A Combination of Regression Techniques and Cuckoo Search Algorithm for FOREX Speculation

Achchab, Saïd; Bencharef, Omar; Ouaarab, Aziz

doi:10.1007/978-3-319-56535-4_23

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…The key objective of this study was to collect, sort out, and gather information on the research on the FOREX forecasting by analyzing the papers. In the past, a lot of researchers used statistical analysis (e.g., Ludmila, Pavel, and Krzysztof [39]; Ardiana, Adhistya, and Silmi [40]; Said, Omar, and Aziz [41]). They used a variety of statistical analysis-based algorithms for forecasting the FOREX currency prices, including regression, decision trees, the trading rule, SVM, and fuzzy systems.…”

Section: Methodsmentioning

confidence: 99%

“…With combining regression techniques along with the cuckoo search algorithm, a hybrid model for speculating USD/EUR variations was developed by the authors of [41]. They proposed a dataset made of JYN, EUR, and BRP variations and USD/EUR currency pair.…”

Section: Regression Methodsmentioning

confidence: 99%

“…However, there are other algorithms such as lasso regression, logistic regression, and multivariate regression which were not explored and can be used for forecasting in later years. The reviewed literature suggests that the system based on a regression model performed better than ARIMA and ARFIMA models [48], and the model performance may increase to a great extent [41] when a regression model is combined with other techniques. However, when working with a large number of currency pairs, it may become difficult with regression techniques, as most of the currency pairs return a higher MSE [49].…”

Section: Regression Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Methodsmentioning

confidence: 99%

Section: Regression Methodsmentioning

confidence: 99%

Section: Regression Methodsmentioning

confidence: 99%

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A Review on Recent Advancements in FOREX Currency Prediction

Islam¹,

Hossain

Rahman³

et al. 2020

Algorithms

View full text Add to dashboard Cite

show abstract

“…As the Forex market is a dynamic environment and sensitive to any event that can disrupt its stability in terms of sudden changes in currency exchange rates, speculation is considered a risky operation and can contain unexpected surprises, therefore, the prediction represents an essential tool to have an overview of the market trend during the following hours or days; because it can allow knowing when and how the trader should react to avoid losing money and to maximizing profits of his investment in Forex. In the literature, there is a large bibliography used different techniques and methods [4]- [6] shows success. The model proposed in this paper is characterized by using various contributing techniques in the aim to obtain a good prediction accuracy of currency exchange rates, the added value of this model consists in combining several types of machine learning (supervised learning and reinforcement learning (RL)) [7], [8].…”

Section: State Of the Artmentioning

confidence: 99%

Hybrid Forex prediction model using multiple regression, simulated annealing, reinforcement learning and technical analysis

Jamali

Chihab

García‐Magariño

et al. 2023

IJ-AI

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Foreign exchange market refers to the market in which currencies from around the world are traded. It allows investors to buy or sell a currency of their choice. Forex interests several categories of stakeholders, such as companies that carry out international contracts, large institutional investors, via the main banks, which carry out transactions on this market for speculative purposes. One of the most important aspects in the Forex market is knowing when to invest by buying, selling, and this through the recorded trend of a currency pair, but given the characteristics of the Forex market namely its chaotic, noisy and not stationary nature, prediction becomes a big challenge for traders when it comes to predicting accuracy. This paper aims to predict the right action to be taken at a certain moment through the development of a model that combines multiple techniques such multiple regression, simulated annealing meta-heuristics, reinforcement learning and technical indicators.

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“…In recent years, there has been a strong focus on predicting the volatile FOREX market, leading researchers to explore various methods. Among these, statistical analysis techniques have gained popularity, with researchers employing algorithms such as regression, decision trees, trading rules, support vector regression (SVR), and fuzzy systems (Dymova et al, 2016;Achchab et al, 2017). Raimundo and Okamoto (2018) contributed to this area by proposing a hybrid model for FOREX rate prediction.…”

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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A Combination of Regression Techniques and Cuckoo Search Algorithm for FOREX Speculation

Cited by 5 publications

References 24 publications

A Review on Recent Advancements in FOREX Currency Prediction

A Review on Recent Advancements in FOREX Currency Prediction

Hybrid Forex prediction model using multiple regression, simulated annealing, reinforcement learning and technical analysis

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

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