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http://hdl.handle.net/11718/27878
Title: | Credit to GDP ratio: to examine whether technological innovation had impact on credit-to-GDP ratio through cross-country evidence |
Authors: | Deepika, Azmeera |
Keywords: | Financial technology - Economic aspect;Digital finance - Statistical methods;Credit - Gross domestic product - Econometric models |
Issue Date: | 1-Jan-2023 |
Publisher: | Indian Institute of Management Ahmedabad |
Abstract: | In an increasingly interconnected and technology-driven world, digitalization and financial technology (fintech) have significantly shaped economies. This project aimed to examine the impact of digitalization and fintech on the credit-to-GDP ratio of the country, a crucial indicator of financial stability and economic progress. The project employed regression analysis to link digitalization, fintech indicators, and credit-to-GDP ratios in a sample of economies. The results revealed essential insights illuminating the intricate interplay between technology and the credit-to-GDP ratio. The model fit measures provided valuable information about the overall effectiveness of the regression model. The coefficient of determination (R-squared) indicated that approximately 36.9% of the variability in the credit ratio could be explained through the predictor variables, namely: Individuals using the internet (% of population) as a proxy for technology adoption, Digital transaction value (in billion USD) as a proxy for fintech adoption by the country, and ATM availability (per 100,000 adults) as a proxy for the digitalization of the banking system. The adjusted R-squared of 0.352 considered the trade-off between model complexity and explanatory power, suggesting that about 35.2% of the variability could be explained while accounting for the number of predictors. |
Description: | In an increasingly interconnected and technology-driven world, digitalization and financial technology (fintech) have significantly shaped economies. This project aimed to examine the impact of digitalization and fintech on the credit-to-GDP ratio of the country, a crucial indicator of financial stability and economic progress. The project employed regression analysis to link digitalization, fintech indicators, and credit-to-GDP ratios in a sample of economies. The results revealed essential insights illuminating the intricate interplay between technology and the credit-to-GDP ratio. The model fit measures provided valuable information about the overall effectiveness of the regression model. The coefficient of determination (R-squared) indicated that approximately 36.9% of the variability in the credit ratio could be explained through the predictor variables, namely: Individuals using the internet (% of population) as a proxy for technology adoption, Digital transaction value (in billion USD) as a proxy for fintech adoption by the country, and ATM availability (per 100,000 adults) as a proxy for the digitalization of the banking system. The adjusted R-squared of 0.352 considered the trade-off between model complexity and explanatory power, suggesting that about 35.2% of the variability could be explained while accounting for the number of predictors. |
URI: | http://hdl.handle.net/11718/27878 |
Appears in Collections: | Student Projects |
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