scholarly journals Neural Networks for Option Pricing and Hedging: A Literature Review

2019 ◽  
Author(s):  
Johannes Ruf ◽  
Weiguan Wang
Keyword(s):  
Neural Networks ◽  
Literature Review ◽  
Option Pricing

Hybrid Option Pricing Using Neural Networks

2007 ◽  
Author(s):  
Anoop John ◽  
Prasun Banerjee ◽  
Vigil Francis
Keyword(s):  
Neural Networks ◽  
Option Pricing

Applications in Machine Learning

2010 ◽  
pp. 149-167 ◽  
Author(s):  
E. Parsopoulos Konstantinos ◽  
N. Vrahatis Michael
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Literature Review ◽  
General Framework ◽  
Computational Models ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Optimization Process ◽  
Learning Problems

This chapter presents the fundamental concepts regarding the application of PSO on machine learning problems. The main objective in such problems is the training of computational models for performing classification and simulation tasks. It is not our intention to provide a literature review of the numerous relative applications. Instead, we aim at providing guidelines for the application and adaptation of PSO on this problem type. To achieve this, we focus on two representative cases, namely the training of artificial neural networks, and learning in fuzzy cognitive maps. In each case, the problem is first defined in a general framework, and then an illustrative example is provided to familiarize readers with the main procedures and possible obstacles that may arise during the optimization process.

scholarly journals Review for Optimisation of Neural Networks With Genetic Algorithms and Design of Experiments in Stock Market Prediction

2019 ◽  
Vol 22 ◽  
pp. 15-21 ◽  
Author(s):  
Yunus Emre Midilli ◽  
Sergei Parshutin
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Exchange Rate ◽  
Literature Review ◽  
Stock Market ◽  
Design Of Experiments ◽  
Stock Market Prediction ◽  
Input Layer ◽  
And Performance ◽  
Exchange Rate Prediction

Neural networks are commonly used methods in stock market predictions. From the earlier studies in the literature, the requirement of optimising neural networks has been emphasised to increase the profitability, accuracy and performance of neural networks in exchange rate prediction. The paper proposes a literature review of two techniques to optimise neural networks in stock market predictions: genetic algorithms and design of experiments. These two methods have been discussed in three approaches to optimise the following aspects of neural networks: variables, input layer and hyper-parameters.

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