A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility

Hajizadeh, Ehsan; Mahootchi, Masoud; Esfahanipour, Akbar; Kh., Mahdi Massahi

doi:10.1007/s00521-015-2032-7

Cited by 24 publications

(4 citation statements)

References 47 publications

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“…Cao et al (2020) harnessed the power of deep learning, specifically deep long-short memory (LSTM), to forecast the exchange rate between the US dollar and the Chinese yuan, achieving a remarkable precision level of up to 75%. Hajizadeh et al (2019) introduced a hybrid model that combined the GARCH model with a neural network to forecast foreign exchange currency price volatility using the EUR/USD dataset, leading to improved prediction accuracy. Finally, Fan et al (2021) investigated the correlation between Taiwan Weighted Stock and Google Trends, demonstrating the superiority of neural networks over support vector machines and decision trees in their machine learning and trend search experiments.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…Ehsan, Masoud, Akbar, and Mahdi [66] proposed a new hybrid model for foreign exchange currency price volatility forecasting. They created a new neural network-based system.…”

Section: Neural Networkmentioning

confidence: 99%

“…Many algorithms, such as the modular neural network and deep belief model are yet to be explored. The reviewed literature indicate that neural network-based models can be equipped with different types of approaches, which proves the versatility of these models [62][63][64][65][66][67]. Moreover, instead of manual audit trails, neural networks have been applied to the financial distress problem, audit fees, internal control risk assessments, etc.…”

Section: Neural Networkmentioning

confidence: 99%

A Review on Recent Advancements in FOREX Currency Prediction

Islam¹,

Hossain

Rahman³

et al. 2020

Algorithms

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In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.

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“…But using them as an input for feeding models, including ANN, can improve their performance (Kristjanpoller and Minutolo, 2015). Hajizadeh et al (2019) have improved the model's accuracy by using hybridization of GARCH models and neural networks for predicting the Euro/Dollar volatility exchange rate. Since volatility is one of the most influential parameters on the return, using GARCH models' output as a new input for forecasting the return could help the model train better.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based methods for forecasting Brent Crude Oil return considering COVID-19 pandemic effect

Khodaee

Sajadi

Hajizadeh

et al. 2022

Preprint

Self Cite

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Forecasting return and profit is a primary concern for financial practitioners. It is even more critical when it comes to forecasting energy market returns. This research attempts to propose an effective method for predicting the Brent Crude Oil return, which results in remarkable performance compared to the well-known models in the return prediction. The proposed model is a hybrid model based on LSTM and CNN networks where ARIMA and GARCH outputs are used as features along with return lags, price, and macroeconomic variables for training the models, resulting in significant improvement in the model's performance. According to the obtained results, our proposed model performs better than other models, including ANN, PCA-ANN, LSTM, CNN. In the second part of this study, by considering the spread of the COVID-19 and its impact on the financial markets, we present a precise LSTM model that can reflect this disease's impact on the Brent Crude Oil return. This paper uses the significance test and correlation measures to show similarity between the series of Brent Crude Oil during the SARS period and COVID-19; after that, we use SARS period data along the COVID-19 to train the LSTM. The results demonstrate that the proposed LSTM model, tuned by the SARS data, can better predict the Brent Crude Oil return during the COVID19 pandemic.

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A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility

Cited by 24 publications

References 47 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

A Review on Recent Advancements in FOREX Currency Prediction

Deep learning-based methods for forecasting Brent Crude Oil return considering COVID-19 pandemic effect

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