Showing papers on "Foreign exchange market published in 2023"

An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges

Abstract: Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate stock and foreign exchange markets due to the advantage of artificial intelligence. Recent years have seen a proliferation of the deep reinforcement learning algorithm’s application in algorithmic trading. DRL agents, which combine price prediction and trading signal production, have been used to construct several completely automated trading systems or strategies. Our objective is to enable interested researchers to stay current and easily imitate earlier findings. In this paper, we have worked to explain the utility of Machine Learning, Deep Learning, Reinforcement Learning, and Deep Reinforcement Learning in Quantitative Finance (QF) and the Stock Market. We also outline potential future study paths in this area based on the overview that was presented before.

Hybrid Forex prediction model using multiple regression, simulated annealing, reinforcement learning and technical analysis

Abstract: Foreign exchange market refers to the market in which currencies from around the world are traded. It allows investors to buy or sell a currency of their choice. Forex interests several categories of stakeholders, such as companies that carry out international contracts, large institutional investors, via the main banks, which carry out transactions on this market for speculative purposes. One of the most important aspects in the Forex market is knowing when to invest by buying, selling, and this through the recorded trend of a currency pair, but given the characteristics of the Forex market namely its chaotic, noisy and not stationary nature, prediction becomes a big challenge for traders when it comes to predicting accuracy. This paper aims to predict the right action to be taken at a certain moment through the development of a model that combines multiple techniques such multiple regression, simulated annealing meta-heuristics, reinforcement learning and technical indicators.

Forex market forecasting using machine learning: Systematic Literature Review and meta-analysis

Abstract: Abstract Background When you make a forex transaction, you sell one currency and buy another. If the currency you buy increases against the currency you sell, you profit, and you do this through a broker as a retail trader on the internet using a platform known as meta trader. Only 2% of retail traders can successfully predict currency movement in the forex market, making it one of the most challenging tasks. Machine learning and its derivatives or hybrid models are becoming increasingly popular in market forecasting, which is a rapidly developing field. Objective While the research community has looked into the methodologies used by researchers to forecast the forex market, there is still a need to look into how machine learning and artificial intelligence approaches have been used to predict the forex market and whether there are any areas that can be improved to allow for better predictions. Our objective is to give an overview of machine learning models and their application in the FX market. Method This study provides a Systematic Literature Review (SLR) of machine learning algorithms for FX market forecasting. Our research looks at publications that were published between 2010 and 2021. A total of 60 papers are taken into consideration. We looked at them from two angles: I the design of the evaluation techniques, and (ii) a meta-analysis of the performance of machine learning models utilizing evaluation metrics thus far. Results The results of the analysis suggest that the most commonly utilized assessment metrics are MAE, RMSE, MAPE, and MSE, with EURUSD being the most traded pair on the planet. LSTM and Artificial Neural Network are the most commonly used machine learning algorithms for FX market prediction. The findings also point to many unresolved concerns and difficulties that the scientific community should address in the future. Conclusion Based on our findings, we believe that machine learning approaches in the area of currency prediction still have room for development. Researchers interested in creating more advanced strategies might use the open concerns raised in this work as input.

Consequences to the Stock Market Caused by Currency Fluctuations: Jordanian Evidence

Abstract: Purpose: The aim of this study is to examine the fluctuations in currency exchange in the stock market, as it relates to the Jordanian market. Theoretical framework: The study's main goal is to identify how Jordanian SMR changes in currency exchange are influenced. Thus, the rational expectations theory has been used in this study. This theory illustrates three key aspects that influence the decision to participate in the stock market, including the availability of information, prior experience, and human reason. Design/ methodology/approach: The study incorporates the quantitative design, in which, information was extracted from secondary sources i.e. Investing.com. The information has been compiled considering the exchange rates, stock market returns, and cotton prices, beginning in 2015 and ending in 2020. The analysis has been conducted with Granger Causality test, VAR, and ADF. Findings: The results of this study concludes that fluctuations of exchange rates do not impact changes in cotton prices and the returns of the Jordanian stock exchange. Research, Practical & Social implications: The study is limited to Jordan, as only Jordan’s cotton price and stock market return data have been gathered. In addition, the time period of consideration of data is confined from 2015 to 2019, limiting the study’s scope. Originality/Value: The study uses sensitivity analysis to investigate the robustness of the impacts of fluctuations in currency exchange in the stock market, as it relates to the Jordanian market.

Asymmetric impact of the COVID-19 pandemic on foreign exchange markets: Evidence from an extreme quantile approach

Abstract: This study analyzes asymmetric transmission from the COVID-19 pandemic to major foreign exchange markets from 2 January 2020 to 2 June 2022. This paper contributes to the literature by investigating how the impact of COVID-19 on currency markets co-moves across market conditions and investment horizons. The article uses the recently developed cross-quantilogram framework to achieve this, which quantifies the cross-quantile dependency across time series without any moment condition requirement. The findings demonstrate that changes in the total daily global confirmed cases of COVID-19 can forecast changes in the currency markets under all market circumstances. These findings have significant implications for global investors and policymakers.

Currency risk management in the bank

Abstract: The importance and actuality of the theme is characterized by the fact that at the base of every entity, be it commercial or non-commercial, private or public, financial, there are resources, whether they are in foreign currency or the national currency. In addition, thanks to their correct management by using the most efficient technologies and well-structured policies, it will be possible to avoid and minimize any risk. Currency risk represents the probability of suffering a loss or non-realization of the forecasted profits due to the variation of the exchange rate on the market, in a direction unfavorable to the adopted position. Currency risk in the bank can occur at any time because it cannot be predicted. That is why each bank practices different methods to prevent this risk. In order to protect against possible future losses, the bank must create a complex evaluation system. The development of such a system is related to conducting multiple researches. Exchange rate fluctuations negatively influence any activity, including bank activity. Each bank is oriented towards minimizing or eliminating this exchange rate risk by using different well-determined methods that will contribute to the stability of the exchange rate. The purpose of the research is to highlight the aspects that comprise the management of foreign exchange risk in the bank, the methods used regarding the effective management of the existing foreign exchange risk, the characterization of foreign exchange risk management strategies.

Heterogeneous Behavior and Volatility Transmission in the Forex Market using High-Frequency Data

Abstract: Abstract This paper examines the dynamics of volatility transmission in the forex market using high-frequency data for five exchange rates (EUR/USD, EUR/JPY, EUR/CHF, EUR/GBP and EUR/AUD) from January 2004 to October 2014. We apply a multivariate HAR model in which the daily realized volatility of a given exchange rate depends on both its own lags and the lagged realized volatilities of the other exchange rates. Furthermore, this model is able to identify short-term, medium-term, and long-term transmission effects. We also find evidence of statistically significant volatility transmission between exchange rates in the forex market, especially during periods marked by market uncertainty. JEL classification numbers: C5, F31, G15. Keywords: Foreign exchange markets, Realized volatility, High-frequency data, Volatility transmission, HAR model, DCC-GARCH.

A Brief Overview of the Development on the Foreign Exchange Market in China: From 2018 to 2022

Abstract: Transactions in the foreign exchange market determine the value of one currency relative to another, and in the present day of globalization, the foreign exchange market in each country has been the focus of scholars. However, few scholars seem to have combed through the development of Chinese foreign exchange market and the hot spots and directions of research in the last five years. Therefore, this study uses keyword co-occurrence and clustering analysis of 177 literature from Scopus and Web of Science were conducted using Citiespace software. The results show that the topics that scholars have focused on in the last five years include "exchange rate," "market," "China,” etc. In addition, this study considers that the research directions of scholars are mainly divided into three categories.

Validity of Forex Trading Agreements Using Foreign Currency as the Object of Investment

Abstract: Forex (Foreign Exchange) is better known in Indonesian as Forex trades between two currencies from two countries by many States Parties, institutions and individuals. Forex trading between market players that lasts Open 5 days a week, 24 hours a day. inner consistency Forex trading operations based on Article 1338 of the Legislative Code Civil. These provisions are less effective because there are still many brokers illegal brokers, unlicensed and unscrupulous companies with the intention of misleading investors. Due to the futures trading law Commodity has not yet implemented a complete forex trading system.Speaking of validity foreign exchange investment contract based on article 1320 of the civil code on the conditions of validity of the contract, foreign exchange as the object of the contract, not contrary to the objective conditions which have legal consequences, null and void history and characteristics of book iii of the civil code which regulates the commitment is open, so it is allowed to make one agreements other than those provided for in the civil code, provided that they are not contrary with the law, public order and good morals. This research advising the government and the legislator to make regulations related toforeign exchange investment regulations to prevent things that are not legal protection desired and guaranteed for investors

An International Study of Application of Long Short-Term Memory (LSTM) Neural Networks for the prediction of stock and forex markets

Abstract: This study focuses on the use of Long Short-Term Memory (LSTM) neural networks for stock and currency market forecasting. Accurate projections are difficult to make because of the complex dynamics and non-linear interactions that characterise financial markets. A Recurrent Neural Networks (RNN) variation called LSTM is particularly good at identifying temporal dependencies and long-term patterns in sequential data. The goal of LSTM models is to discover significant insights and produce precise predictions using past price data. In addition to discussing data pretreatment methods, model creation, and assessment metrics related to stock and FX market prediction, this work examines the benefits of LSTM in capturing market dynamics. Case studies and empirical analysis are used to investigate the capabilities and constraints of LSTM models in forecasting market movements.

Machine Learning-Based Timeseries Analysis for Cryptocurrency Price Prediction: A Systematic Review and Research

Abstract: A virtual currency known as cryptocurrencies holds all business online. It’s virtual money that wouldn’t materialize like complicated conventional paper currency. Thus, this study emphasizes a distinction between distributed paper currency and cryptocurrencies, where these individuals may access information without outside interference. Because of its considerable market swings, such cryptocurrencies have an influence upon commerce as well as foreign diplomacy. Virtual currencies which are available in the market, such as Bitcoin (BTC), Ethereum (ETH), Terra (LUNA), Solana (SOL), Cardano (ADA), Tether (USDT), Binance coin (BNB), USD coin, XRP coin, Avalanche coin (AVAX) and Lite coin (LTC), etc. This study focussed on a detailed analysis of the literature about Machine Learning (ML) methods used for predictions. This proposed work also focused on implementing an efficient Machine Learning (ML)-based time series model for predicting BTC cryptocurrency prices. Long Short-Term Memory (LSTM) forecasting theory was established to accommodate the fluctuation of bitcoin prices and achieve great precision. The effectiveness of the LSTM in predicting the price of a cryptocurrency is demonstrated by this suggested study’s comparison between it and comparable time-series models.

Foreign Exchange Transactions in the Perspective of Islamic Economics

Abstract: Judging from its history, the currency has undergone many changes in meaning. Starting from its existence during the barter period to the modern era of money consisting of fiat money and demand deposits. At this time a new term has appeared virtual money. History records that money has developed its meaning, function and value of money. This change automatically has an impact on changes in the law that applies to it. This article uses a qualitative method with a literature study approach. The data presented in this article are sourced from statutory documents, journals, records, and books related to this research. The legal buying and selling of foreign exchange (forex) are permissible, with the following conditions; There is Ijab accompanied by Qabul (there is an agreement to give or receive). As stated in the MUI fatwa that foreign exchange transactions (al-Sharf) are only permitted if there is a need, for example as a precaution and not for speculation (chance) with conditions that have no legal basis.

Machine Learning-Based Trading Robot for Foreign Exchange (FOREX)

Abstract: Financial markets are extremely complex due to their non-linear, non- stationary, and time-variant nature. Researchers are increasingly investigating the automation of financial trading as AI capabilities advance particularly in complicated markets such as Foreign Exchange. (FOREX). Traders typically use three types of trading analysis: technical analysis, which uses historical price data to perform mathematical computations; fundamental analysis, which investigates economic factors influencing price movement; and sentiment analysis, which investigates the emotional movement due to market news. There have been a variety of integration techniques utilized in prior attempts to combine AI prediction models with these approaches. Federated learning, which might integrate the learning capacity of dispersed models, has not yet been used by anyone. This study proposes a FOREX trading robot that uses federated learning to aggregate trading analysis methodologies, specifically analyzing market news sentiment and technical calculations based on historical prices for a specific currency pair to determine when to buy or sell for maximum profit. The implementation of federated learning is a future work that we intend to test in the near future. This is the first research work to study the deployment of federated learning in the financial trading field.

Scalp the Foreign Exchange Market with Deep Reinforcement Learning

Abstract: This paper presents a reinforcement learning approach for foreign exchange trading. Inspired by technical analysis methods, this approach makes use of technical indicators by encoding them into Gramian Angular Fields and searches for patterns that indicate price movements using convolutional neural networks (CNN). In addition to the policy that determines the action to take, an extra regression head is utilized to determine the size of market orders. This paper also experimentally shows that maximizing the return of individual trade or cumulative reward in a finite time window results to better performance.

Modelling volatility effects between stock, oil, gold and forex markets: Evidence from India

Abstract: Although several studies on the integration of diverse stock markets have been conducted in the financial literature, most of them have focused on the integration and volatility spillovers across established stock markets. The present study explores the dynamics of integration and volatility spillover across gold, oil, forex, and stock markets during four significant events in India: the pre-changed government regime, the post-changed government regime, the post-Brexit referendum date, and the COVID era. Daily data from 2010 to 2022 is divided into four categories using the Chow test. This is done to examine if these events’ financial turmoil affects market interconnectivity. The unit root test determines data stationarity. The ARCH LM test examines series volatility clustering, and the BEKK GARCH test examines market volatility spillover. Results indicate that gold cannot be considered a hedge or safe haven. Secondly, market interconnectedness increased during the crisis period. Third, domestic political and geopolitical conditions globally do not increase the scale of spillover amongst financial assets, though they impact the spillover’s magnitude. The results of this study have several important implications for portfolio diversification and risk management.

Quantum Monte Carlo simulations for estimating FOREX markets: a speculative attacks experience

Abstract: Abstract The foreign exchange markets, renowned as the largest financial markets globally, also stand out as one of the most intricate due to their substantial volatility, nonlinearity, and irregular nature. Owing to these challenging attributes, various research endeavors have been undertaken to effectively forecast future currency prices in foreign exchange with precision. The studies performed have built models utilizing statistical methods, being the Monte Carlo algorithm the most popular. In this study, we propose to apply Auxiliary-Field Quantum Monte Carlo to increase the precision of the FOREX markets models from different sample sizes to test simulations in different stress contexts. Our findings reveal that the implementation of Auxiliary-Field Quantum Monte Carlo significantly enhances the accuracy of these models, as evidenced by the minimal error and consistent estimations achieved in the FOREX market. This research holds valuable implications for both the general public and financial institutions, empowering them to effectively anticipate significant volatility in exchange rate trends and the associated risks. These insights provide crucial guidance for future decision-making processes.