A Comparative Study of Support Vector Machine and Artificial Neural Network for Option Price Prediction

Madhu, B; Rahman, M.A.; Mukherjee, Arnab; Islam, Zahidul; Roy, Raju; Ali, Lasker Ershad

doi:10.4236/jcc.2021.95006

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…We likewise believe that our model can be used to solve problems related to regression after a SVM-to-SVR-like transformation (e. g. [12][13][14][15][16][17]). Of particular interest is the fact that our algorithms are well suited for applications in the field of financial forecasting(e. g. [18][19][20].). e field of financial forecasting requires algorithms with controllable computation times and good performance for nonlinear problems.…”

Section: Discussionmentioning

confidence: 99%

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

Yan

Wang

Lei

2022

Computational Intelligence and Neuroscience

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The support vector machine theory has been developed into a very mature system at present. The original support vector machine to solve the optimization problem is transformed into a direct calculation formula of line in this paper and the model is o n 2 time complexity. In the model of this article, weited theory, multiclassification problem and online learning have all become the direct inference, and we have applied the new model to the UCI data set. We hope that in the future, this model will be useful in real-world problems such as stock forecasting, which require nonlinear hi-speed algorithms.

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Section: Discussionmentioning

confidence: 99%

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

Yan

Wang

Lei

2022

Computational Intelligence and Neuroscience

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“…Neural network and financial markets. Many academics have reported that the neural network is one of the best models to examine many financial concepts and can be used to explore the effects of COVID-19 on stock financial markets [8,[54][55][56][57][58]. [59] showed that algorithms can predict stock market deterioration.…”

Section: Plos Onementioning

confidence: 99%

Impact of the twin pandemics: COVID-19 and oil crash on Saudi exchange index

AL-Najjar

2022

PLoS ONE

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This study aims to explore the effects of COVID-19 indicators and the oil price crash on the Saudi Exchange (Tadawul) Trading Volume and Tadawul Index (TASI) for the period from January 1, 2020, to December 2, 2020. The independent variable is oil price, and the COVID-19 indicators are lockdown, first and second decreases of Repo and Reverse Repo rates, Saudi government response, and cumulative deceased cases. The study adopts two phases. In the first phase, linear regression is used to identify the most influential variables affecting Trading volume and TASI. According to the results, the trading volume model is significant with an adjusted R2 of 65.5% and a standard error of 81. The findings of this model indicate a positive effect of cumulative deceased cases and first decrease of Repo and Reverse Repo rates and a negative effect of oil prices on Trading Volume. The TASI model is significant with an adjusted R2 of 86% and a standard error of 270. The results of this model indicate that lockdown and first decrease of Repo and Reverse Repo rates have a significant negative effect on TASI while the cumulative decrease in cases and oil prices have a positive effect on TASI. In the second phase, linear regression, and neural network predictors (with and without validation) are applied to predict the future TASI values. The neural network model indicates that the neural networks can achieve the best results if all independent variables are used together. By combining the collected results, the study finds that oil price has the most substantial effect on the changes in TASI as compared to the COVID-19 indicators. The results indicate that TASI rapidly follows the changes in oil prices.

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“…e number of groups, move steps, and number of iterations within groups were mainly simulated to verify the effect of different parameters on the prediction accuracy. e number of hidden layer neurons of the RBF neural network was set to 10 and It can be seen that when the number of groups belongs to [10,20,30,40,50] and the step size [1,3,5], the RMSE does not exceed 0.7, and the maximum and minimum values do not deviate much from the mean value, so the algorithm is relatively stable. In practice, the SFLA parameters can be fine-tuned by changing the main parameters several times in order to achieve better prediction results.…”

Section: Effect Of Sfla Parameters On Gdp Forecastingmentioning

confidence: 99%

“…e human brain can handle a variety of complex non-linear problems very quickly, and researchers have been inspired by the idea of how to simulate the human brain to deal with complex non-linear problems. An artificial neural network (ANN) is an intelligent bionic model that mimics the function of neurons in the brain [18][19][20][21], and is a non-linear complex network system consisting of a large number of interconnected neurons.…”

Section: Introductionmentioning

confidence: 99%

GDP Economic Forecasting Model Based on Improved RBF Neural Network

2022

Mathematical Problems in Engineering

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Among the existing GDP forecasting methods, time series forecasting and regression model forecasting are the two most commonly used forecasting methods. However, traditional macroeconomic forecasting models are unable to accurately achieve optimal forecasts of highly complex nonlinear dynamic macroeconomic systems due to the influence of multiple confounding factors. In order to solve the above problems, a GDP economic forecasting model based on an improved RBF neural network is proposed. First, the main traditional GDP forecasting methods are analyzed. Then, RBF neural networks are used to solve the problem that traditional forecasting technology methods cannot handle multi-factor complex nonlinearities well. Second, to further improve the convergence speed and accuracy of the RBF neural network learning algorithm, the Shuffled Frog Leaping Algorithm with global search capability and high practicality is fused into the RBF network training. Finally, the improved RBF neural network is used to build a GDP economic forecasting model. The performance of the Shuffled Frog Leaping Algorithm and the improved RBF neural network was tested using the approximation of Hermit polynomials and the Iris classification problem as simulation examples. The experimental results show that the improved RBF neural network-based GDP economic forecasting model achieves more accurate forecasting accuracy than other forecasting methods.

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A Comparative Study of Support Vector Machine and Artificial Neural Network for Option Price Prediction

Cited by 19 publications

References 20 publications

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

Impact of the twin pandemics: COVID-19 and oil crash on Saudi exchange index

GDP Economic Forecasting Model Based on Improved RBF Neural Network

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