Suchergebnisse

Financial time series analysis is an important research area that can predict various economic indicators such as the foreign currency exchange rate. In this paper, a deep-learning-based model is proposed to forecast the foreign exchange rate. Since the currency market is volatile and susceptible to ongoing social and political events, the proposed model incorporates event sentiments to accurately predict the exchange rate. Moreover, as the currency market is heavily dependent upon highly volatile factors such as gold and crude oil prices, we considered these sensitive factors for exchange rate forecasting. The validity of the model is tested over three currency exchange rates, which are Pak Rupee to US dollar (PKR/USD), British pound sterling to US dollar (GBP/USD), and Hong Kong Dollar to US dollar (HKD/USD). The study also shows the importance of incorporating investor sentiment of local and foreign macro-level events for accurate forecasting of the exchange rate. We processed approximately 5.9 million tweets to extract major events&rsquo ; sentiment. The results show that this deep-learning-based model is a better predictor of foreign currency exchange rate in comparison with statistical techniques normally employed for prediction. The results present evidence that the exchange rate of all the three countries is more exposed to events happening in the US.

The main aim of this paper is to forecast the future values of the exchange rate of the USD. Dollar (USD) and Pakistani Rupee (PR). For this purpose was used the ARIMA model to forecast the future exchange rates, because the time series was stationary at first difference. Data reported to five years ranging from the first day of April 2014 to 31st March 2019. The results proved that ARIMA (1,1,9) is the most suitable model to forecast the exchange rate. The difference between the forecasted values and actual values are less than 1%; therefore, it was found that the ARIMA is robust and this model will be helpful for the government functionaries, monetary policymakers, economists and other stakeholders to identify and forecast the future trend of the exchange rate and make their policies accordingly. ; info:eu-repo/semantics/publishedVersion

The main aim of this paper is to forecast the future values of the exchange rate of the USD. Dollar (USD) and Pakistani Rupee (PR). For this purpose was used the ARIMA model to forecast the future exchange rates, because the time series was stationary at first difference. Data reported to five years ranging from the first day of April 2014 to 31st March 2019. The results proved that ARIMA (1,1,9) is the most suitable model to forecast the exchange rate. The difference between the forecasted values and actual values are less than 1%; therefore, it was found that the ARIMA is robust and this model will be helpful for the government functionaries, monetary policymakers, economists and other stakeholders to identify and forecast the future trend of the exchange rate and make their policies accordingly. ; info:eu-repo/semantics/publishedVersion