A Powerful Neural Network Method with Digital-contract Hints for Pricing Complex Options

2003 ◽  
Vol 7 (2) ◽  
pp. 139-146 ◽  
Author(s):  
Jun Lu ◽  
◽  
Hiroshi Ohta
Keyword(s):  
Neural Network ◽  
Auxiliary Information ◽  
Financial Data ◽  
Simulation Experiments ◽  
Neural Network Method ◽  
Robust Property ◽  
Network Methods ◽  
Parallel Forms ◽  
Network Method ◽  
The Common

Many researches have proved that common neural network methods outperform parametric methods for option pricing. However, performance of the common neural network method usually suffers from the non-stationary and noisy properties of observed financial data. In this paper, we propose some parametric digital-contract (DC) hints, which can be utilized as auxiliary information to guide a neural network’s learning process about target pricing formula, and thus can be expected to get a better pricing performance in the case of observed data with noise. The DC hints are incorporated into a neural network with serial and parallel forms. Some Monte Carlo simulation experiments are performed and demonstrated that both the two forms not only have the nonparametric method’s advantages like generalization and superior accuracy, but also have the parametric method’s robust property to financial data with noise. The results also show that these two forms have their own strengths and limitations.

scholarly journals Noise-robust classification with hypergraph neural network

2021 ◽  
Vol 21 (3) ◽  
pp. 1465
Author(s):  
Nguyen Trinh Vu Dang ◽  
Loc Tran ◽  
Linh Tran
Keyword(s):  
Neural Network ◽  
Semisupervised Learning ◽  
Reduction Technique ◽  
Learning Method ◽  
Learning Problem ◽  
Neural Network Method ◽  
Network Methods ◽  
Network Method ◽  
Noise Robust ◽  
Image Datasets

<p>This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph neural network are employed to solve the noisy label learning problem. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases. Moreover, the hypergraph neural network methods are at least as good as the graph neural network.</p>

scholarly journals INDONESIAN LANGUAGE CLASSIFICATION OF CYBERBULLING WORDS ON TWITTER USING ADABOOST AND NEURAL NETWORK METHODS

2021 ◽  
Vol 3 (2) ◽  
pp. 93-100
Author(s):  
Kristiawan Nugroho
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Negative Consequences ◽  
Accuracy Rate ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Methods ◽  
Network Method ◽  
Cyberbullying Detection

Cyberbullying is a very interesting research topic because of the development of communication technology, especially social media, which causes negative consequences where people can bully each other, causing victims and even suicide. The phenomenon of Cyberbullying detection has been widely researched using various approaches. In this study, the AdaBoost and Neural Network methods were used, which are machine learning methods in classifying Cyberbullying words from various comments taken from Twitter. Testing the classification results with these two methods produces an accuracy rate of 99.5% with Adaboost and 99.8% using the Neural Network method. Meanwhile, when compared to other methods, the results obtained an accuracy of 99.8% with SVM and Decision Tree, 99.5% with Random Forest. Based on the research results of the Neural Network method, SVM and Decision Tree are tested methods in detecting the word cyberbullying proven by achieving the highest level of accuracy in this study

Shunt Control on Smart Structures Using Genetic Algorithm and Neural Network Method.

2019 ◽  
Author(s):  
Venicio Silva Araujo ◽  
Guilherme Silva Prado ◽  
Heinsten Frederich Leal dos Santos
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Smart Structures ◽  
Neural Network Method ◽  
Network Method ◽  
Shunt Control

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

scholarly journals The approximation neural-network method for solving nonlinear multi-criteria inverse problems of geophysics

2021 ◽  
Vol 1715 ◽  
pp. 012045
Author(s):  
M I Shimelevich ◽  
E A Obornev ◽  
I E Obornev ◽  
E A Rodionov
Keyword(s):  
Neural Network ◽  
Inverse Problems ◽  
Neural Network Method ◽  
Network Method
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