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In this article, we examine whether incorporating complexity measures as features in deep learning (DL) algorithms enhances their accuracy in predicting forex market volatility. Our approach involved the gradual integration of complexity measures alongside traditional features to determine whether their inclusion would provide additional information that improved the model's predictive accuracy. For our analyses, we employed recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) as DL model architectures, while using the Hurst exponent and fuzzy entropy as complexity measures. All analyses were conducted on intraday data from four highly liquid currency pairs, with volatility estimated using the Range-Based estimator. Our findings indicated that the inclusion of complexity measures as features significantly enhanced the accuracy of DL models in predicting volatility. In achieving this, we contribute to a relatively unexplored area of research, as this is the first instance of such an approach being applied to the prediction of forex market volatility. Additionally, we conducted a comparative analysis of the three models' performance, revealing that the LSTM and GRU models consistently demonstrated a superior accuracy. Finally, our findings also have practical implications, as they may assist risk managers and policymakers in forecasting volatility in the forex market. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between India’s Stock Market, Forex Market, Oil Market, and Gold Market to capture interdependencies addressing both direct and indirect market linkages. The study provides new insights into the interdependencies of key markets within India, an emerging economic powerhouse and significant oil consumer. For the same, our analysis is divided into two parts. Part 1 is based on daily data, where we explore the long-run relationships from 1st January 2000 to 15th December 2023 among the markets (oil, stock, gold, and forex) using the Vector Error Correction Model. Further, monthly data from January 2000 to December 2023 (Part-2) has been employed to analyse market mechanisms through intermediary variables. We use data from 10 macroeconomic variables: Bombay Stock Exchange (BSE) Prices, INR/USD Exchange Rate, Consumer Price Index, Bank Interest Rate, 91-day Risk-free Government Bond Yield, Global WTI Oil Prices, WTI Futures, National Gold Prices, and India’s and China’s Imports and utilised Simultaneous Equation Models. As a major oil refiner, Indias deepening integration into global economy highligjts its markets' role in shaping international strategies and promoting insights into resilience and interdependencies amid uncertainty. Oil prices (WTI) are positively associated to WTI futures, reflecting the forward-looking nature, while gold shows a weak negative, though statistically insiginificant correlation with WTI, indicating minimal direct impact.

This paper examines the relationship between India's Stock Market, Forex Market, Oil Market, and Gold Market to capture interdependencies addressing both direct and indirect market linkages. The study provides new insights into the interdependencies of key markets within India, an emerging economic powerhouse and significant oil consumer. For the same, our analysis is divided into two parts. Part 1 is based on daily data, where we explore the long-run relationships from 1st January 2000 to 15th December 2023 among the markets (oil, stock, gold, and forex) using the Vector Error Correction Model. Further, monthly data from January 2000 to December 2023 (Part-2) has been employed to analyse market mechanisms through intermediary variables. We use data from 10 macroeconomic variables: Bombay Stock Exchange (BSE) Prices, INR/USD Exchange Rate, Consumer Price Index, Bank Interest Rate, 91-day Risk-free Government Bond Yield, Global WTI Oil Prices, WTI Futures, National Gold Prices, and India's and China's Imports and utilised Simultaneous Equation Models. As a major oil refiner, Indias deepening integration into global economy highligjts its markets' role in shaping international strategies and promoting insights into resilience and interdependencies amid uncertainty. Oil prices (WTI) are positively associated to WTI futures, reflecting the forward-looking nature, while gold shows a weak negative, though statistically insiginificant correlation with WTI, indicating minimal direct impact. Impact Statement: This research provides a comprehensive analysis of the interdependencies among India's stock, forex, oil, and gold markets, shedding light on their dynamic relationships and resilience in the context of a rapidly globalizing economy. The findings not only enhance our understanding of market interconnections in one of the world's largest oil consumers but also offer critical insights for policymakers, investors, and global economic strategists navigating uncertainty and volatility in interconnected markets. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had an unprecedented impact on the global economy and financial markets. In this article, we explore the impact of the pandemic on the weak-form efficiency of the cryptocurrency and forex markets by conducting a comprehensive comparative analysis of the two markets. To estimate the weak-form of market efficiency, we utilize the asymmetric market deficiency measure (MDM) derived using the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach, along with fuzzy entropy, Tsallis entropy, and Fisher information. Initially, we analyze the temporal evolution of these four measures using overlapping sliding windows. Subsequently, we assess both the mean value and variance of the distribution for each measure and currency in two distinct time periods: before and during the pandemic. Our findings reveal distinct shifts in efficiency before and during the COVID-19 pandemic. Specifically, there was a clear increase in the weak-form inefficiency of traditional currencies during the pandemic. Among cryptocurrencies, BTC stands out for its behavior, which resembles that of traditional currencies. Moreover, our results underscore the significant impact of COVID-19 on weak-form market efficiency during both upward and downward market movements. These findings could be useful for investors, portfolio managers, and policy makers. [ABSTRACT FROM AUTHOR]

The role of the G20 in global governance has been increasingly prominent in the context of the extensive spread of coronavirus disease 2019 and the aggravation of financial risk contagion. Detecting the risk spillovers among the G20 FOREX markets is crucial to maintain financial stability. Therefore, this paper first adopts a multi-scale approach to measure the risk spillovers among the G20 FOREX markets from 2000 to 2022. Furthermore, the key markets, the transmission mechanism, and the dynamic evolution are researched based on the network analysis. We derive the following findings: (1) The magnitude and volatility of the total risk spillover index of the G20 countries are highly associated with extreme global events. (2) The magnitude and volatility of risk spillovers among the G20 countries are asymmetric in the different extreme global events. (3) The key markets in the risk spillover process are identified, and the USA always occupies a core position in the G20 FOREX risk spillover networks. (4) In the core clique, the risk spillover effect is obviously high. In the clique hierarchy, as the risk spillover effect is transmitted downward, the risk spillovers present the decrease trends. (5) The density, transmission, reciprocity, and clustering degrees in the G20 risk spillover network during the COVID-19 period are much higher than that in other periods. [ABSTRACT FROM AUTHOR]

Globalization and financial liberalization have made market participants and policymakers ponder over the importance of financial interconnectedness and shock spillovers. This discussion is in line with the International Portfolio Theory & Diversification. In this context, this study investigates the intensity and direction of return and volatility spillovers of foreign exchange (FX) markets of Pakistan and its major trading partners. Using an innovative research technique by Diebold and Yilmaz (2009, 2012), we separately compute measures of return and volatility spillovers. Furthermore, we also calculate total spillover, directional spillover, and net spillover indices using a daily data set over the period 1995 to 2019. To capture secular and cyclical movements in the trading partners' FX markets, we rely upon a rolling window analysis. Our results, based on the spillovers indices, support the presence of dynamic connectedness among currency pairs of the major trading partners. We also note that among the sample economies, the USA, the EU, Singapore, and Malaysia are the main sources and originators of shocks spillover while Pakistan, India, Japan, Kuwait, Singapore, and UAE are net shock receivers. The rolling window analysis indicates that returns and volatility spillovers intensify during the phases of financial or economic anxiety. These results have some important implications for individuals working on risk management, portfolio diversification, and trading strategies. [ABSTRACT FROM AUTHOR]

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.

The objective of this research is to examine how sentiment analysis can be employed to generate trading signals for the Foreign Exchange (Forex) market. The author assessed sentiment in social media posts and news articles pertaining to the United States Dollar (USD) using a combination of methods: lexicon-based analysis and the Naive Bayes machine learning algorithm. The findings indicate that sentiment analysis proves valuable in forecasting market movements and devising trading signals. Notably, its effectiveness is consistent across different market conditions. The author concludes that by analyzing sentiment expressed in news and social media, traders can glean insights into prevailing market sentiments towards the USD and other pertinent countries, thereby aiding trading decision-making. This study underscores the importance of weaving sentiment analysis into trading strategies as a pivotal tool for predicting market dynamics.

AbstractRedenomination of currency has become a common phenomenon in recent past among emerging and transitional economies. In 2007, Ghana became one of the economies to redenominate in recent past. This currency policy adaptation has the potential of triggering certain individual behavioral biases on the forex market. This study provides evidence that currency biases that accompanied Ghana’s currency reform adaptation in 2007 contribute to its forex market price (exchange rate) fluctuations. Using data from 1980 to 2018 with some estimated biases and some selected macroeconomic fundamentals as covariates in an ANCOVA (Analysis of Covariance) model, the study revealed that estimated biases which were induced as a result of currency reform adaptation impact positively and significantly on Ghana’s forex market prices. It is therefore recommended that policy makers, political leaders and stakeholders begin to look at human factors that may exist in the forex market and incorporate this information into future plans in addressing issues relating to forex market fluctuations.

Redenomination of currency has become a common phenomenon in recent past among emerging and transitional economies. In 2007, Ghana became one of the economies to redenominate in recent past. This currency policy adaptation has the potential of triggering certain individual behavioral biases on the forex market. This study provides evidence that currency biases that accompanied Ghana's currency reform adaptation in 2007 contribute to its forex market price (exchange rate) fluctuations. Using data from 1980 to 2018 with some estimated biases and some selected macroeconomic fundamentals as covariates in an ANCOVA (Analysis of Covariance) model, the study revealed that estimated biases which were induced as a result of currency reform adaptation impact positively and significantly on Ghana's forex market prices. It is therefore recommended that policy makers, political leaders and stakeholders begin to look at human factors that may exist in the forex market and incorporate this information into future plans in addressing issues relating to forex market fluctuations. [ABSTRACT FROM AUTHOR]

This article investigates the dynamical complexity and fractal characteristics changes of the Bitcoin/US dollar (BTC/USD) and Euro/US dollar (EUR/USD) returns in the period before and after the outbreak of the COVID-19 pandemic. More specifically, we applied the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method to investigate the temporal evolution of the asymmetric multifractal spectrum parameters. In addition, we examined the temporal evolution of Fuzzy entropy, non-extensive Tsallis entropy, Shannon entropy, and Fisher information. Our research was motivated to contribute to the comprehension of the pandemic's impact and the possible changes it caused in two currencies that play a key role in the modern financial system. Our results revealed that for the overall trend both before and after the outbreak of the pandemic, the BTC/USD returns exhibited persistent behavior while the EUR/USD returns exhibited anti-persistent behavior. Additionally, after the outbreak of COVID-19, there was an increase in the degree of multifractality, a dominance of large fluctuations, as well as a sharp decrease of the complexity (i.e., increase of the order and information content and decrease of randomness) of both BTC/USD and EUR/USD returns. The World Health Organization ( W H O ) announcement, in which COVID-19 was declared a global pandemic, appears to have had a significant impact on the sudden change in complexity. Our findings can help both investors and risk managers, as well as policymakers, to formulate a comprehensive response to the occurrence of such external events. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Decision support and trading systems for the forex market mostly derive a single signal for the decision-maker. This is so, because instruments are evaluated based on a single criterion, which creates a ranking of instruments, from which the best one is selected. At the same time, one can observe a lack of tools allowing one to derive the set of non-dominated trading opportunities considered in the multicriteria space. This article focuses on multicriteria analysis, in which several different market indicators describe a single instrument on the forex market (currency pair), leading to definite criteria. Thus, for a given time horizon, we consider a set of currency pairs described by a group of technical market indicators in every trading session. However, instead of deriving crisp information, based on the buy-no buy binary logic, we use concepts from the fuzzy sets theory, in which each criterion for a single variant takes a value from the 〈0, 1〉 interval. We select only the non-dominated variants from such a set, which will be used as elements of the portfolio of currency pairs on the forex market. We test our idea on the real-world data covering more than ten years, several technical market indicators, and over twenty different currency pairs. The preliminary results show that the proposed idea can be treated as a promising concept for deriving a portfolio of currency pairs instead of focusing on only a single currency pair. [ABSTRACT FROM AUTHOR]

In the current complex financial world, paper currencies are vulnerable and unsustainable due to many factors such as current account deficit, gold reserves, dollar reserves, political stability, security, the presence of war in the region, etc. The vulnerabilities not limited to the above, result in fluctuation and instability in the currency values. Considering the devaluation of some Asian countries such as Pakistan, Sri Lanka, Türkiye, and Ukraine, there is a current tendency of some countries to look beyond the SWIFT system. It is not feasible to have reserves in only one currency, and thus, forex markets are likely to have significant growth in their volumes. In this research, we consider this challenge to work on having sustainable forex reserves in multiple world currencies. This research is aimed to overcome their vulnerabilities and, instead, exploit their volatile nature to attain sustainability in forex reserves. In this regard, we work to formulate this problem and propose a forex investment strategy inspired by gradient ascent optimization, a robust iterative optimization algorithm. The dynamic nature of the forex market led us to the formulation and development of the instantaneous stochastic gradient ascent method. Contrary to the conventional gradient ascent optimization, which considers the whole population or its sample, the proposed instantaneous stochastic gradient ascent (ISGA) optimization considers only the next time instance to update the investment strategy. We employed the proposed forex investment strategy on forex data containing one-year multiple currencies' values, and the results are quite profitable as compared to the conventional investment strategies. [ABSTRACT FROM AUTHOR]

This study evaluates the behaviour of the forex market during the first and the second wave of COVID-19. We have analysed the behaviour of exchange rates of CNY, JPY, CHF, and GBP in response to daily cases of COVID-19 and daily deaths, using Continuous Wavelet Transform and Wavelet Transform Coherence. The results show that the second wave has been more aggressive. The relationship of new cases and deaths has been more significant and negative with the exchange rates during the second wave of COVID-19. The currencies that are considered safe havens are severely affected by COVID-19 during the second wave. [ABSTRACT FROM AUTHOR]

In order to understand the impact of Covid-19 on different economies, this paper tries to underline the effects of the Covid-19 pandemic over some of the most traded currencies. For this purpose, a period of 3 years has been taken into consideration and the data was analyzed with the help of MetaTrader platform. The findings indicate a correlation between the pandemic and the volatility of the analyzed exchange rates. The results indicate that the observed pairs where affected negatively by the pandemic crisis. Thus, the effect of the pandemic is not only on a short time, but the effects will also be seen on a longer period of time.

This paper proposed a short-term two-stage hybrid algorithmic framework for trade and trend analysis of the Forex market by augmenting the currency pair datasets with transformed attributes using a few technical indicators and statistical measures. In the first phase, an optimized deep predictive coding network (DPCN) based on a meta-heuristic reptile search algorithm (RSA) inspired by the intelligent hunting activities of the crocodiles is exploited to develop this RSA-DPCN predictive model. The proposed model has been compared with optimized versions of extreme learning machine (ELM) and functional link artificial neural network (FLANN) with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) along with the RSA optimizers. The performance of this model has been evaluated and validated through several statistical tests. In the second phase, the up and down trends are analyzed using the Higher Highs Higher Lows, and Lower Highs Lower Lows (HHs/HLs and LHs/LLs) trend analysis tool. Further, the observed trends are compared with the actual trends observed on the exchange price of real datasets. This study shows that the proposed RSA-DPCN model accurately predicts the exchange price. At the same time, it provides a well-structured platform to discern the directions of the market trends and thereby guides in finding the entry and exit points of the Forex market. [ABSTRACT FROM AUTHOR]

This paper applies recursive right-tailed unit root tests to detect bubble activity for Turkish Lira against financially most-traded five currencies (i.e., the US Dollar (USD/TRY), the British pound (GBP/TRY), the Euro (EUR/TRY), the Chinese Yuan (CNY/TRY) and the Russian Ruble (RUB/TRY)) over January 2, 2015 to February 12, 2021. It can be identified from the Supremum Augmented Dickey–Fuller (SADF) and the Generalized Supremum Augmented Dickey-Fuller (GSADF) tests statistics that there is a high degree of evidence of bubble activity which characterizes all five exchange rates both in the full-sample period and in the sub-periods, including the pre-COVID-19 era (January 2, 2015 to November 15, 2019) and the COVID-19 era (November 18, 2019 to February 12, 2021). The empirical results also indicate that positive bubbles are common for each selected exchange rate and the multiple bubbles were intensified during the COVID-19 period, referring that forex markets became relatively more inefficient compared to the pre-COVID-19 period.

This paper presents a method for creating Forex market predictive models using multi-agent and fuzzy systems, which have the objective of simulating the interactions that provoke changes in the price. Agents in the system represent traders performing buy and sell orders in a market, and fuzzy systems are used to model the rules followed by traders performing trades in a live market and intuitionistic fuzzy logic to model their decisions’ indeterminacy. We use functions to restrict the agents’ decisions, which make the agents become specialized at particular market conditions. These “specialization” functions use the grades of membership obtained from an agent’s fuzzy system and thresholds obtained from training data sets, to determine if that agent is specialized enough to handle a market’s current conditions. We have performed experiments and compared against the state of the art. Results demonstrate that our method obtains predictive errors (using mean absolute error) that are in the same order of magnitude than those errors obtained by models generated using deep learning and models generated by random forest, AdaBoost, XGBoost, and support-vector machines. Furthermore, we performed experiments that show that identifying specialized agents yields better results.

Volatility in financial markets is a highly explored area of research for the last few decades. Possible reasons for high concentration on the markets are its unexplained and unexplored sources. The present study aims to check certain macroeconomic variables as determinants of financial markets (stock market and exchange rate) volatility. It also aims to analyse the contribution of the volatility of one financial market to the volatility of another financial market before and after the financial crises. The analysis is conducted using two types of data sets from 27 European countries. The study employed A.R. (k)-EGARCH (p, q) models to measure financial markets volatility. The study finds no significant interlink effects among volatilities of stock market returns and volatility of exchange rate returns after the financial crises. However, the increase in volatility in one market caused an increase in the other market’s volatility before the financial crises. Further, results also revealed that macroeconomic variables affect volatilities in these markets differently before and after the financial crises. The study recommends that the macroeconomic policies for stability in these markets cannot coincide as they differ in their impacts in different markets.

This paper applies recursive right-tailed unit root tests to detect bubble activity for Turkish Lira against financially most-traded five currencies (i.e., the US Dollar (USD/TRY), the British pound (GBP/TRY), the Euro (EUR/TRY), the Chinese Yuan (CNY/TRY) and the Russian Ruble (RUB/TRY)) over January 2, 2015 to February 12, 2021. It can be identified from the Supremum Augmented Dickey-Fuller (SADF) and the Generalized Supremum Augmented Dickey-Fuller (GSADF) tests statistics that there is a high degree of evidence of bubble activity which characterizes all five exchange rates both in the full-sample period and in the sub-periods, including the pre-COVID-19 era (January 2, 2015 to November 15, 2019) and the COVID-19 era (November 18, 2019 to February 12, 2021). The empirical results also indicate that positive bubbles are common for each selected exchange rate and the multiple bubbles were intensified during the COVID-19 period, referring that forex markets became relatively more inefficient compared to the pre-COVID-19 period. [ABSTRACT FROM AUTHOR]

The article deals with foreign jurisdictions on the subject of regulation of the retail Forex market. The analysis of the activity of the state agencies for the stable functioning of the OTC Forex market is carried out.

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., Competing Interests: Data availability and accessData available within the article or its supplementary materials. The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

An accurate exchange rate forecasting and its decision-making to buy or sell are critical issues in the Forex market. Short-term currency rate forecasting is a challenging task due to its inherent characteristics, which include high volatility, trend, noise, and market shocks. We propose a novel deep learning architecture consisting of an adaptive activation function selection mechanism to achieve higher predictive accuracy. The proposed architecture is composed of seven neural networks that have different activation functions as well as softmax layer and multiplication layer with a skip connection, which are used to generate the dynamic importance weights that decide which activation function is preferred. In addition, we introduce an extended Min-Max smoothing technique to further normalize financial time series that have non-stationary properties. In our experimental evaluation, the results showed that our proposed model not only outperforms deep neural network baselines but also other classic machine learning approaches. The extended Min-Max smoothing technique is step towards forecasting non-stationary financial time series with deep neural networks.

In order to understand the impact of Covid-19 on different economies, this paper tries to underline the effects of the Covid-19 pandemic over some of the most traded currencies. For this purpose, a period of 3 years has been taken into consideration and the data was analyzed with the help of MetaTrader platform. The findings indicate a correlation between the pandemic and the volatility of the analyzed exchange rates. The results indicate that the observed pairs where affected negatively by the pandemic crisis. Thus, the effect of the pandemic is not only on a short time, but the effects will also be seen on a longer period of time. [ABSTRACT FROM AUTHOR]

The paper investigates the bullish and the bearish engulfing patterns in the forex spot market. We scanned over 112,792 in-sample daily candles and 148,992 out-of-sample four-hour candles and used more than three million spot quote observations among 24 currency pairs from 2000 to 2018. The findings are of great interest. First, we document the significance of profitability of technical analysis in the forex market, particularly for the seven majors. This presumably lends support to the inefficiency of the forex market. Second, we document the significance importance of transactions costs. Third, we document the superiority of the American dollar over the other major currencies. [ABSTRACT FROM AUTHOR]

A new monetary theory is set out to resolve the "uncovered interest parity (UIP)" puzzle. It explores the possibility that liquidity properties of money and nominal bonds can account for the puzzle. A key concept in our model is that nominal bonds carry liquidity premia. We show that the UIP can fail to hold under the economic environment where collateral pledgeability and/or liquidity of nominal bonds and/or collateralized credit‐based transactions are relatively bigger. Our liquidity‐based theory can help understanding many empirical observations that risk‐based explanations find difficult to reconcile with. [ABSTRACT FROM AUTHOR]

Volatility in financial markets is a highly explored area of research for the last few decades. Possible reasons for high concentration on the markets are its unexplained and unexplored sources. The present study aims to check certain macroeconomic variables as determinants of financial markets (stock market and exchange rate) volatility. It also aims to analyse the contribution of the volatility of one financial market to the volatility of another financial market before and after the financial crises. The analysis is conducted using two types of data sets from 27 European countries. The study employed A.R. (k)-EGARCH (p, q) models to measure financial markets volatility. The study finds no significant interlink effects among volatilities of stock market returns and volatility of exchange rate returns after the financial crises. However, the increase in volatility in one market caused an increase in the other market's volatility before the financial crises. Further, results also revealed that macroeconomic variables affect volatilities in these markets differently before and after the financial crises. The study recommends that the macroeconomic policies for stability in these markets cannot coincide as they differ in their impacts in different markets. [ABSTRACT FROM AUTHOR]

Traders who instantly react to changes in the financial market and place orders in milliseconds are called high-frequency traders (HFTs). HFTs have recently become more prevalent and attracting attention in the study of market microstructures. In this study, we used data to track the order history of individual HFTs in the USD/JPY forex market to reveal how individual HFTs interact with the order book and what strategies they use to place their limit orders. Specifically, we introduced an 8-dimensional multivariate Hawkes process that included the excitations due to the occurrence of limit orders, cancel orders, and executions in the order book change, and performed maximum likelihood estimations of the limit order processes for 134 HFTs. As a result, we found that the limit order generation processes of 104 of the 134 HFTs were modeled by a multivariate Hawkes process. In this analysis of the EBS market, the HFTs whose strategies were modeled by the Hawkes process were categorized into three groups according to their excitation mechanisms: (1) those excited by executions; (2) those that were excited by the occurrences or cancellations of limit orders; and (3) those that were excited by their own orders.

We examine 112,792 daily candles using more than one million spot quotes among 24 currency pairs between 2000 and 2018. We find that chart patterns are profitable. Relying on these visually based patterns achieves returns of more than 600% after accounting for the transaction costs. Nevertheless, the transaction costs are substantial. In particular, the spread is a large burden on profitability. Overall, our evidence suggests that technical analysis could generate excess returns and that the profitability of technical analysis cannot be explained by market inefficiency. Rather, the evidence is consistent with that on the link between the efficiency and profitability of technical analysis.