A Bayesian regularized artificial neural network for stock market forecasting

Ticknor, Jonathan L.

doi:10.1016/j.eswa.2013.04.013

Cited by 441 publications

(173 citation statements)

References 28 publications

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“…In their study, they found that the BRANN gave slightly better performance, but not significantly so. In many studies [8,[31][32][33], the BR training algorithm has given either moderate or the best performance in terms of comparison with other training algorithms. BRANNs have some important advantages, such as choice and robustness of model, choice of validation set, size of validation effort, and optimization of network architecture [13].…”

Section: Resultsmentioning

confidence: 99%

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

Kayrı

2016

MCA

270

159

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Abstract:The objective of this study is to compare the predictive ability of Bayesian regularization with Levenberg-Marquardt Artificial Neural Networks. To examine the best architecture of neural networks, the model was tested with one-, two-, three-, four-, and five-neuron architectures, respectively. MATLAB (2011a) was used for analyzing the Bayesian regularization and Levenberg-Marquardt learning algorithms. It is concluded that the Bayesian regularization training algorithm shows better performance than the Levenberg-Marquardt algorithm. The advantage of a Bayesian regularization artificial neural network is its ability to reveal potentially complex relationships, meaning it can be used in quantitative studies to provide a robust model.

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Section: Resultsmentioning

confidence: 99%

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

Kayrı

2016

MCA

270

159

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“…In fact, even if there are a large sample data, but it does not necessarily find rule, even if there is a statistical rule, but it is also not a typical. The other is the artificial intelligence technique such as artificial neural network (ANN) [3][4][5], genetic algorithm (GA) [6,7], and many hybrid intelligent algorithms [8][9][10][11]. The hybrid intelligent algorithms have more flexibility to solve the complex models, so more and more researchers tend to use them to deal with forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Hybrid Algorithm Based on PSO and BP for Stock Price Forecasting

Gao¹

2015

TOCSJ

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Stock price prediction is the main concern for financial firms and private investors. In this paper, we proposed a hybrid BP neural network combining adaptive PSO algorithm (HBP-PSO) to predict the stock price. HBP-PSO takes full use of the global searching capability of PSO and the local searching advantages of BP Neural Network. The PSO algorithm is applied for training the connection weights and thresholds of BP, in order to take advantage of BP, each particle in PSO swarm will be optimized by error correcting method of BP in probability. The trained BP neural network is used to predict the stock price. The empirical analysis using the real data of Chinese stock market demonstrates the feasibility and effectiveness of this method.

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“…[37][38][39]. In this paper, we choose Bayesian regularization mainly because the data in our study are scarce, and the Bayesian method can improve the performance of neural networks (by reducing the training iterations) [36]. One of our future works is to use other methods, such as GA and PSO, to optimize the weights in neural networks.…”

Section: Training Parameter Selection On the Bp Neural Networkmentioning

confidence: 99%

“…In this paper, we use regularization to limit the scale of network weights so that we can improve the generalization of neural networks. In particular, we consider Bayesian regularization, which is a method to estimate the regularization parameters based on Bayesian methods [36]. It is worth mentioning that there are many methods to improve or optimize the weights of the neural networks, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), etc.…”

Section: Training Parameter Selection On the Bp Neural Networkmentioning

confidence: 99%

A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China

2017

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Abstract:Air pollution has become one of the key environmental concerns in the urban sustainable development. It is important to evaluate the impact of air pollution on socioeconomic development since it is the prerequisite to enforce an effective prevention policy of air pollution. In this paper, we model the impact of air pollution on the urban economic development as a Multiple Criteria Decision Making (MCDM) problem. In particular, we propose a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis framework to evaluate multiple factors of air pollutants and economic development. Our method can overcome the drawbacks of conventional TOPSIS methods by using Bayesian regularization and the Back-Propagation (BP) neural network to optimize the weight training process. We have conducted a case study to evaluate our proposed framework.

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A Bayesian regularized artificial neural network for stock market forecasting

Cited by 441 publications

References 28 publications

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

An Improved Hybrid Algorithm Based on PSO and BP for Stock Price Forecasting

A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China

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