This study investigates the efficacy of the Prophet model by Facebook with respect to forecasting bank capital ratios. Bank financial ratios and macroeconomic information are combined to forecast total risk-adjusted capital ratios for 19 large U.S. banks. Using a sample period from March 2005 to December 2020, in-sample results show that the model accurately estimates bank capital ratios over time. As validation, out-of-sample tests indicate that forecasting errors are smaller for Prophet models compared to benchmark ARIMAX models. Based on these and other results, we conclude that the Prophet model does a good job of forecasting bank capital ratios. By implication, it provides a practical forecasting tool for bank regulatory supervisors, management, and investors. 

/pdf/forecasting-bank-capital-ratios-using-the-prophet-model-by-3aclry8r.pdf

Forecasting Bank Capital Ratios Using the Prophet Model by Facebook

This study investigates the efficacy of the Prophet model by Facebook with respect to forecasting bank capital ratios. Bank financial ratios and macroeconomic information are combined to forecast total risk-adjusted capital ratios for 19 large U.S. banks. Using a sample period from March 2005 to December 2020, in-sample results show that the model accurately estimates bank capital ratios over time. As validation, out-of-sample tests indicate that forecasting errors are smaller for Prophet models compared to benchmark ARIMAX models.  Based on these and other results, we conclude that the Prophet model does a good job of forecasting bank capital ratios. By implication, it provides a practical forecasting tool for bank regulatory supervisors, management, and investors.

This paper explains the scalable methods used for extracting and analyzing the Covid-19 vaccine data. Using Big Data such as Hadoop and Hive, we collect and analyze the massive data set of the confirmed, the fatality, and the vaccination data set of Covid-19. The data size is about 3.2 Giga-Byte. We show that it is possible to store and process massive data with Big Data. The paper proceeds tempo-spatial analysis, and visual maps, charts, and pie charts visualize the result of the investigation. We illustrate that the more vaccinated, the fewer the confirmed cases.

Scalable Analysis for Covid-19 and Vaccine Data.

COVID 19 is an acute disease that started spreading throughout the world, beginning in December 2019. It has spread worldwide and has affected more than 7 million people, and 200 thousand people have died due to this infection as of Oct 2020. In this paper, we have forecasted the number of deaths and the confirmed cases in Los Angeles and New York of the United States using the traditional and Big Data platforms based on the Times Series: ARIMA and ETS. We also implemented a more sophisticated time-series forecast model using Facebook Prophet API. Furthermore, we developed the classification models: Logistic Regression and Random Forest regression to show that the Weather does not affect the number of the confirmed cases. The models are built and run in legacy systems (Azure ML Studio) and Big Data systems (Oracle Cloud and Databricks). Besides, we present the accuracy of the models.

Scalable Predictive Time-Series Analysis of COVID-19: Cases and Fatalities

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Scalable Predictive Time-Series Analysis of COVID-19: Cases and Fatalities