Interest Rate Analyzer Using Deep Learning Model

EasyChair Preprint 10005

Abstract

In macroeconomics, decision-making is highly sensitive and significantly influences the financial and business world, where the interest rate is a crucial factor. In addition, the interest rate is used by the governments to manage the monetary policy. There is a need to design an efficient algorithm for interest rate prediction. The analysis of the social media sentiment impact on financial decision-making is also an open research area. In this study, user deploy a deep learning model for the accurate forecasting of the interest rate for  India, the UK, Turkey, China, Hong Kong, and Mexico. For this purpose, daily data of the interest rate and exchange rate covering the period from Jan 2010 to Oct 2021 is used for all the mentioned countries. It also incorporate the input of the Twitter sentiments of six mega-events, namely the Indian election 2021, the US election 2012, the Mexican election 2012, Gaza under attack 2014, the Hong Kong protest 2014, the Refugee Welcome 2015, and Brexit 2016. The results will provide evidence that the error of the deep learning model significantly decreases when event sentiment is incorporated. The Gaza under attack 2014, the Hong Kong protest 2014, the Refugee Welcome 2015, and Brexit 2016. The results will provide evidence that the error of the deep learning model significantly decreases when event sentiment is incorporated.

Keyphrases: CNN, SVM, deep learning, logistic regression, machine learning

@booklet{EasyChair:10005,
  author    = {T Hemanthkumar and M Kavitha},
  title     = {Interest Rate Analyzer Using Deep Learning Model},
  howpublished = {EasyChair Preprint 10005},
  year      = {EasyChair, 2023}}