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 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 Rozliczenie kredytu mieszkaniowego z niedozwolonymi walutowymi klauzulami indeksacyjnymi

Studia BAS ◽  
2021 ◽  
Vol 2 (66) ◽  
pp. 173-193
Author(s):  
Marcin Liberadzki
Keyword(s):  
Exchange Rate ◽  
Final Section ◽  
Foreign Currency ◽  
The Other ◽  
Mortgage Loan ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Better Than

This paper deals with how to settle a foreign currency exchange rate indexed mortgage loan between a bank and a consumer if the court declares that the loan agreement has an abusive clause. At present, many consumers in Poland strive to void their contracts on the grounds that they contain an abusive indexation clause, mainly referred to the CHF/PLN exchange rate. The calculations are based on a CHF indexed 30 years mortgage with decreasing monthly installments, starting in 2008. The settlement amount is calculated for two most probable scenarios: 1) the contract is declared void; 2) the contract continues but without the abusive indexation clause. One cannot determine which scenario is definitely better than the other for any party. In the final section of the article the implications for Polish banks are presented.

The Legal Consequences of Unfair Contract Terms in Consumer Loan Agreements Valorized with Foreign Currency

2021 ◽  
Vol 46 (3-4) ◽  
pp. 346-373
Author(s):  
Bartosz Ziemblicki ◽  
Mateusz Lewandowski
Keyword(s):  
European Union ◽  
Exchange Rate ◽  
Foreign Currency ◽  
Exchange Rate Risk ◽  
The European Union ◽  
Currency Exchange ◽  
Court Of Justice ◽  
Currency Exchange Rate ◽  
Unfair Terms ◽  
Legal Consequences

Abstract In recent years, the Court of Justice of the European Union has issued a number of judgments addressing the issue of consumer protection in connection with the use of unfair terms by banks in loan agreements indexed with a foreign currency exchange rate. Most of them have concerned issues of exchange rate risk and exchange rate differences between the purchase and sale rates of a given currency applied by the bank. This article analyzes the recent ruling by the Court of Justice of the European Union in the Dziubak case, which was initiated by referring questions for a preliminary ruling by a Polish court. The article’s purpose is to assess the position taken by the cjeu in this respect and its significance for consumers in Poland. Particular attention was paid to the considerations with regard to the possibility of replacing unfair provisions with general provisions and assessing the consumer’s awareness of the consequences of declaring a contract invalid. The aim is to examine the issues that were dealt with by the Court of Justice of the European Union in the Dziubak case, including – in particular – the answer to the question of whether the issues discussed by the cjeu had already been considered in its previous jurisprudence and whether it presents new, previously unknown legal consequences of the inclusion of unfair contract terms in loan agreements.

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 Foreign currency exchange rate prediction using neuro-fuzzy systems

2018 ◽  
Vol 144 ◽  
pp. 232-238 ◽  
Author(s):  
Yoke Leng Yong ◽  
Yunli Lee ◽  
Xiaowei Gu ◽  
Plamen P Angelov ◽  
David Chek Ling Ngo ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Fuzzy Systems ◽  
Foreign Currency ◽  
Rate Prediction ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Neuro Fuzzy ◽  
Exchange Rate Prediction

scholarly journals ANALYSIS OF COMPANY PERFORMANCE AND AUDIT OPINIONS IN INDONESIAN FOOD AND BEVERAGES COMPANIES

2018 ◽  
Vol 2 (2) ◽  
pp. 1-12
Author(s):  
Febby Namira ◽  
Yosman Bustaman
Keyword(s):  
Exchange Rate ◽  
Inflation Rate ◽  
Foreign Currency ◽  
Cash Flows ◽  
Company Performance ◽  
Audit Opinion ◽  
Food And Beverages ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Thin Volume

This paper analyzes relationship between company performance and audit opinion. We use some financial ratios as measurement of firm’s performance such as liquidity, efficiency, profitability, market measurement and cash flows. This study uses food and beverages companies listed in Bursa Efek Indonesia (BEI) for five consecutive years from 2009 until 2013 as our sample data. Panel data analyses are used to regress our empirical model. After controlling with size of company and macroeconomics variables namely inflation rate and movement of Indonesian currency exchange rate against US dollar, we find that firm’s liquidity, profitability, market ratios and company cash flow significantly affect the audit opinion. Large firm size does not influence the audit opinion significantly, meanwhile both macro economic variables inflation rate and exchange rate link negatively with audit opinion however do not significantly affect the opinion. Thin volume of transactions in foreign currency among these companies might lead to non-significant effect between audit opinion and exchange rate.

scholarly journals Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

2019 ◽  
Vol 9 (2) ◽  
pp. 2858-2863
Keyword(s):  
Exchange Rate ◽  
Foreign Currency ◽  
Short Term Memory ◽  
Percentage Error ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Currency Exchange ◽  
Currency Exchange Rate

With the growing population in the world, economic stability varies day by day. In case of India all banking transaction rules and regulations are taken by Reserve bank of India (RBI) whereas for other countries it is different. Therefore numerous academicians have projected their research on forecasting the currency exchange rate for diverse countryside. Foreign currency exchange rate prediction is a very pivotal task for international market. Hence researchers have explored different methods for predicting foreign currency exchange rate. In this work, we have taken Indian rupees (INR) with two different country’s data set such as Japanese yen (JPY) andChinese Yuan (CNY)for daily, weekly and monthlyprediction beforehand. We implemented a hybrid model oflong short-term memory (LSTM) with K-nearest neighbour (KNN) which gives better opening price prediction accuracy on our dataset. The accuracy of the prediction results are measured by the help of performance standards such as mean absolute percentage error (MAPE) and root mean square error (RMSE).

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