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The IUP Journal of Financial Risk Management
Exchange Rate Forecast Enhancement Using Sentiments from Google Trends
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Does incorporation of sentiments from credit, financial and price markets add to the forecasting abilities of the models using fundamental factors in exchange rate forecasting? The present study attempts to answer this question by introducing a different method of exchange rate forecasting by considering sentiments from Google Trends along with macroeconomic fundamentals. It attempts to increase the predictive powers of foreign exchange forecasting models based on macroeconomic observable fundamental factors by incorporating the sentiments from the above-mentioned three markets. This work is based on Principal Component Analysis (PCA) and Vector Autoregression (VAR). The study has extracted sentiments by preserving 90% cumulative variance in principle components from each of these three markets. Further, it estimated VARs with and without sentiments. It is observed that the estimates of VAR model with sentiments provide better results as compared to fundamental VAR model. Finally, this study concludes that sentiments can enhance predictive content of foreign exchange rate along with macroeconomic variables.

 
 
 

Prediction of the prices of financial securities has always been an area of interest for both academia and the industry because it brings with it the ability to generate gains and hedge against losses. One such market which has been the cynosure among the investment community is the foreign exchange market because of the large volumes of transactions and high net worth institutions like banks and hedge funds involved that constantly seek to gain a competitive advantage in terms their ability to forecast prices. There have been multiple attempts to model the forex market in the past ranging from pure fundamental frameworks to computationally intensive neural network prediction systems over the years. As a part of hedging, Yaganti and Kamaiah (2015) magnify the importance of exchange rate forecasting.

 
 
 

Financial Risk Management Journal, Exchange Rate Forecast Enhancement, Principal Component Analysis (PCA), Vector Autoregression (VAR).