scholarly journals An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

2019 ◽  
Vol 9 (15) ◽  
pp. 2980 ◽  
Author(s):  
Muhammad Yasir ◽  
Mehr Yahya Durrani ◽  
Sitara Afzal ◽  
Muazzam Maqsood ◽  
Farhan Aadil ◽  
...  
Keyword(s):  
Deep Learning ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Foreign Exchange Rate ◽  
Exchange Rate Forecasting ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Us Dollar ◽  
The Exchange Rate

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’ 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.

scholarly journals Uprawnienia kredytobiorcy zaciągającego kredyt denominowany lub indeksowany do waluty innej niż polska w świetle ustawy antyspreadowej

Ekonomia ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 39-56
Author(s):  
Magdalena Paleczna ◽  
Edyta Rutkowska-Tomaszewska
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Consumer Credit ◽  
Competitive Position ◽  
Credit Facility ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Credit Law ◽  
Credit Contract

Rights of the borrower committing denominated or indexed loan in a foreign currency in light of the Anti-spread ActIn 2004–2008 banks offered consumer denominated loan in a foreign currency, which was a competitive position in relation to a PLN credit facility. Banks had not informed about foreign exchange differences, therefore had caused increase in household indebtedness. Banks also had reserved that consumer has to buy currency only from the bank-lender. In 2011 the Anti-spread Act was adopted, which amended banking law and consumer credit law. Creditors were obligated to inform consumer about rules of determining the manners and dates of fixing the currency exchange rate on the basis of which in particular the amount of credit, its tranches and principal and interest instalments are calculated, and the rules of converting into the currency of credit disbursement or repayment. That information and information about the rules of opening and operating the account shall be concluded in a credit contract. Borrower can repay principal and interest instalments and prepay the full or partial amount of the loan directly in that currency.

scholarly journals Forecasting INR Exchange Rate Against USD, GBP, JPY, SGD, EUR, AED Using Machine Learning

2021 ◽  
pp. 684-689
Author(s):  
Sumith Pevekar
Keyword(s):  
Machine Learning ◽  
Exchange Rate ◽  
Random Forest ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Foreign Exchange Rate ◽  
Random Forest Algorithm ◽  
Currency Exchange ◽  
Predicting Performance ◽  
Machine Learning Model

The price of a native currency expressed in terms of another currency is known as a foreign exchange rate. In other terms, a foreign exchange rate compares the value of one currency to that of another. The value of standardized currencies varies with demand, supply, and consumer confidence around the world due to which their values fluctuate over time. To forecast the exchange rate of INR, I have developed a machine learning model. The model was trained to estimate six foreign currency exchange rates against the Indian Rupee using historical data. This model uses Random Forest algorithm to train and predict the values. The suggested system’s predicting performance is assessed and contrasted using statistical metrics. According to the findings, the Random Forest algorithm-based model predicts well and achieves an accuracy of 93.61%. KEYWORDS: Regression, Random Forest, Exchange Rate, INR

scholarly journals Forecasting The Exchange Rate (IDR) of US Dollar (USD) Using Locally Stationary Wavelet

2018 ◽  
Vol 18 (2) ◽  
pp. 145-154
Author(s):  
Dina Tri Utari
Keyword(s):  
Exchange Rate ◽  
Time Series Data ◽  
Market Mechanism ◽  
Series Data ◽  
Percentage Error ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Us Dollar ◽  
The Exchange Rate ◽  
The Impact

Currency exchange rate of a country to the other countries is fluctuative. The movement of the exchange rate affects the country’s economy. The exchange rate can change any time according to the market mechanism, therefore currency exchange predictions is required to determine future economic policy. Based on the impact of exchange rate in economy fluctuations, an accurate model is needed to determine the exchange rate movements.In this case, the model is Locally Stationary Wavelet (LSW). This model combines stocastic process class based on wavelet non decimated. LSW model can catch most of the information in time series data. Based on the application of LSW mtehod on the data of the rupiah against the US dollar for the period April 2016 - March 2017, it can be concluded that model provides forecasting results approaching actual data therefore it can be used for forecasting exchange rates. The value of the mean absolute percentage error (MAPE) is 0,1201293%. 

Optimistic and pessimistic economic sentiments and US Dollar exchange rate

2021 ◽  
Author(s):  
Sonia Kumari ◽  
Suresh Kumar Oad Rajput ◽  
Rana Yassir Hussain ◽  
Jahanzeb Marwat ◽  
Haroon Hussain
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Ordinary Least Squares ◽  
Exchange Market ◽  
Dollar Exchange Rate ◽  
The Us ◽  
Short Run ◽  
Us Dollar ◽  
The Exchange Rate

This study investigates the affiliation of various proxies of economic sentiments and the US Dollar exchange rate, mainly focusing on the real effective exchange rate of USD pairing with three other major currencies (USDEUR, USDGBP, and USDCAD). The study has employed Google Trends data of economy optimistic and pessimistic sentiments index and survey-based economy sentiments data on monthly basis from January 2004 to December 2018. The study engaged Ordinary Least Squares (OLS) and Auto-Regressive Distributed Lag (ARDL) estimation techniques to evaluate the short-run and long-run effects of economy-related sentiments and macroeconomic variables on the exchange rate. The results from the study found that Economy Optimistic Sentiments Index (EOSI) and Economy Pessimistic Sentiments Index (EPSI) appreciate and depreciate the US Dollar exchange rate in the short-run, respectively. Our sentiment measures are robust to survey-based Michigan Consumer Sentiment Index (MSCI), Consumer Confidence Index (CCI), and various macroeconomic factors. The MSCI and CCI sentiments show a long-term impact on the foreign exchange market. This study implies that economic sentiments play a vital role in the foreign exchange market and it is essential to consider behavioral aspects when modeling the exchange rate movements.

Determining Influencing Factors of Currency Exchange Rate for Decision Making in Global Economy Using MARS Method

2018 ◽  
pp. 261-273 ◽  
Author(s):  
Hasan Dinçer ◽  
Ümit Hacıoğlu ◽  
Serhat Yüksel
Keyword(s):  
Economic Growth ◽  
Decision Making ◽  
Exchange Rate ◽  
Current Account ◽  
Global Economy ◽  
Current Account Deficit ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Us Dollar ◽  
Turkish Lira

The aim of this study is to identify the determinants of US Dollar/Turkish Lira currency exchange rate for strategic decision making in the global economy. Within this scope, quarterly data for the period between 1988:1 and 2016:2 was used in this study. In addition to this aspect, 10 explanatory variables were considered in order to determine the leading indicators of US Dollar/Turkish Lira currency exchange rate. Moreover, Multivariate Adaptive Regression Splines (MARS) method was used so as to achieve this objective. According to the results of this analysis, it was defined that two different variables affect this exchange rate in Turkey. First of all, it was identified that there is a negative relationship between current account balance and the value of US Dollar/Turkish Lira currency exchange rate. This result shows that in case of current account deficit problem, Turkish Lira experiences depreciation. Furthermore, it was also concluded that when there is an economic growth in Turkey, Turkish Lira increases in comparison with US Dollar. While taking into the consideration of these results, it could be generalized that emerging economies such as Turkey have to decrease current account deficit and investors should focus on higher economic growth in order to prevent the depreciation of the money in the strategic investment decision.

scholarly journals US Dollar/Turkish Lira Exchange Rate Forecasting Model Based on Deep Learning Methodologies and Time Series Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1553
Author(s):  
Harun Yasar ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Exchange Rate ◽  
Time Series Analysis ◽  
Word Embedding ◽  
Learning Models ◽  
Exchange Rate Forecasting ◽  
Us Dollar ◽  
Turkish Lira ◽  
Embedding Methods

Exchange rate forecasting has been an important topic for investors, researchers, and analysts. In this study, financial sentiment analysis (FSA) and time series analysis (TSA) are proposed to form a predicting model for US Dollar/Turkish Lira exchange rate. For this purpose, the proposed hybrid model is constructed in three stages: obtaining and modeling text data for FSA, obtaining and modeling numerical data for TSA, and blending two models like a symmetry. To our knowledge, this is the first study in the literature that uses social media platforms as a source for FSA and blends them with TSA methods. To perform FSA, word embedding methods Word2vec, GloVe, fastText, and deep learning models such as CNN, RNN, LSTM are used. To the best of our knowledge, this study is the first attempt in terms of performing the FSA by using the combinations of deep learning models with word embedding methods for both Turkish and English texts. For TSA, simple exponential smoothing, Holt–Winters, Holt’s linear, and ARIMA models are employed. Finally, with the usage of the proposed model, any user who wants to make a US Dollar/Turkish Lira exchange rate forecast will be able to make a more consistent and strong exchange rate forecast.

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