opinion texts on financial inclusion is 2 quarters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.