Forex Market Research Articles

Unveiling the dynamic linkages between energy, forex and financial markets amidst natural and man-made outbreaks

Purpose This paper aims to analyze the dynamic linkages of the energy market with the forex market. The energy market is measured by crude oil WTI, while the forex market is proxied by Brazilian real (RBRL), Mexican peso (RMXN), South African rand (RZAR), Turkish lira (RTRY) and British pound sterling (RGBP) exchange rate. Design/methodology/approach For the study, daily observations of these constituent asset classes extending from December 31, 2019, to August 16, 2022, are taken as the data. Furthermore, it is categorized into two different sub-samples in the form of the COVID-19 outbreak (December 31, 2019 to February 23, 2022) and the Russo−Ukraine invasion (February 24, 2022 to August 16, 2022). For empirical estimation, Diebold and Yilmaz model (2014) and Barunik and Krehlik test (2018) are used to examine the dynamic linkages. Findings The study concludes that the Mexican peso (RMXN) receives and transmits the highest spillover, while crude oil (RCOWTI) receives and transmits the least volatility to the network connection in full sample. In addition, the authors report that the dynamic linkage is not constant in the short, medium and long run. Furthermore, the spillover index in the Russo−Ukraine invasion is higher (29.92%) than full observation (22.03%) and COVID-19 outbreak (21.10%) in the short run. Originality/value This paper ventures to offer insight to investors, traders and policymakers based on normal trading days and crisis periods.

Analysis in Decision Making for XAUUSD (Gold/USD) Pair Transactions in Futures Trading

Nowadays, many trading instruments, such as XAU/USD commodity futures trading, a label for spot gold traded in the foreign exchange market, are offered. Gold (XAU) is traded against the US dollar (USD), and its price represents the cost of one ounce of gold in USD. XAU/USD is traded like any other traditional currency in the forex market. This study explores how fundamental and technical analysis can be used as a basis for better decision-making in gold futures trading. The research method used is qualitative, with a descriptive analysis approach based on secondary data in the market. So, in the summary of the analysis results, it is found that fundamental and technical analysis is a tool used for decision-making in gold transactions and futures trading. Decision-making in gold transactions in futures trading should be based on fundamental and technical analysis at a minimum. Decision-making based solely on the results of one analysis alone can be fatal. Various sources of fundamental and technical analysis tools can be used as a decision-making tool in gold and futures trading.

Financial Modelling System Using Deep Neural Networks (DNNs) for Financial Risk Assessments

ABSTRACTThe FOREX market assessment is a big challenge for investors and global risk managers. However, the present study uses daily multicurrency exchange rate returns data from 2007 to 2022 to estimate the learning returns performance of the proposed model to find a safe‐haven currency for optimal investment strategy. The categorical returns are classified into good returns (GRs), bad returns (BRs) and no returns (NRs). Therefore, the present study needs to use a one‐hot‐encoding function to convert a categorical dataset into a numeric format with TensorFlow. The present study proposes a deep neural network‐based multilayer perceptron (DNN‐based MLP) with a backpropagation algorithm to estimate the learning returns performance of the proposed model to find a safe‐haven currency for optimal investment strategy. The findings showed that currency exchange rate return 2 (CERR2) is relatively a safe‐haven currency than currency exchange rate return 1 (CERR1) and currency exchange rate return 3 (CERR3). Moreover, the findings also showed that the proposed model gives optimal learning return performance. This study may assist FOREX investors to modify their investment strategies under shed light of findings of the study. In addition, the findings of the present study may also support global risk managers to revisit their hedging strategies.

Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets

Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability.

Comparative analysis of neural network architectures for short-term FOREX forecasting

This document outlines the analysis, design, implementation, and benchmarking of various neural network architectures in a short-term frequency prediction system for the FOREX market. Our objective is to emulate the decision-making process of a human expert (technical analyst) through a system that swiftly adapts to market condition changes, thereby optimizing short-term trading strategies. We have designed and implemented a series of LSTM neural network architectures that take exchange rate values as input to generate short-term market trend forecasts. Additionally, we developed a custom ANN architecture based on simulators for technical analysis indicators. We performed a comparative analysis of the results and came to useful conclusions regarding the suitability of each architecture and the cost in terms of time and computational power to implement them. The ANN custom architecture produces better prediction quality with higher sensitivity using fewer resources and spending less time than LSTM architectures. The ANN custom architecture appears to be ideal for use in low-power computing systems and for use cases that need fast decisions with the least possible computational cost.

Foreign Exchange Rates Prediction Based on Comparative Models

With the development of globalization, fluctuations in the foreign exchange market have the huge potential to influence global economy. Machine Learning technology shows great potential in the field of financial forecasting than traditional statistical methods in recent years. This research selected statistical model Autoregressive Integrated Moving Average (ARIMA) and machine learning models Linear Regression and Long- and Short-Term Memory (LSTM) to make training and testing of historical data on the forex market. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-Squared were chosen as evaluation indexes to evaluate the performance of each model and compare their performance in forex exchange rate forecasts. The results show that ARIMA and LSTM could make better predictions than Linear Regression, between which LSTM has higher degree of fitting data. This research provides a detailed analysis comparing the performance of different models in forex exchange rate forecasting whose results are beneficial to guide forex market forecast in practical application and provide reference for future research.

Multi-agent platform to support trading decisions in the FOREX market

Trading decisions often encounter risk and uncertainty complexities, significantly influencing their overall performance. Recognizing the intricacies of this challenge, computational models within information systems have become essential to support and augment trading decisions. The paper introduces the concepts of trading software agents, investment strategies, and evaluation functions that automate the selection of the most suitable strategy in near real-time, offering the potential to enhance trading effectiveness. This approach holds the promise of significantly increasing the effectiveness of investments. The research also seeks to discern how changing market conditions influence the performance of these strategies, emphasizing that no single agent or strategy universally outperforms the rest. In summary, the overarching objective of this research is to contribute to the realm of financial decision-making by introducing a pragmatic platform and strategies tailored for traders, investors, and market participants in the FOREX market. Ultimately, this endeavor aims to empower people with more informed and productive trading decisions. The contributions of this work extend beyond the theoretical realm, demonstrating a commitment to address the practical challenges faced by traders and investors in real-time decision-making within the financial markets. This multidimensional approach to financial decision support promises to enhance investment effectiveness and contribute to the broader field of algorithmic trading.

Interconnectedness in the FOREX market during the high inflation regime: A network analysis

Our study employs network analysis to examine the interconnectedness in the Foreign Exchange market, focusing on 18 key currencies from both advanced and emerging markets from 2017 to 2023. This analysis includes a comparative look at the impact of two major recent crises: the COVID–19 pandemic and the Russia-Ukraine war. The latter is notably marked by increased inflation and uncertainty in economic policies. During the COVID–19 pandemic, we observed a significant level of interconnectedness among the EURO and 17 other currencies, indicating a dense network. In contrast, the period of the Russia-Ukraine war revealed a tendency for countries less engaged in international trade to become more isolated, as seen by their greater distance from the network's centroid. Additionally, we developed the Systemic Contagion Index, which shows a heightened susceptibility to contagion among developed economies during both the COVID–19 and Russia-Ukraine war crises, underscoring the vulnerability of developed countries' currency networks in times of turmoil. This study provides valuable insights for investors and global businesses, offering guidance on assessing currency vulnerability and enhancing their strategies for hedging and managing risks amidst fluctuating market conditions, especially during the high inflation regime.

Comparative Study of Artificial Neural Networks and Hidden Markov Model for Financial Time Series Prediction

Financial Time Series analysis and prediction isone of the interesting areas in which past data could be usedto anticipate and predict data an d information about future.There are many artificial intelligence approaches used in theprediction of time series, such as Artificial Neural Networks (ANN) and Hidden Markov Models (HMM). In this paperHMM and HMM approaches for predicting financial timeseries are presented. ANN and HMM are used to predicttime series that consists of highest and lowest Forex indexseries as input variable. Both of ANN and HMM are trained on the past dataset of the chosen currencies (such as EURO/USD which is used in this paper). The trained ANN andHMM are used to search for the variable of interestbehavioral data pattern from the past dataset. The obtained results was compared with real values from Forex (ForeignExchange) market database [1]. The power and predictiveability of the two models are evaluated on the basis of MeanSquare Error (MSE). The Experimental results obtained areencouraging, and it demonstrate that ANN and HMM can closely predict the currency market, with a small differentin predicting performance

Revolutionizing Forex Trading: Harnessing AI for Seamless Market Automation

The article explores the integration of AI and automated trading in the Forex market, focusing on the development of a custom Expert Advisor using FXDreema. To improve trading success, the approach includes a custom indicator and a martingale mechanism for hedging. Backtesting results show clear trade opportunities and substantial profits. Iterative testing procedures are highlighted while discussing optimization and integration challenges. The significance of automated trading methods and the part FXDreema plays in democratizing the creation of expert advisors are emphasized in the references. The potential for consistent profitability and future optimization tactics of the Expert Advisor are emphasized in the conclusion.

Forecasting Next-Time-Step Forex Market Stock Prices Using Neural Networks

Purpose: This study aims to predict the closing price of the EUR/JPY currency pair in the forex market using recurrent neural network (RNN) architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), with the incorporation of Bidirectional layers. Methods: The dataset comprises hourly price data obtained from Yahoo Finance and pre-processed accordingly. The data is divided into training and testing sets, and time series sequences are constructed for input into the models. The RNN, LSTM, and GRU models are trained using the Adam optimization algorithm with the mean squared error (MSE) loss metric. Results: Results indicate that the LSTM model, particularly when coupled with Bidirectional layers, exhibits superior predictive performance compared to the other models, as evidenced by lower MSE values. Conclusions: Therefore, the LSTM model with Bidirectional layers is the most effective in predicting the EUR/JPY currency pair's closing price in the forex market. These findings offer valuable insights for practitioners and researchers involved in financial market prediction and neural network modelling

Operational efficiency in the presence of undesirable byproducts: an analysis of Indian banking sector under traditional and market-based banking framework

PurposeIn the backdrop of an increase in market-based banking activities, this paper aims to study operational efficiency of Indian banking sector during 2009–2010 through 2017–2018 considering Capital Gain and Gain from Forex Market (as desirable outputs) and Slippage (as undesirable byproducts) simultaneously, along with Advances – a desirable output considered in the traditional banking performance assessment literature. This enables to have an assessment of performance (as captured by the measured efficiency scores) of Indian Banks following an alternative viewpoint about the banking activities. The authors also explain such efficiency scores in terms of bank-specific factors, banking industry competition scenario and interest rate channel.Design/methodology/approachUsing data envelopment analysis (DEA) method, the authors estimate six alternatives but interlinked operational efficiency scores (TES) of the Indian domestic commercial banks. In the second stage, they explain such TES in terms of bank-specific factors, banking industry competition scenario and interest rate channel.FindingsThe authors observe that the private sector banks as a group outperform those under public ownership. Moreover, although the private sector banks could maintain somewhat consistency in their operational efficiency performance over the sample period, public sector banks clearly show a declining tendency. The second stage econometric estimation results show that the priority sector lending has a negative effect on efficiency. Interestingly, the authors get varying results for the relationship between maturity and efficiency score depending on banks’ strategies on stressed assets management. Furthermore, the analyses result that banks are not so efficient in managing relatively larger-volume loans. It is also observed that banks’ efficiency positively depends on the Credit-to-Deposit (CD) ratio. It is found that the overall operational efficiency of the banks to manage their credit risk portfolio improves with a reduction in the lending rate (LR). However, the interaction of lending activities and capital market shows that with the increase in LR, corporate borrowers may switch to capital market to explore for desired funds, which may induce the banking sector to investment in capital markets and create a positive market sentiment.Originality/valueLiterature, although scanty, is there dealing stressed assets of a bank as some undesirable byproducts of its operational and business activities. However, such literature mostly done within the traditional framework of banking business activities and modern market-based business activities are almost absent in the literature. The authors have done it in the present study.

The Impact of Russia-Ukraine War on Bangladesh’s Economy: A Supply Chain Perspective and Way Forward

Bangladesh’s economy is facing significant challenges restricting from the Russia-Ukraine conflict, intensified by its status as an oil-importing nation vulnerable to global oil price fluctuations. This study addresses the current perspectives and challenges which include inflationary pressures, trade imbalances, and disruptions in the supply chain, leading to increased expenditures for essential commodities. This research was developed through a systematic literature review based on secondary data, including online databases, journals, and review papers, etc. This exploratory study reveals that policymakers need to diversify energy sources, forge new trade partnerships, and implement measures to bolster economic resilience. The economic fallout from the conflict, surpassing the actual war in cost, has depleted the central bank's reserves, prompting austerity measures. The forex market experiences volatility, contributing to high trade deficits and the highest inflation rate in recent years. Bangladesh must address forex management challenges and formulate prudent policies for economic recovery. The conflict has disrupted global commodity markets, leading to inflationary pressures and increased costs for businesses in Bangladesh, especially larger organizations handling rising raw material costs and weak consumer demand. The rationale of this paper is to evaluate the literature on this aspect and apprehend the global demand, and supply of food and commodities, economic impacts in Bangladesh, challenges in global trade, trade relations, currency fluctuations, inflation, emergency price, and its consequences, recognize gaps and advocate future research itineraries. The paper offers a synopsis and appraisal of the overall economy of Bangladesh and finally navigates a way forward to the policymakers which would unlock further research to develop this sector.

High-Accuracy Forex Trading Prediction Model Using Machine Learning Algorithms

The foreign exchange (Forex) market, the largest financial market globally, involves the simultaneous buying and selling of currency pairs such as the United States Dollar (USD) and the Japanese Yen (JPY). Due to the high leverage and volatility inherent in Forex trading, retail investors and traders face significant financial risks. To mitigate this, a predictive model was developed using supervised machine learning to improve trading outcomes. A dataset comprising EUR/USD currency pair data from MetaTrader5, covering a period of 7 years (2015–2022), was streamlined to 5 years (2017–2022) for relevance. The dataset contained 13 features and 1,029 records, which underwent data cleaning and processing. Feature selection was performed using the Boruta package in R, and various machine learning algorithms were employed for model training and testing. The model’s accuracy was evaluated using the ROC curve and the Kappa statistic. The inclusion of the Simple Moving Average indicator improved forecast accuracy. Among the algorithms tested, the Generalized Linear Model (Logistic Model) performed best, achieving an accuracy of 86.28%, a specificity of 87.42%, a sensitivity of 86.5%, and an AUC (ROC) score of 86.97%. This predictive model has the potential to optimize profits, particularly in the context of binary options trading within the Forex market.

Sentimental Analysis on Forex Market

Sentiment analysis in the forex market is all about understanding how traders feel about a particular currency or the overall market. This perception can influence price movements, so traders use sentiment analysis to gauge market direction and identify potential entry and exit points for their trades. Common tools for this include reports on commercial and non-commercial positions (like the Commitment of Traders report), broker sentiment indicators, and even social media analysis. By understanding where the crowd is leaning, traders can make more informed decisions about their own positions.

Cryptocurrency and Financial Stability

This paper introduces cryptocurrency into a two-country open-economy model. Based on the theoretical model, we employ the TVP-VAR model to study the dynamic interdependence among interest rate spread (a proxy in the monetary market), exchange rate (a proxy in the forex market), and Bitcoin transactions (a proxy in the cryptocurrency market). The key finding is that Bitcoin has an effect of de-fiatization in the global financial market. When there is a higher divergence in monetary policy between the US and China, Bitcoin attracts greater attention with a higher price, posing a competing force against USD. When there are greater fluctuations in the exchange rate of USD/CNY, Bitcoin diverts investors from CNY. The fiat currencies of the two largest economies are both losers while Bitcoin gains. Therefore, cryptocurrency not only decentralizes the role of commercial banks as a medium of payment, but also decentralizes the role of central banks as a monetary policymaker. In face of this challenge, it is suggested that central banks should embrace blockchain technology and develop their own digital currency to restore the trust lost in the global financial crisis. International collaborations in terms of regulation are necessary given its borderless and authority-less feature.

Connectedness across commodities, stocks, exchange rates and bonds markets in Africa: the Covid-19 pandemic case

Connectedness across commodities, stocks, exchange rates and bonds markets in Africa: the Covid-19 pandemic case

Co-movement between carbon emissions and forex market: A tale of COVID-19 outbreak and Russia-Ukraine invasion

Carbon emissions, a fast-moving environmental problem, is truly becoming an overarching international financial sensation. The carbon emission challenges faced during the COVID-19 and Russia Ukraine War adversely affected forex market. This paper attempts to unravel the frequency co-movement of carbon emissions with forex market. We consider daily observation of carbon emissions (CE) and forex market proxied by Euro (EUR), Japanese Yen (JPY), British Pound Sterling (GBP), Australian Dollar (AUD) and Canadian Dollar (CAD) against United States Dollar (USD). These currencies exchange rates are analyzed since their volatility and trading have been particularly high. We collect daily observation of these markets extending form December 31, 2019 to February 10, 2023 and employ wavelet analysis for the empirical computation. The result unfolds that there is a strong and positive co-movement between carbon emissions and exchange rate during the COVID-19 period in the long run for CAD, AUD, GBP and weak for EUR and JPY. However, JPY is in anti-phase relationship with carbon emission in 128–256 frequencies. In addition, JPY leads the relationship during COVID-19 outbreak and Russia-Ukraine invasion respectively. This study ventures to offer insight to the policy analyst, investors and portfolio managers.

Bitcoin halving and the integration of cryptocurrency and forex markets: An analysis of the higher-order moment spillovers

This paper introduces a flexible semi-nonparametric model, the so-called SNP-VSK model, to analyze the higher-order moment interactions among cryptocurrencies and the forex markets, i.e., accounting for conditional volatility, skewness, and kurtosis spillovers. These transmissions among cryptos are identified considering Bitcoin halving as a factor of price uncertainty. To study the degree of integration of cryptocurrency markets in the economy, spillover effects with the forex market are considered, showing that Bitcoin and Ethereum are the prime receivers of positive volatility transmission, a sign of integration with the currency markets, but also potential sources of financial instability.

Deep learning-based predictive models for forex market trends: Practical implementation and performance evaluation.

In recent years, there has been growing interest in the prediction of financial market trends, due to its potential applications in the real world. Unlike traditional investment avenues such as the stock market, the foreign exchange (Forex) market revolves around two primary types of orders that correspond with the market's direction: upward and downward. Consequently, forecasting the behaviour of the Forex behaviour market can be simplified into a binary classification problem to streamline its complexity. Despite the significant enhancements and improvements in performance seen in recent proposed predictive models for the forex market, driven by the advancement of deep learning in various domains, it remains imperative to approach these models with careful consideration of best practices and real-world applications. Currently, only a limited number of papers have been dedicated to this area. This article aims to bridge this gap by proposing a practical implementation of deep learning-based predictive models that perform well for real-world trading activities. These predictive mechanisms can help traders in minimising budget losses and anticipate future risks. Furthermore, the paper emphasises the importance of focussing on return profit as the evaluation metric, rather than accuracy. Extensive experimental studies conducted on realistic Yahoo Finance data sets validate the effectiveness of our implemented prediction mechanisms. Furthermore, empirical evidence suggests that employing the use of three-value labels yields superior accuracy performance compared to traditional two-value labels, as it helps reduce the number of orders placed.