Optimized Modular Deep Neural Network for Forex Stock Price Forecasting

Investment in the foreign exchange (Forex) market is an effective strategy to enhance capital and gain profit. As one of the most profitable financial markets worldwide, Forex can provide substantial investment returns for those equipped with the right trading and economics knowledge. This decentralized market for currency trading and exchange is globally recognized as one of the largest investment markets, playing a critical role in determining currency exchange rates. The Forex market facilitates currency exchange in various transactions, particularly those related to the purchase and sale of goods and services by companies, covering travel expenses, and enabling investments by individuals or agencies in countries that utilize the respective currencies. The dynamic nature of the Forex market allows traders and investors to capitalize on fluctuations in currency values, which can be influenced by a multitude of factors including economic indicators, geopolitical events, and market sentiment. The present study aims to provide a comprehensive review of the data collection process, focusing on the selection of relevant Forex market datasets. It meticulously evaluates the measures adopted to ensure the consistency and reliability of each dataset, which is critical for accurate analysis and forecasting. Given the complexity and volatility of the Forex market, maintaining high-quality datasets is essential for effective trading strategies and decision-making. In addition to the data review, the study also proposes and describes a modular neural network (MNN) model designed specifically for forecasting price fluctuations within the Forex market. This advanced modeling approach leverages machine learning techniques to enhance predictive accuracy, allowing traders to make informed decisions based on anticipated market movements. By integrating rigorous data analysis with sophisticated forecasting models, this research aims to provide valuable insights into the complexities of Forex trading and its potential for profit maximization.

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Publication Details

Journal
Journal of Economics Management and Trade
Published
2025-05-28
DOI
https://doi.org/10.9734/jemt/2025/v31i61305
Citations
1
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
3.13
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article

Optimized Modular Deep Neural Network for Forex Stock Price Forecasting

Hossein AdabiFirouzjaei
1 citations
Journal of Economics Management and Trade
Market Dynamics and Volatility
3.13
article

Optimized Modular Deep Neural Network for Forex Stock Price Forecasting

Hossein AdabiFirouzjaei
article en
1 citations

Abstract

Investment in the foreign exchange (Forex) market is an effective strategy to enhance capital and gain profit. As one of the most profitable financial markets worldwide, Forex can provide substantial investment returns for those equipped with the right trading and economics knowledge. This decentralized market for currency trading and exchange is globally recognized as one of the largest investment markets, playing a critical role in determining currency exchange rates. The Forex market facilitates currency exchange in various transactions, particularly those related to the purchase and sale of goods and services by companies, covering travel expenses, and enabling investments by individuals or agencies in countries that utilize the respective currencies. The dynamic nature of the Forex market allows traders and investors to capitalize on fluctuations in currency values, which can be influenced by a multitude of factors including economic indicators, geopolitical events, and market sentiment. The present study aims to provide a comprehensive review of the data collection process, focusing on the selection of relevant Forex market datasets. It meticulously evaluates the measures adopted to ensure the consistency and reliability of each dataset, which is critical for accurate analysis and forecasting. Given the complexity and volatility of the Forex market, maintaining high-quality datasets is essential for effective trading strategies and decision-making. In addition to the data review, the study also proposes and describes a modular neural network (MNN) model designed specifically for forecasting price fluctuations within the Forex market. This advanced modeling approach leverages machine learning techniques to enhance predictive accuracy, allowing traders to make informed decisions based on anticipated market movements. By integrating rigorous data analysis with sophisticated forecasting models, this research aims to provide valuable insights into the complexities of Forex trading and its potential for profit maximization.

Journal of Economics Management and TradeVol. 31(6)
Shahid Beheshti University (IR)
Openalex Percentile: Top 8%
Market Dynamics and Volatility
3.13
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Optimized Modular Deep Neural Network for Forex Stock Price Forecasting — Hossein AdabiFirouzjaei · Journal of Economics Management and Trade (2025) | TGRS Research Map | TGRS