Forex Trading Research Articles

Development of Marketing Strategy to Improve AFX Market Penetration in Indonesia

In this paper, we examine the marketing strategy of AFX, a pseudonym for a real forex trading platform. This alias helps us discuss the company’s strategies without disclosing its identity. Our focus is on AFX’s efforts to expand its market presence in Indonesia. Despite the rapid growth of the global fintech sector, AFX has not effectively reached the young Indonesian market due to low financial literacy levels. This study uses both qualitative and quantitative research methods to explore the factors that affect AFX’s position in the market and its strategic marketing actions. We apply the 7P Marketing Mix and STP (Segmentation, Targeting, Positioning) frameworks to analyze AFX’s internal processes and external challenges. The goal is to propose strategies that could increase the platform’s Monthly Active Users (MAU).

Sharia Economic Law Analysis of Foreign Currency Buying and Selling In Forex Trading Transaction at PT. Didimax Berjangka

This research aims to determine the law of buying and selling currency in forex trading transactions at PT. Didimax Futures. Forex trading is a currency transaction from various countries involving major money markets and brokers and is carried out 24 hours a day. PT. Didimax Berjangka is the leading and first broker company in Indonesia. This research uses a qualitative approach with descriptive methods. The research results found that money cannot be used as a trading commodity, according to Al-Ghazali's opinion in the Book of Ihya Al-Ulumudin. However, based on the DSN-MUI fatwa Number 96/DSN-MUI/IV/2015 concerning Hedging Transactions, forex trading transactions are permitted because, in the fatwa, the laws of trading and forex are equated using mutual promises (muwa'addah). According to the author's analysis, forex trading transactions carried out by PT. Didimax is by the DSN-MUI fatwa because it uses a promise agreement (wa'ad) when carrying out transactions. In conclusion, it buys and sells currency in forex trading transactions at PT. Didimax Berjangka is permitted according to Sharia Economic Law.

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.

INTERNET USERS’ PERCEPTION OF THE PREVALENCE OF ONLINE INVESTMENT FRAUD AND VICTIMISATION IN NIGERIA

This study investigated the prevalence of online investment fraud and victimisation among internet users in Nigeria, focusing on sociodemographic characteristics, investment behaviours, victimisation. experiences, and perceptions of fraud risk factors. The study employed a cross-sectional survey design, utilizing Google Forms for data collection and convenience sampling to recruit 164 participants from Ilorin, Kwara State. The study adopted the Routine Activities Theory (RAT) as theoretical framework. Findings reveal that a majority of respondents are young individuals with tertiary education, indicating their susceptibility to online investment fraud due to limited financial experience and a desire for quick financial gains. Notably, over half of the participants actively engage in online investments. In addition, family and friends' recommendations and the pursuit of financial freedom influencing investment decisions significantly. However, a substantial proportion of investors report falling victim to online investment fraud, predominantly through Ponzi/pyramid schemes, crypto currency scams, and forex trading scams. Financial losses vary among victims, with factors such as lack of regulation, poverty, unemployment, and insufficient training on fraud prevention contributing to vulnerability. Statistical analysis indicates a significant relationship between investment experience and the perception of high returns as a risk factor for fraud victimisation. These findings underscore the urgent need for regulatory measures, public awareness campaigns, and educational initiatives to mitigate the risks associated with online investment fraud in Nigeria.

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.

An Analytical Study on Investment Awareness and Pattern of Different Class of Investors

In Today’s era, Individuals are earning a lot with the help of Investment or Passive income rather than active income. People start searching new ways of income generation because of increased expenses and inflation. Investment helps in achieving income, additional growth and creates value. The return and risk are totally dependent on chosen avenue of Investment. This research measures and analyze the awareness of respondents regarding different investment avenues in relation with the occupation of the respondents. For the fulfilment of the objectives of the study, data were collected from 100 respondents of Haryana, who have diverse demographic and socio-economic profile. The findings of the study revealed that there is no significant difference between awareness of respondents regarding different investment options and the occupation of the respondents except Recurring deposit, KVP, Derivatives, FOREX Trading and PPF. The results also found that investors of same income level donot have same knowledge of the investment but the investors lie in same age group generally have same knowledge about investment. The investors of same occupation donot have same knowledge of the investment but investors who comes under same category of annual income usually invest same percentage of their income. This study further concluded that different investors have diverse knowledge and time horizon of investment according to their income level, age, gender and occupation.

Applying News and Media Sentiment Analysis for Generating Forex Trading Signals

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.

Transformers in High-Frequency Trading

Transformer is a deep learning model that, having an innovative performance in many tasks, has uniquely and significantly modified all the cast of mind of the AI scientific community. In this paper, we introduce a Transformer model that is applied to 1-minute timescale in the EURUSD and GBPUSD instruments of forex trading. We use the classical Transformer architecture without the Decoder since the Encoder is enough. Moreover, the Exponential Moving Average (EMA) model is applied to the input, while different values of its smoothing factor α are tested. With cross-entropy training loss less than 0.2, it is exclamatory that Transformers are a promising tool for lucrative strategies in high-frequency trading.

A Hybrid Approach for Accurate Forecasting of Exchange Rate Prices Using Vmd-Ceemdan-Gru-Atcn Model

The foreign exchange (Forex) market has greatly influenced the global financial market. While Forex trading offers investors substantial yield prospects, some risks are also involved. It is challenging to accurately model financial time series due to their nonlinear, non-stationary and noisy properties with an uncertain and hidden relationship. Thus, developing extremely precise forecasting techniques is crucial for investors and decision-makers. This study introduces a novel hybrid forecasting model, VMD-CEEMDAN-GRU-ATCN, designed to improve Forex price prediction accuracy. To begin with, our proposed model utilizes the variational model decomposition (VMD) technique for breaking down raw prices into multiple sub-components and residual terms. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is utilized to extract features from the residual terms, which involves further decomposition and analysis of these complex information-containing terms. These sub-components are then predicted by the gated recurrent unit (GRU) model. To enhance the effectiveness of our hybrid model, we include the open, high, low, and close prices and seven Forex market technical indicators. Finally, an attention-based temporal convolutional network (ATCN) model is used to obtain the Forex price forecasts. For both one-step and multi-step ahead forecasting, our proposed VMD-CEEMDAN-GRU-ATCN model has demonstrated superior and consistent performance in predicting USD/PKR exchange rate price series.

Forex market directional trends forecasting with Bidirectional-LSTM and enhanced DeepSense network using all member-based optimizer

This study focuses on successful Forex trading by emphasizing the importance of identifying market trends and utilizing trend analysis for informed decision-making. The authors collected low-correlated currency pair datasets to mitigate multicollinearity risk. Authors developed a two-stage predictive model that combines regression and classification tasks, using the predicted closing price to determine entry and exit points. The model incorporates Bi-directional long short-term memory (Bi-LSTM) for improved price forecasting and higher highs and lower lows (HHs-HLs and LHs-LLs) to identify trend changes. They proposed an enhanced DeepSense network (DSN) with all member-based optimization (AMBO-DSN) to optimize decision variables of DSN. The performance of the models was compared to various machine learning, deep learning, and statistical approaches including support vector regressor (SVR), artificial neural network (ANN), auto-regressive integrated moving average (ARIMA), vanilla-LSTM (V-LSTM), and recurrent neural network (RNN). The optimized form of DSN using genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) was compared with AMBO-DSN, yielding satisfactory results that demonstrated comparable quality to the observed trends on the original currency pairs. The effectiveness and reliability of the AMBO-DSN approach in forecasting trends for USD/EUR, AUD/JPY, and CHF/INR currency pairs were validated through statistical analysis while considering computational cost.

Comparison of LSTM and GRU Models for Forex Prediction

Trading foreign currencies worth trillions of dollars takes place daily in the forex market, characterized by highly volatile movements. The forex market operates on bid and ask prices, with exchange rates determined by the principles of supply and demand. Trading involves currency pairs like EUR/USD, where the value of the Euro is compared to the US Dollar, serving as a basis for analyzing price fluctuations. Due to the volatile nature of forex, market participants must make informed decisions when buying and selling, as improper choices can result in financial losses. One approach to mitigating risk in forex trading decisions is through the use of forecasting techniques. This research study employs LSTM and GRU methods to predict forex trends, which are evaluated using various dataset divisions. The most accurate results are obtained using a dataset of 4979, split into three equal parts: 80% for training, 10% for validation, and 10% for testing. This approach yields an RMSE value of 0.054, MAPE of 0.037, and R-square of 97%

Cryptocurrency and African fiat currencies: A peaceful coexistence?

AbstractThis study examines the asymmetric behaviour of Bitcoin relative to six major African fiat currencies (Egyptian Pound, Cedi, ZAR, Naira, Rupee and Dinar) for the period 10 August 2015 to 31 December 2022. The time and frequency information in the time series of the currencies were captured applying the ensemble empirical mode decomposition. The quantile regression (QR) and quantile‐in‐quantile regression (QQR) were applied on the decomposed series to examine the connections among the currencies at different currency regimes across time. The empirical results show that both QR and QQR can adequately capture the time‐varying asymmetric behaviour of the currencies across time. The results range from weak to very strong dependencies albeit both negative and positive across different quantiles. Our findings suggest that except for ZAR, Bitcoin is a viable alternative currency to African reserve currencies from the medium‐term since it can hedge depreciation and forex risk of the fiat currencies. Based on the findings of this study, we recommend that forex traders and policymakers in Africa should adopt Bitcoin as an alternative currency to African currencies in the medium‐term to mitigate currency crises in the continent.

Forex In The Eyes Of Shariah: A Qualitative Analysis In The Malaysian Context

The purpose of this study is to expound the fundamentals leading to the verdict about the permissibility of foreign exchange trading or simply forex. The jurisdiction will cover Quranic verses, Hadith and Islamic legal maxims or commonly known as qawaid fiqhiyyah. The contemporary views of the ulema were also discussed as the scope would later be focused in a Malaysian context. The primary data from a questionnaire to 100 Malaysian respondents would be presented. The findings show that most of the Malaysian respondents were aware of Forex trading but they had never attempted. This was due to their state of not well informed about Forex due to skepticism and extreme doubt. They also felt more secure not involved with this trading and this related strongly to their prejudice. Therefore, implications were covered in three spheres of influence: economic, social and national perspectives. Finally, recommendations for respective parties such as governments, auctioning bodies and general public would be mentioned as well in order to address the public incognizance about Forex from Sharia perspective. The research concluded that riba (usury) will never be accepted in any circumstances, and if Forex insists on incorporating this despised element, then this financial transaction might stay haram (forbidden) indefinitely, unless otherwise abiding the Islamic apparatus and guidelines.

Purchasing Power Parity Strategy in Trading Forex

Purchasing Power Parity (PPP) strategy has been implemented in the foreign currency exchange market for many years. The crucial basis for this strategy is the equilibrium of the purchasing power and disproportionate exchange rate. However, previous research has generally determined the profitability of the PPP strategy but has ambiguous signal generations, as PPP is data released annually. Thus, the repeatability of the research is low. In this paper, we use data including CPI, PPP, interest rate, market exchange rate, bid-ask spread, and DXY between January 1, 1997, to August 1, 2022, to generate a dynamic relatively purchasing power parity, which is a monthly data, and then generate our signal and test the feasibility and profitability of our research. Our finding, including the in-sample and out-of-sample tests, indicates that, RPPP could be an effective forex trading strategy. Furthermore, in the long run, the PPP strategy based on our signal is profitable, and the loss is acceptable.

IMPLEMENTASI EXPERT ADVISOR UNTUK TRADING OTOMATIS FOREX MENGGUNAKAN INDIKATOR RSI DAN MA DENGAN METODE MARTINGALE DI PLATFORM METATRADER 4

Among the many digital businesses, forex trading is one of them. To trade forex, various types of software are used which are tools for traders, such as Metatrader 4. The core business of forex trading is to buy or sell consistently to trade forex, making a profit. In order to get the desired profit, it is important to adjust the forecast towards a growing market. Therefore, we need the right forex indicators to predict prices that are not wrong. However, traders who follow predictive indicators signals often fail to execute trades on time and therefore are unable to make consistent profits. The solution for traders to find timely trading opportunities based on indicators is to create an automated forex trading system or Expert Advisor. Expert Advisor (EA) is a program/system that has the ability to appear in the terminal on the instructions of a trader without direct involvement. All tasks that the Expert Advisor performs automatically. Expert Advisors can work automatically without the need to monitor price movements for 24 hours. EA is designed to analyze the market based on 2 indicators RSI (Relative Strength Index) and MA (Moving Average) as a reference for prices that will occur and also as a reference for trading using the martingale method.

Prediksi Harga pada Trading Forex Pair USDCHF Menggunakan Regresi Linear

In the era of globalization, free trade grows rapidly and technology develops, affecting economic competition. Forex, foreign exchange trading, is one of the investments used to face this challenge. Technical and fundamental analysis is used to predict price movements in forex trading. Previous studies have used linear regression algorithms and other techniques for price prediction in forex. In this study, the linear regression algorithm is used to predict closing prices in forex trading because the linear regression algorithm is an algorithm that has been widely used in predictions, its strengths are in estimating simple model parameters and data based on time series. In addition, the linear regression algorithm can perform analysis using several independent variables so that the prediction results can be more accurate. The purpose of this study is to create a forex price prediction model, to make it easier for traders to make price predictions. A dataset of 2066 data was obtained through the metatrader software and processed through the preprocessing stage. The linear regression model was created using 5 scenarios, and the evaluation was carried out using the Mean Squared Error (MSE) and Root Mean Square Error (RMSE) values to select the best model. The results show that linear regression is able to predict the closing price of the USDCHF pair. The best linear regression model is obtained using the independent variable in scenario 1, namely the Open variable, with a linear regression equation of y=0.0145+0.9849x, the best MSE is 0.0000328509 and the best RMSE is 0.0057315705.

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.

Quo Vadis Kebijakan Pemerintah Terhadap Legalitas Keberadaan Investasi Robot Trading (Expert Advisor) dikaji menggunakan Teori Hukum dan Pembangunan

The purpose of writing this journal is to analyze and understand government policies regarding the legality of trading robots. The development of the times has provided many advances in both the technological and economic fields, technological advances have provided openness to trade in both goods and services. Forex trading is a business activity that offers high profits accompanied by high risks, where individuals must monitor and study the movements of the money market without stopping to make transactions. The existence of trading robots makes it easy to do forex trading due to automation to save time and does not require in-depth understanding of forex trading. The existence of trading robots provides an opportunity for criminal acts to occur in the forex field, one of which is the existence of Illegal Trading Robots which results in losses for users. One of them is the fraudulent investment case of Net 89, which has a trading robot mode, which uses a permit to sell e-books in carrying out trading activities based on trading robots. This problem has resulted in an urgency that can be seen from the enactment of BAPPEBTI Regulation Number 12 of 2022 as a step to regulate the existence of trading robots.

A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization

This paper is based on some assumptions validated using real data and the most used Forex trading platform Meta Trader 4. First, we assume that any reasonable and relatively simple models, reflecting some trading hypotheses, can be profitable for a certain period. Having a sufficient set of such models optimized for selected currency pairs, we can use the model that provides the greatest profit in the current trading period. The second proposal is to use a fuzzy multiple-criteria approach at the training optimization stage using historical data in order to overcome or reduce the negative effect of overfitting. Here for the first time, the problem of fuzzy multiple-criteria optimization of trading was formulated and solved based on the output parameters of the developed simple single-criteria crisp models. This provides significantly greater profit than that obtained using single-criteria crisp models. The third proposal is to use the hierarchical structure of fuzzy local criteria to solve the multiple-criteria problem. It is shown that this additionally provides a significant increase in profit. The profitability and riskless of the developed trading models are studied based on real quotations of currency pairs USDJPY, EURUSD and AUDUSD using H4 timeframe.

A Wavelet-Decomposed WD-ARMA-GARCH-EVT Model Approach to Comparing the Riskiness of the BitCoin and South African Rand Exchange Rates

In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR empirically. Maximal Overlap Discrete Wavelet Transform (MODWT) and two mother wavelet filters on the returns series are considered in this paper, viz., the Haar and Daubechies (d4). The findings show that BitCoin/USD is riskier than ZAR/USD since it has a higher VaR per unit invested in each currency. At the 99% significance level, BitCoin/USD has average values of VaR of 2.71% and 4.98% for the WD-ARMA-GARCH-GPD and WD-ARMA-GARCH-GEVD models, respectively; and this is slightly higher than the respective 2.69% and 3.59% for the ZAR/USD. The average BitCoin/USD returns of 0.001990 are higher than ZAR/USD returns of −0.000125. These findings are consistent with the mean-variance portfolio theory, which suggests a higher yield for riskier assets. Based on the p-values of the Kupiec likelihood ratio test, the hybrid model adequacy is largely accepted, as p-values are greater than 0.05, except for the WD-ARMA-GARCH-GEVD models at a 99% significance level for both currencies. The findings are helpful to financial risk practitioners and forex traders in formulating their diversification and hedging strategies and ascertaining the risk-adjusted capital requirement to be set aside as a cushion in the event of the occurrence of an actual loss.