An Efficient Deep Learning Based Model to Predict Interest Rate Using Twitter Sentiment

Muhammad Yasir, Sitara Afzal, Khalid Latif, Ghulam Mujtaba Chaudhary, Nazish Yameen Malik, Farhan Shahzad and Oh-young Song
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Muhammad Yasir: Department of Management Sciences, COMSATS University Islamabad, Attock Campus 43600, Pakistan
Sitara Afzal: Department of Computer Science, COMSATS University Islamabad, Attock Campus 43600, Pakistan
Ghulam Mujtaba Chaudhary: Department of Business Administration, University of Kotli AJ&K, Kotli,11100, Pakistan
Nazish Yameen Malik: Department of Management Sciences, COMSATS University Islamabad, Attock Campus 43600, Pakistan
Farhan Shahzad: Department of Management Sciences, University of Wah, Wah Cantt, 47040, Pakistan
Oh-young Song: Department of Software, Sejong University, Seoul 05006, Korea

Sustainability, 2020, vol. 12, issue 4, 1-16

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, we deploy a deep learning model for the accurate forecasting of the interest rate for 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 2019 is used for all the mentioned countries. We also incorporate the input of the twitter sentiments of six mega-events, namely the US election 2012, Mexican election 2012, Gaza under attack 2014, Hong Kong protest 2014, Refugee Welcome 2015, and Brexit 2016. Our results provide evidence that the error of the deep learning model significantly decreases when event sentiment is incorporated. A notable improvement has been observed in the case of the Hong Kong interest rate, i.e., a 266% decline in the error after incorporating event sentiments as an input in the deep learning model.

Keywords: Deep learning; Interest rate forecasting; twitter sentiment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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