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

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.

Can People With Higher Versus Lower Scores on Impression Management or Self-Monitoring Be Identified Through Different Traces Under Faking?

“…Although only R 2 can be used to assess a model’s performance on different data sets (e.g., faking high scores vs. faking low scores; see, e.g., Chicco et al, 2021 ), we nevertheless present all the common performance indices in our results to demonstrate their convergence (e.g., Ayitey Junior et al, 2023 ; Iskandaryan et al, 2020 ; see Table 4 ).…”

“…Performance Evaluation. The performance of regressions is typically evaluated with the following performance indices: R 2 , RMSE, MAE, and MSE, whereby R 2 and RMSE are used most frequently (e.g., Ayitey Junior et al, 2023;Iskandaryan et al, 2020). R 2 represents a measure of relative improvement in prediction over the mean model.…”

“…Whether or not machine learning is able to predict the respective trait above chance levels can be evaluated by a bundle of performance measures (e.g., in our case, R 2 , the root mean square error [ RMSE ], the mean average error [ MAE ], and the mean square error [ MSE ]; Ayitey Junior et al, 2023 ; Iskandaryan et al, 2020 ) that are explained in detail in the Method section. Moreover, feature importance analyses allow researchers to obtain in-depth insights into which items were most important for the prediction (e.g., Vijayakumar & Cheung, 2018 ), that is, which items were faked differently by individuals with respect to their scores on the respective personality variable (e.g., the most important feature for predicting the SM of fakers is Item 3).…”

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

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