Deep Learning based Currency Exchange Volatility Classifier for Best Trading Time Recommendation

Tigani, Smail; Tadist, Khawla; Saadane, Rachid; Chehri, Abdellah; Chaibi, Hasnae

doi:10.1016/j.procs.2022.09.216

Procedia Computer Science

2022

DOI: 10.1016/j.procs.2022.09.216

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Deep Learning based Currency Exchange Volatility Classifier for Best Trading Time Recommendation

Smail Tigani

Khawla Tadist

Rachid Saadane

et al.

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Cited by 3 publications

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“…They reached an average deviation of 0.94. In their study, Tigani et al (2022) developed a model utilizing Gaussian kernel density and Monte Carlo simulation to forecast the volatility patterns of the EUR/USD currency pair on an hourly basis. The proposed model holds potential applications in the financial market, particularly in algorithmic trading, where the Monte Carlo method is employed to estimate integrals within this framework.…”

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confidence: 99%

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

Alaminos

Salas

Fernández-Gámez

2023

Humanit Soc Sci Commun

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The foreign exchange markets, renowned as the largest financial markets globally, also stand out as one of the most intricate due to their substantial volatility, nonlinearity, and irregular nature. Owing to these challenging attributes, various research endeavors have been undertaken to effectively forecast future currency prices in foreign exchange with precision. The studies performed have built models utilizing statistical methods, being the Monte Carlo algorithm the most popular. In this study, we propose to apply Auxiliary-Field Quantum Monte Carlo to increase the precision of the FOREX markets models from different sample sizes to test simulations in different stress contexts. Our findings reveal that the implementation of Auxiliary-Field Quantum Monte Carlo significantly enhances the accuracy of these models, as evidenced by the minimal error and consistent estimations achieved in the FOREX market. This research holds valuable implications for both the general public and financial institutions, empowering them to effectively anticipate significant volatility in exchange rate trends and the associated risks. These insights provide crucial guidance for future decision-making processes.

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mentioning

confidence: 99%

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

Alaminos

Salas

Fernández-Gámez

2023

Humanit Soc Sci Commun

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Deep Learning based Currency Trend Classification Trained on Technical Indicators based Generated Dataset

Tigani,

Makrane,

Saadane

et al. 2023

Procedia Computer Science

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

Dash,

Sahu,

Mishra

2023

IDT

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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.

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Deep Learning based Currency Exchange Volatility Classifier for Best Trading Time Recommendation

Cited by 3 publications

References 15 publications

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

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

Deep Learning based Currency Trend Classification Trained on Technical Indicators based Generated Dataset

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

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