The Effect of Kernel Functions on Cryptocurrency Prediction Using Support Vector Machines

Hitam, Nor Azizah; Ismail, Amelia Ritahani; Samsudin, Ruhaidah; Alkhammash, Eman H.

doi:10.1007/978-3-030-98741-1_27

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Ref. [20] used SVM kernel functions to predict the price directions of cryptocurrency and foreign exchange. They showed that the Radial Basis Gaussian (RBG) SVM forecasting model outperformed other SVM-kernel models.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies

Cohen

2022

Mathematics

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Artificial Intelligence (AI) has been recently recognized as an essential aid for human traders. The advantages of the AI systems over human traders are that they can analyze an extensive data set from different sources in a fraction of a second and perform actual high-frequency trading (HFT) that can take advantage of market anomalies and price differences. This paper reviews the most important papers published in recent years that use the most advanced techniques to forecast financial asset trends and answer the question of whether those techniques can be used to successfully trade the complex financial markets. All systems use deep learning (DL) and machine learning (ML) protocols to explore nonobvious correlations and phenomena that influence the probability of trading success. Their predictions are based on linear or nonlinear models often combined with social media investors’ sentiment derivations or pattern recognitions. Most of the reviewed papers have proven the successful ability of their developed system to trade the financial markets.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%