Towards Better USD/PKR Exchange Rate Forecasting Using Public Sentiment Analysis

Abstract

Exchange rate forecasting plays a crucial role in financial planning, investment decisions, and economic policy formulation. Traditional forecasting models primarily rely on historical financial data and macroeconomic indicators; however, these models often fail to capture the influence of public perception and market sentiment on currency movements. This study aims to improve the forecasting accuracy of the USD/PKR exchange rate by incorporating public sentiment analysis derived from online sources such as social media platforms, news articles, and financial forums.

The research collects historical exchange rate data along with large-scale textual data reflecting public opinions related to economic conditions and currency fluctuations. Natural Language Processing (NLP) techniques are applied to preprocess the textual data and perform sentiment analysis, categorizing the information into positive, negative, and neutral sentiments. These sentiment indicators are then integrated with traditional time-series forecasting models and machine learning techniques to predict future movements of the USD/PKR exchange rate.