A Comparative Study of Machine Learning Algorithms for Financial Data Prediction

Bencharef, Omar; Daniel, Gonzalez Cortes; Bousbaa, Zineb; Aida, Cortes Jofre

doi:10.1109/isaect.2018.8618774

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Many papers in the literature that propose various methodologies and techniques [10], [14], [15] for modeling the prediction of exchange rates in the Forex market, Yong et al [13] studied the effects of different types of inputs including: The close price as well as various technical indicators derived from the close price are studied to determine its effects on the Forex trend predicted by an intelligent machine learning module and it has been found that the type of input data used for Forex price prediction is a crucial element that cannot be taken lightly. This means that the incorporation of trading rules and technical analysis as performed by the technical analyst in the initial phase of the forecasting algorithm will help increase the accuracy of Forex price forecasting.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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show abstract

“…The system learns to make recommendations by analyzing the similarity of features between items [15]. For example, based on a user's rating of different movie genres, the system will learn to recommend the genre that is positively rated by the user [8].…”

Section: Content-based Filteringmentioning

confidence: 99%

An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

2022

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Nowadays, recommendation systems offer a method of facilitating the user’s desire. It is useful for recommending items from a variety of areas such as in the e-commerce, medical, education, tourism, and industry domains. The e-commerce area represents the most active research we found, which assists users in locating the things they want. A recommender system can also provide users with helpful knowledge about things that could be of interest. Sometimes, the user gets bored with recommendations which are similar to their profiles, which leads to the over-specialization problem. Over-specialization is caused by limited content data, under which content-based recommendation algorithms suggest goods directly related to the customer profile rather than new things. In this study, we are particularly interested in recommending surprising, new, and unexpected items that may likely be enjoyed by users and will mitigate this limited content. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved using genetic algorithms that brings diversity to recommendations being made. This paper describes a Revolutionary Recommender System using a Genetic Algorithm called RRSGA which improves the fitness functions for recommending optimal results. The proposed approach employs a genetic algorithm to address the over-specialization issue of content-based filtering. The proposed method aims to incorporate genetic algorithms that bring variety to recommendations and efficiently adjust and suggest unpredictable and innovative things to the user. Experiments objectively demonstrate that our technology can recommend additional products that every consumer is likely to appreciate. The results of RRSGA have been compared against recommendation results from the content-based filtering approach. The results indicate the effectiveness of RRSGA and its capacity to make more accurate predictions than alternative approaches.

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Forecasting Financial Success App: Unveiling the Potential of Random Forest in Machine Learning-Based Investment Prediction

Khanna,

Goyal,

Chaurasia

et al. 2023

Lecture Notes in Networks and Systems

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A Comparative Study of Machine Learning Algorithms for Financial Data Prediction

Cited by 3 publications

References 25 publications

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

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

An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

Forecasting Financial Success App: Unveiling the Potential of Random Forest in Machine Learning-Based Investment Prediction

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