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Sentiment-Driven Exchange Rate Forecasting: Integrating Twitter Analysis with Economic Indicators

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  • Kazım Berk Küçüklerli
  • Veysel Ulusoy

Abstract

This study focuses on predicting the USD/TL exchange rate by integrating sentiment analysis from Twitter with traditional economic indicators. With the dynamic nature of global finance, accurate exchange rate forecasting is crucial for financial planning and risk management. While economic indicators have traditionally been used for this purpose, the increasing influence of public sentiment, particularly on digital platforms like Twitter, has prompted the exploration of sentiment analysis as a complementary tool. Our research aims to evaluate the effectiveness of combining sentiment analysis with economic indicators in predicting the USD/TL exchange rate. We employ machine learning techniques, including LSTM Neural Network, xgboost, and RNN, to analyze Twitter data containing keywords related to the Turkish economy alongside TL/USD exchange rate data. Our findings demonstrate that integrating sentiment analysis from Twitter enhances the predictive accuracy of exchange rate movements. This study contributes to the evolving landscape of financial forecasting by highlighting the significance of sentiment analysis in exchange rate prediction and providing insights into its potential applications in financial decision-making processes. Â JEL classification numbers: C53, F31, E60.

Suggested Citation

  • Kazım Berk Küçüklerli & Veysel Ulusoy, 2024. "Sentiment-Driven Exchange Rate Forecasting: Integrating Twitter Analysis with Economic Indicators," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(3), pages 1-4.
    • Handle: RePEc:spt:apfiba:v:14:y:2024:i:3:f:14_3_4
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    References listed on IDEAS

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    Cited by:

    1. Hongcheng Ding & Xuanze Zhao & Zixiao Jiang & Shamsul Nahar Abdullah & Deshinta Arrova Dewi, 2024. "EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods," Papers 2408.13214, arXiv.org.

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    More about this item

    Keywords

    Twitter narratives; LSTM; XGBoost; RNN; USD/TL FX rate; Narrative economics.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General

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