Research on investment portfolio model based on neural network and genetic algorithm in big data era

Abstract With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm.

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An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060420000219 ◽

2020 ◽

Vol 34 (3) ◽

pp. 430-443

Author(s):

A. Saravanan ◽

J. Jerald ◽

A. Delphin Carolina Rani

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Network Model ◽

Neural Network Model ◽

Network Models ◽

Manufacturing Cost ◽

Genetic Operators ◽

Neural Network Models ◽

Data Set ◽

The Neural Network

AbstractThe objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

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Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

Computational Intelligence and Neuroscience ◽

10.1155/2014/318524 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 6

Author(s):

Shipra Banik ◽

A. F. M. Khodadad Khan ◽

Mohammad Anwer

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Market ◽

Network Model ◽

Rough Set ◽

Neural Network Model ◽

Stock Exchange ◽

Machine Learning Techniques ◽

Applied Machine Learning ◽

The Neural Network

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

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Modelling Clearance Sales Outshopping Behaviour Using Neural Network Model

Intelligent Techniques in Recommendation Systems ◽

10.4018/978-1-4666-2542-6.ch012 ◽

2013 ◽

pp. 230-244

Author(s):

M. Hemalatha

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Network Theory ◽

Time Series Prediction ◽

Strategy Development ◽

Neural Network Modeling ◽

System A ◽

The Neural Network ◽

Neural Network Theory

The neural network is a very useful tool for approximation of a function, time series prediction, classification, and pattern recognition. If there is found to be a non-linear relationship between input data and output data, it is difficult to analyse the system. A neural network is very effective to solve this problem. This chapter studies the applied neural network model in relation to clearance sales outshopping behaviour. Since neural network theory can be applied effectively to this case, the authors have used neural network theory to recognise the retail area satisfaction and loyalty. To measure the impact among the retail area attributes, retail area satisfaction, and retail area loyalty, the authors have used the neural network model. In this chapter, they have treated twenty seven factors as the input signals into the input layer. Therefore, they find the weights between nodes in the relationship between the value of all twenty seven factors and the retail area satisfaction and loyalty. The development of the model by retail area attributes, and their interpretation, was facilitated by a collection of data across three trading areas. This neural network modeling approach to understand clearance sales outshopping behaviour provides retail managers with information to support retail strategy development.

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Prediction of Stock Market by BP Neural Network Model

Journal of Physics Conference Series ◽

10.1088/1742-6596/1744/4/042232 ◽

2021 ◽

Vol 1744 (4) ◽

pp. 042232

Author(s):

Benben Ruan

Keyword(s):

Neural Network ◽

Stock Market ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Bp Neural Network Model

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Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119837236 ◽

2019 ◽

Vol 2673 (4) ◽

pp. 108-116 ◽

Cited By ~ 1

Author(s):

Mostafa H. Tawfeek ◽

Karim El-Basyouny

Keyword(s):

Neural Network ◽

Linear Regression ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Safety Management ◽

Estimation Method ◽

Estimation Model ◽

The Neural Network ◽

Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

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