The foreign exchange or forex market is the largest financial market in the world where currencies are bought and sold simultaneously. It is even larger than the stock market; According to the 2019 Triennial Central Bank Survey of FX and Over-the-Counter (OTC) derivatives markets, it has a daily volume of $6.6 trillion [7]. It is a non-centralized market that operates 24 h a day except the weekend, which makes it unique from other financial markets. Because of its high volatility, nonlinearity, and irregularity, the forex market, unlike stocks, is one of the most complex markets [1]. The traits of Forex show differences compared to the stock, bond, and other financial markets. These differences make forex traders have more trading opportunities and advantages for profitable trades. Some of these advantages include no commissions, no middlemen, no fixed lot size, low transaction costs, high liquidity, almost instantaneous transactions, low margins/high leverage, 24-h operations, no insider trading, limited regulation, and online trading opportunities [73]. In the forex market, currency pairs are traded, with the base currency being the first listed currency and the quote currency being the second. Currency pairs compare the value of one currency to another (the base currency to the quote currency). When the prices depreciate, a quote currency is bought against the base currency, which leads to profit, and when the prices elevate, the base currency is bought against the quote currency [1]. Two main types of techniques are used to forecast future values for a typical financial time series, which are fundamental analysis and technical analysis. Fundamental analysis is a method of examining economic, social, and political issues that may influence currency prices in the forex market. In contrast, technical analysis involves using historical data price chart, which provides a roadmap for past price behavior. To forecast the future, a technical analyst looks to the past. Predicting the direction of a currency pair's movement is the most important choice in Forex. Predicting currency movement correctly can bring many benefits to traders and vice versa. In past and recent years, the research community has been highly active in predicting the forex market using machine-learning models. On one hand, many verifiable types of research have been conducted with the aim of understanding and predicting currency trends in the forex market using machine-learning models. According to Zhelev and Avresky [77], the cited literature in the field of deep learning is a basic foundation for solving the challenging problem of prediction of forex price. While the research community has spent a lot of time studying the methodologies used by researchers and practitioners in the context of predictive models in the forex market, there isn't much information on how to forecast currency pair movement in the forex market using machine-learning models and Meta-Analysis. To address this gap in knowledge, we conducted a Systematic Literature Review (SLR) on the use of machine learning (ML) techniques for forex market forecasting, with the goal of (i) understanding and summarizing current algorithms and models, and (ii) analyzing its evaluation metrics and open challenges to guide future research. Our SLR is to provide a complete examination of I machine learning as it has been considered in previous research, and (ii) the training processes used to train and assess machine learning algorithms. We also give a meta-analysis of the performance of the machine learning models that have been developed so far, as judged by their assessment criteria. In addition to examining the state of the art, we critically examined the approaches that have been applied thus far.

Research Question

Motivation

1. For forecasting, what machine learning algorithm was used?

To look into the most up-to-date machine learning approaches for forecasting the FX market that has been considered so far

2. What dataset was used to train the model, the period and timeframe considered in the literature

In terms of machine learning algorithms, examine the machine learning parameters employed in previous studies. The answers to these questions will help practitioners and researchers figure out the best machine learning configuration for FX market forecasting: which training technique will produce the greatest forecasting results

3. Evaluation setup

i. What types of validation techniques were exploited?

ii. What evaluation measures were employed to get access to the prediction models?

3. Performance Meta-Analysis: using evaluation metrics of the selected studies

Examine the approaches studied to (i) validate and (ii) assess the proposed forex market forecasting models

Our objective is to conduct a systematic literature review to comprehend and summarize studies on machine learning prediction models in the forex market. It's worth noting, however, that some secondary research on machine learning algorithms and deep learning has been proposed. [18, 27, 44, 54, 59].

According to Fletcher [18] when including advanced exogenous financial information to estimate daily FX carry basket returns, committees of discriminative techniques such as Support Vector Machines (SVM), Relevance Vector Machines (RVM), and Neural Networks) perform well.

Panda et al. [44] conducted a second SLR on Exchange Rate Prediction utilizing ANN and Deep Learning Methodologies, and offered novel approaches that were distinct according to them from 2000 to 2019, for predicted exchange rate projection the effects observed during the protected period within examined are displayed using newly proposed models such as Artificial Neural Network (ANN), Functional Link Artificial Neural Network (FLANN), Hidden Markov Model (HMM),

Support Vector Regression (SVR), an Auto-Regressive (AR) model. Some of the suggested novel neural network models for forecasting, on the other hand, took into account theoretical support and a methodical approach in model creation. This results in the transmission of new deep neural network models.

Islam et al. [27] conducted a SLR, which looked at recent advances in FOREX currency prediction using machine-learning algorithms. They utilized a keyword-based search approach to filter out popular and relevant research from papers published between 2017 and 2019. They also used a selection algorithm to decide which papers should be included in the review. They analyzed 39 research articles published on "Elsevier," "Springer," and "IEEE Xplore" that forecasted future FOREX prices within the specified time frame based on the selection criteria. According to their findings, in recent years, academics have been particularly interested in neural network models, pattern-based approaches, and optimization methodologies. Many deep learning algorithms, like the gated recurrent unit (GRU) and long short-term memory (LSTM), have been thoroughly investigated and show great promise in time series prediction.

Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting was the subject of Ryll & Seidens [54] study, more than 150 related publications on utilizing machine learning in financial market forecasting were reviewed in this study. They created a table across seven primary factors outlining the experiments done in the studies based on a thorough literature review. They provide a simple, standardized syntax for textually describing machine-learning algorithms by listing and classifying distinct algorithms. They conducted rank analyses to analyze the comparative performance of different algorithm classes based on performance criteria acquired from publications included in the survey. In financial market forecasting, machine-learning algorithms beat most classic stochastic methods, according to their findings. They also discovered evidence that recurrent neural networks outperform feed-forward neural networks and support vector machines on average, implying that there are exploitable temporal relationships in financial time series across asset classes and countries. The same is true when comparing the benefits of different machine learning architectures.

Sezer et al. [59] did a thorough evaluation of DL studies for financial time series forecasting implementations. Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Long-Short Term Memory (LSTM) were used to categorize the papers. Their findings show that, despite the fact that financial forecasting has a lengthy study history, overall interest in the DL community is increasing as a result of the use of new DL models,thus, there are numerous chances for researchers. They also attempted to predict the field's future by highlighting potential bottlenecks and opportunities in order to aid interested scholars.

Berradi et al. [6] suggested that giving the latest research of deep learning techniques applied to the financial market field can help investors to make an accurate decision. They gathered all the recent articles related to deep learning techniques applied to forecasting the financial market, which includes the stock market, stock index, commodity forecasting, and Forex. Their main goal was to find the most models used recently to solve the prediction problem using RNN, their characteristics, and their novelty. They gave all aspects that involve the process of forecasting beginning with preprocessing, the input features, the deep learning techniques, and the evaluation metrics employed. Their finding is that the hybrid model outperforms the traditional machine learning techniques, which leads to the conclusion that there is a very strong relationship between the combination of all the approaches and better prediction performance.

The goal of Henrique et al. [23] is to present methods for selecting the most important advances in machine learning applied to financial market prediction to present a review of the articles chosen, clarify the knowledge flow that the literature follows, and propose a classification for the articles. In addition, their study provides an overview of the best approaches for applying machine learning to financial time series forecasting as determined by the literature. The publications were then objectively assessed and categorized into the following categories: markets utilized as test data sources, predictive variables, predicted variables, methodologies or models, and performance metrics used in comparisons. In all, 57 papers from 1991 to 2017 were examined and categorized, spanning the specialist literature. Based on searches of connected article databases, no reviews employing such objective methodologies as main route analysis on the topic provided here were discovered according to them. The most cited articles, those with the highest bibliometric coupling and co-citation frequencies, the most recently published articles, and those that are part of the primary path of the literature studied knowledge flow were all discussed in the study. It should be highlighted that they were objective and straightforward survey methodologies, independent of the researcher's expertise, that could be used not just for preliminary research but also as knowledge validation for seasoned experts. In addition, the prediction algorithms and key performance measures for each article were presented. In addition to using neural and SVM networks, the authors used data from the North American market extensively. Similarly, the majority of the forecasts are based on stock indexes. New suggested models will likely be compared to neural and SVM network benchmarks, using data from the North American market, as one of the probable findings regarding the categorization presented in the research. The examination of the behavior of forecasts in developing markets, such as those of the BRICS, as well as the application of novel models in financial market prediction, continues to provide research opportunities.

Kaushik [32] presents a comprehensive review of contemporary research on Machine Learning and Deep Learning for exchange rate forecasting, based on peer-reviewed publications and books. The paper examines how Machine Learning and Deep Learning algorithms vary in projecting exchange rates in the FOREX market. SVM, Deep learning approaches such as Feedforward Neural networks, and hybrid ensembles have superior prediction accuracy than standard time series models, according to research. Future research should be conducted to assess the performance of these models, according to the authors,however, no single forecasting model consistently stands out as the best when evaluated using different criteria and on different currency pairs, and decisions based on the models' predictions should be used with caution.

Regarding the publications mentioned above, it's worth noting that none of them focused specifically on machine-learning methods for the FX market from 2010 to 2021. Fletcher [18] concentrated on discriminative approaches (Support Vector Machines (SVM), Relevance Vector Machines (RVM), and Neural Networks) without examining other machine learning algorithms critically.

Islam et al. [27] took into account machine learning in the context of forex trading, highlighting Regression Methods, Optimization Techniques, SVM Method, Neural Network Chaos Theory, Pattern-based Methods, and Other Methods, but the period under consideration was from 2017 to 2019, but there has been a lot of work in this area over the last two decades. Panda et al. [44] concentrated on deep learning and hybrid techniques, as well as a few other machine learning algorithms, but the number of publications chosen was insufficient for the period under evaluation.

The research published by Sezer et al. [59] focused on deep learning research by looking at Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Long-Short Term Memory (LSTM): however, there are other deep learning models such as Radial Basis Function, Multi-layer Perceptron, and many more deep learning methods that have been used in forex market prediction that were not looked at by the article.

The following are the contributions by this SLR:

We took a critical look at 60 primary articles or studies that present machine learning forecasting models in the forex market. Researchers can use them as a beginning stage to expand the knowledge on the topic.

We give a comprehensive summary of the primary studies found. This section is divided into three sections: I machine learning methodologies, (ii) evaluation strategies, and (iii) performance analysis of the presented models.

Based on our findings, we offer guidance and recommendations to help further research in the field.