Forex Market Research Articles

Revolutionizing Foreign Exchange Market: A Critical Analysis of Blockchain's Opportunities and Challenges

The fast use of blockchain technology, cryptocurrencies, and electronic trading has had a profound influence on the world's foreign exchange market, creating both difficulties and potential for market regulation and exploitation. Blockchain has the capability to completely transform the foreign exchange business by bringing improved transparency, efficiency, and security. Blockchain allows for the construction of transparent, tamper-proof transaction records since it uses distributed ledger technology. Lately, Blockchain technology and Forex trading have experienced tremendous growth in popularity in recent years, but it's important to remember that they also include hazards that investors need to be aware of. The goal of this study is to determine how blockchain technology might be applied to boost the Forex market, highlighting the advantages of decentralization, efficiency, security, and transparency. This study aims to explore the potential contribution of blockchain technology to the foreign exchange market. The research employs a literature review and analysis approach to investigate the relationship between blockchain technology and the foreign exchange market. The findings of the study indicate that blockchain technology has the potential to revolutionize the foreign exchange market. The paper concludes that the use of distributed ledger technology enables the creation of transparent and tamper-proof transaction records, addressing existing challenges of transparency and security in the Forex market.

A multi-model approach to the development of algorithmic trading systems for the Forex market

In the decade passed, considerable affords were made to develop effective trading systems based on different assumptions concerned with the market nature, methods for data processing and uncertainty modeling. Such systems are often so sophisticated that they can be applied only by their authors. Another limitation of them is concerned with the focus on the development of a universal single best model. Besides, any model works well only in limited time periods and fails when noticeable changes in the market behavior occur. Then a major revision or the development of a new model is inevitable. Unfortunately, usually this needs too much time. Therefore, in this paper, to avoid the above problems, the simple multi-model approach to the development of trading systems in the Forex market is proposed. It is based on some working hypotheses, which are justified in this paper. The first of them is based on the observation that the Forex is the aggregation of numerous streams (strategies) provided by the broad trades community. Therefore, we can expect that even a very simple model based on the particular trading idea or ideas may catch such a string to be profitable, at least during a small period. If we have developed a set of such simple models optimized for different currency pairs, in each trading period we can use the model providing maximal profit for a certain currency pair. The profitability of the proposed approach is illustrated by the trading results obtained on the symbols EURUSD,GBPUSD, AUDUSD and USDJPY for the timeframes H1 and H4 with the use of the Meta Trader 4 platform.

Comparison of Selected Trading Strategies in The Forex Market

Comparison of Selected Trading Strategies in The Forex Market

Stock and Currency Market Linkages: An Empirical Analysis from Emerging Economies

Purpose: The Causality relationship between forex and stock market in any economy is dependent on its economic fundamentals. This study examines return and volatility linkages between stock and currency markets for 28 emerging economies weekly MSCI stock index values (in local currency) and foreign exchange rates (indirect quotes) from 1988 to 2019. Theoretical Framework: The understanding of the relationship between forex and stock markets through return and volatility spillover will help in predicting behavior of one market on account of the knowledge of movements in another market. Impact of global financial crisis (GFC) on this relationship is another dimension of research. This study finds the causal relationship between forex and stock markets through return and volatility spillover for all emerging economies along with the effect of GFC on the relationship. Design/Methodology/Approach: The empirical analysis is conducted for the total period and three sub-periods namely pre-global financial crisis, crisis, and post-crisis periods by using the Granger Causality test (Granger 1969) followed by Vector Auto Regression (VAR) model and finally the Dynamic Conditional Correlations (DCC), a multivariate model proposed by Engle (2002). The volatility linkages are studied by employing BEKK-GARCH (Baba, Engle, Kraft, & Kroner, 1990) Findings: It is found that return spillovers are predominantly from Stock to forex markets during the pre-crisis and crisis period but from forex to stock market in the post-crisis period. The increasing presence of return relationships from 10 countries in the pre-crisis period to 19 countries during the crisis period, implying a contagion effect. The BEKK-GARCH result confirm that volatility spillovers are observed throughout from forex to stock markets. Research, practical & social implications: More extensive return and volatility associations between stock and forex market after the global financial crisis confirm the increasing importance of economic fundamentals. Return linkages exhibit contagion against the decoupling effect observed in volatility spillovers during the crisis period. Originality/Value: Based on empirical observations, the study attempts to provide important policy implications for Policy makers, global investors, and academic community.

A new way of measuring effects of financial crisis on contagion in currency markets

Contagion is an extremely important topic in finance. Contagion is at the core of most major financial crises, in particular the global financial crisis that started in 2007. Although various approaches to quantifying contagion have been proposed, many of them lack a causal interpretation. We will present a new measure for contagion among individual currencies within the Foreign exchange market and show how the paths of contagion work within the Forex market using causal inference. This approach will allow us to pinpoint sources of contagion and to find which currencies offer good options for diversification and which are more susceptible to systemic risk, ultimately resulting in feedback on the level of global systemic risk. In particular, we will focus on the effects of the Covid-19 global pandemic.

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

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.

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

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.

Do positive and negative variations in stock indexes lead to depreciation in local currencies? A beyond symmetrical evidence from ASEAN-5 region

Preliminary studies overlooked the importance of the financial crisis in the asymmetric transmission of financial market shocks to FX market returns. Moreover, existing studies also concentrated on the response of aggregate equity market returns to the forex market returns rather than the vice versa asymmetric effect for the ASEAN-5 region. We utilised the panel based ARDL and NARDL framework using pooled mean group method for conducting this study. There are 415 observations spanning the pre-crisis period of January 2001 to December 2007. Furthermore, 595 and 1135 observations are taken into consideration when post-recessionary, and overall sampling periods of January 2010 to December 2019 and January 2001 to December 2019 are considered, correspondingly. Overall, the findings indicated that in the short run, only negative equity market returns caused depreciation in the local currencies of ASEAN-5 member countries during the pre-crisis period, whereas only positive shocks during the post-crisis regime appreciated the local currencies of ASEAN-5 member countries. Furthermore, only longer-term negative financial market shocks contribute to post-crisis local currency deflation in the ASEAN-5 member nations. This demonstrates that investors and exporters must consider the importance of the particulate crisis period when formulating forward currency arrangements.

Political-economic uncertainty and the price of Bitcoin in the forex Market Between 2010 and 2020

Este documento de investigación tuvo como propósito definir si existe correlación de algún nivel entre la variable Incertidumbre política-económica mundial y la variable precio del Bitcoin en dólares desde el año 2010 hasta el año 2020 en el mercado Forex GDAX. Este trabajo de investigación es tipo básico, con enfoque de tipo cuantitativo y el diseño del mismo no es experimental. Los datos fueron tomados de la base de datos de la página web de investing y Economic policy uncertainty, Los resultados muestran que en época de incertidumbre política económica, los agentes financieros buscan adquirir la criptodivisa como activo de cobertura, Por otro lado, concluimos en esta investigación, que la variable Incertidumbre política Económica tiene correlación positiva fuerte, con el precio de la criptomoneda Bitcoin en el mercado Forex GDAX, entre el año 2010 y 2020, con un coeficiente de Pearson 0.662 (P = 0.0, Sig =0,01 ).

Plan motivacional para mejorar el desempeño laboral de los trabajadores del Hospital Santa Gema de Yurimaguas.

La tesis se titula plan motivacional para mejorar el desempeño laboral de los trabajadores del Hospital Santa Gema de Yurimaguas, y es de nivel preexperimental, con el enfoque cuantitativo, siendo el problema el de determinar en qué medida el Plan Motivacional mejora el Desempeño Laboral de los trabajadores del Hospital Santa Gema de Yurimaguas. El instrumento utilizado fue la Ficha de Observación, que se aplicó como prueba de entrada y prueba de salida a una muestra de 16 trabajadores. Los principales resultados que podemos mencionar es el incremento en el Desempeño Laboral del 33% respecto de la prueba de salida con la prueba de entrada. Por lo que se llegó a la conclusión de que el Plan Motivacional mejoró significativamente en un 33% el Desempeño Laboral de los trabajadores del Hospital Santa Gema de Yurimaguas – Loreto 2013. Lo que nos demuestra que antes del Plan Motivacional, los trabajadores no se comprometían con su trabajo.

The sustainability of current account in the BRICS countries depends on economic policies’ support to structural adaptation

This study undertakes a descriptively comprehensive and statistically robust assessment of the sustainability of current account (CA) and solvency of intertemporal budget constraint (IBC) in the BRICS countries. The long-run model estimated in one-regime setting with no structural break and in sample-split setting with multiple structural breaks provides dominant support to the sustainability of CA and solvency of IBC. The policy makers in the countries with surpluses in CA, such as China and Russia, need to manage upward pressure on domestic currency, encourage domestic investment, and seek opportunities for outward FDI. The central bank interventions in FOREX market, accompanied by both short-term portfolio and long-term direct investments overseas, would help relieve upward pressure on domestic currency and make productive use of foreign exchange reserves. The policy focus in the countries with deficits in CA, such as Brazil, India and South Africa, should be to accelerate productivity, boost exports, bolster saving, and implement fiscal consolidation to ensure the sustainability of CA and solvency of IBC.

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

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.

An algorithmic trading system based on a stacked generalization model and hidden Markov model in the foreign exchange market

The Forex market has been one of the most attractive markets to researchers, funds, and traders. Literature shows that a single model algorithm usually cannot perform satisfactorily on the foreign exchange (Forex) time series because of the market's complexity. This study develops an algorithm based on two stacked generalization models consisting of four machine-learning models. First, the optimal lags of features are found using the Fisher discriminant ratio and partial autocorrelation function. Second, one stacked model fits the bullish trends, and the other holds the bearish trends resulting from a hidden Markov model. We reinforce the predictive signals of these models by extracting relationships between currency pairs with correlation and mutual information. Lastly, the proposed algorithm constructs a portfolio based on the strength of signals dependent on correlation and mutual information. As a result, this paper compares the performance of the proposed approach with different states of the model and several established benchmarks regarding return and risk metrics. The outcomes show that the proposed model's added features—such as time series clustering, considering a range of inputs, and internal relationships among different assets—can increase its performance in the Forex market.

Hybrid modified weighted water cycle algorithm and Deep Analytic Network for forecasting and trend detection of forex market indices

This paper presents forecasting and trend analysis of foreign currency exchange rate in financial market using a hybrid Deep Analytic Network (DAN) technique optimized by a modified water cycle algorithm called Weighted WCA (WWCA) with better generalization capability than the traditional WCA.DAN comprises several stacked KRR (Kernel Ridge Regression) Auto encoders in a multilayer nonlinear regression architecture approach that provides better generalization and accuracy using regularized least squares technique. Further DAN using wavelet kernel function is particularly attractive for its strong data fitting and generalization ability along with its simplified execution procedure, high speed, and better performance achievements in comparison to LSSVM (least squares support vector machine). The output from the DAN is fed to a weighted KRR module to reject noise or the outliers in the noisy data and to make DAN a more robust predictor of the Forex markets, To obtain optimal values of wavelet kernel parameters, a modified metaheuristic water cycle algorithm i.e. the proposed WWCA is utilized. Applications of this new approach to predict forex rate along with trend analysis on three stock markets provide successful results and validate its superiority over some well known approaches like ANN, SVM, Naïve-Bayes, ELM.

A Study On Investors Awareness And Perception On Forex Market As Per Indian Demographic Profile With Reference To Visakhapatnam, Andhra Pradesh

A Study On Investors Awareness And Perception On Forex Market As Per Indian Demographic Profile With Reference To Visakhapatnam, Andhra Pradesh

Forex market as the best possible way of investing money during an economic boom and recession

Abstract Since the beginning of the 1980s, a continuous process of integration of national and regional markets into one global market for goods, services and capital can be noticed. Both economic theory and market practice indicate that the level of the exchange rate primarily depends on macroeconomic variables (such as interest rates or the number of new jobs in the non-agricultural sector). The results of the research presented in the article regard the importance of US macroeconomic data publications for the short-term volatility of EUR/USD exchange rate. The main purpose of the study was to show whether macroeconomic data from the United States affects the short-term development of the EUR/USD exchange rate and whether the Forex market is a good way to multiply capital. The following research questions have been posed: does the EUR/USD exchange rate react to the published macroeconomic data from the American economy? Second, is whether investing in the Forex market could be a way to multiply capital in times of economic boom and recession. This paper presents the effects of my own research and observations in terms of the impact of US macroeconomic data, on shaping exchange rates of the Forex market. Based on my own investment experience my goal is to prove, that Forex market is a perfect way to multiply capital. My investment decisions regarding future exchange rate fluctuations, were based on the presented macroeconomic data from the US economy, as well as on the basis of important leading economic indicators. The position was opened and closed on the same day. The trading contracts have been made throughout 7 working days. The underlying financial instruments were EUR/USD and OIL. The conclusions of this study are as follows. The USD/PLN exchange rate reacted to the published macroeconomic data from USA. The strongest exchange rate reaction was noticed after publications of data regarding US Non-farm Payrolls (NFP), Initial Jobless Claims and ISM Services PMI. Strong exchange rate reaction was recorded after ADP US Private Sector Jobs and Factory orders report. When taking described investment examples into account, it can be clearly stated that investing in the Forex market is an excellent alternative to stock investments.

A hybrid neural network and optimization algorithm for forecasting and trend detection of Forex market indices

An Autoencoder (AE) is an independent feature extractor from data samples and a deep network can be obtained by stacking several AEs. This paper presents a novel hybrid stacked Autoencoder-based Deep Kernel-based Random Vector Functional Link Network (DKRVFLN-AE) for forecasting and trend analysis of Foreign Exchange (Forex) rates. The proposed model dispenses the random choices of weights and biases, unlike the stacked Deep Random Vector Functional Link Network-AE (DRVFLN-AE), using a wavelet kernel function with strong data fitting capability based on Mercer’s condition. A modified metaheuristic Water Cycle Algorithm is used to optimize the wavelet kernel parameters and provide DKRVFLN-AE a better generalization and learning capability, faster execution speed, lower storage space, and improved accuracy to traditional deep learning Random Vector Functional Link Network and Extreme Learning Machine models. Applications of this new approach to predict exchange rates and trend analysis on three foreign exchange markets provide successful results and validate its superiority over well-known approaches like Random Vector Functional Link Networks, Support Vector Machines, Naive-Bayes, Extreme Learning Machines, and Deep Random Vector Functional Link Network.

Volatility transmitter or receiver? Investigating dynamic connectedness between the carry trade and financial markets

By examining the connectedness of carry trade currency with stock, foreign exchange (forex), and commodity markets, the paper investigates the extent to which shocks in capital flows driven by interest-rate differentials affect financial markets. Following the framework of Diebold and Yilmaz (2012), we find that dynamic spillovers alternate between positive and negative, but, during the Covid-19 pandemic, the carry trade is the main net transmitter of volatility to all markets. Our findings confirm the ability of carry trade to increase financial vulnerability and amplify market turbulence, especially in the forex market.

Sharia Business Ethics Online Forex Trading at Company Signal Providers Volatility 75 Index JR (VIXJR)

This study aims to determine the practice and application of sharia business ethics in forex trading of JR Volatility 75 Index companies in the forex market and the dissemination of the results of the analysis and transactions to registered members. This research is qualitative research with the research object of corporate signal provider Volatility 75 Index JR. Data collection methods used are observation, in-depth interviews, and documentation. The results of the study show that JR's Volatility 75 Index has implemented sharia business ethics in its service business so as to make it feel safe and comfortable when using the forex market analysis services from the company.

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

BackgroundWhen 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.ObjectiveWhile 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.MethodThis 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.ResultsThe 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.ConclusionBased 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.