Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine lear...Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices.展开更多
An adaptable, energy efficient chemical process is employed to synthesize Cu^2+engrafted MgAl2O4 nanoparticles(Mg1-xCuxAl2O4, x = 0, 0.1, 0.3, 0.5 abbreviated as MCA0, MCA1, MCA3,and MCA5 respectively), using chelatin...An adaptable, energy efficient chemical process is employed to synthesize Cu^2+engrafted MgAl2O4 nanoparticles(Mg1-xCuxAl2O4, x = 0, 0.1, 0.3, 0.5 abbreviated as MCA0, MCA1, MCA3,and MCA5 respectively), using chelating ligand and the calcination temperature was determined by the thermogravimetric analysis of the precursor mass.They acted as good fluoride adsorbent in the presence of co-ions, different pH(2–11) via chemisorption revealed from Fourier-transform infrared spectroscopy(FTIR) and photodegraded Methylene Blue(MB).The satisfactory results were for MCA1(specific surface area 25.05 m^2/g) with 97%fluoride removal at pH 7.0 for the 10 mg/L initial fluoride concentration for 1.5 g/L adsorbent dose with 45 min contact time obeying the Langmuir isotherm model with negative thermodynamic parameters and 4 mmol of MCA3 with 98.51% photodegradation for 10-5 mol/L MB solution obeying pseudo-second-order and pseudo-first-order kinetics respectively.The proposed photodegradation mechanism of MB was established by the FTIR and high-performance liquid chromatography(HPLC) analysis.The nanoparticles are cubic, estimated through X-ray diffraction(XRD) and transmission electron microscopy(TEM) analysis.The band gap energies, grain size, and the effective working pH were estimated by diffuse reflectance spectra(DRS), scanning electron microscope(SEM), and zero-point potential analysis respectively.A soil candle with MCA1 also fabricated for the household purpose and tested with some fluorinated field samples.The MCA3 was able to enhance the latent fingerprint on smooth surfaces.展开更多
文摘Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices.
基金Department of Science and Technology,Government of West Bengal,India,vide project sanction(No.674(sanc)/ST/P/S&T/15G/5/2016)dated 09/11/2016 for financial supportthe Council of Scientific and Industrial Research(CSIR),Government of India for the Senior Research fellowship(No.09/1156(0004)/18-EMR-I).
文摘An adaptable, energy efficient chemical process is employed to synthesize Cu^2+engrafted MgAl2O4 nanoparticles(Mg1-xCuxAl2O4, x = 0, 0.1, 0.3, 0.5 abbreviated as MCA0, MCA1, MCA3,and MCA5 respectively), using chelating ligand and the calcination temperature was determined by the thermogravimetric analysis of the precursor mass.They acted as good fluoride adsorbent in the presence of co-ions, different pH(2–11) via chemisorption revealed from Fourier-transform infrared spectroscopy(FTIR) and photodegraded Methylene Blue(MB).The satisfactory results were for MCA1(specific surface area 25.05 m^2/g) with 97%fluoride removal at pH 7.0 for the 10 mg/L initial fluoride concentration for 1.5 g/L adsorbent dose with 45 min contact time obeying the Langmuir isotherm model with negative thermodynamic parameters and 4 mmol of MCA3 with 98.51% photodegradation for 10-5 mol/L MB solution obeying pseudo-second-order and pseudo-first-order kinetics respectively.The proposed photodegradation mechanism of MB was established by the FTIR and high-performance liquid chromatography(HPLC) analysis.The nanoparticles are cubic, estimated through X-ray diffraction(XRD) and transmission electron microscopy(TEM) analysis.The band gap energies, grain size, and the effective working pH were estimated by diffuse reflectance spectra(DRS), scanning electron microscope(SEM), and zero-point potential analysis respectively.A soil candle with MCA1 also fabricated for the household purpose and tested with some fluorinated field samples.The MCA3 was able to enhance the latent fingerprint on smooth surfaces.
