A novel model by evolving partially connected neural network for stock price trend forecasting

Chang, Pei‐Chann; Wang, Didi; Zhou, Chang-le

doi:10.1016/j.eswa.2011.07.051

Cited by 102 publications

(50 citation statements)

References 20 publications

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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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Section: Introductionmentioning

confidence: 99%

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

Gao¹

2015

TOCSJ

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“…Chann Chang et al (2012) suggested a new approach by Evolving Partially Connected Neural Networks (EPCNN s ) to forecast S&P500. They compared the performance of EPCCN s model with BPN, TSK fuzzy system and multiple regression analysis and stated that the EPCCN s do better than the other models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…That is the investors' duty to manage the opportunities correctly and keep up with the inconsistencies of the market. They should consider the unexpected events, continuous structural failures along with the market turbulence (Chann Chang, Wang& Le Zhou, 2012). Stock price movements are actually affected by many macro economics factors including political events, firms' guidelines, general economic situations, inventory price index, investors' expectations, institutional investors' selections and psychological factors.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Employment of Technical Indicators in Predicting Stock Price Index Variations Using Artificial Neural Networks (Case Study: Tehran Stock Exchange)

Aldin

Dehnavi

Entezari

et al. 2012

IJBM

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Stock price index is the initial significant factor influencing on investors' financial decision making. That's why predicting the exact movements of stock price index is considerably regarded. This study aims at evaluating the effectiveness of using technical indicators, such as Moving Average, RSI, CCI, MACD, etc in predicting movements of Tehran Exchange Price Index (TEPIX). An artificial neural network is employed for stock price index forecasting. The existing data are achieved from Tehran Stock Exchange. To capture the relationship between the technical indicators and the levels of the index in the market for the period under investigation, a back propagation neural network is used. The statistical and financial performance of this technique is evaluated and empirical results revealed that artificial neural networks are dominant tools for financial market predicting.

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“…Forecasting demand is the level of demand for the products that are expected to be realized for a certain period in the future. Basically forecasting approaches can be classified into two approaches: qualitative approach and quantitative approach (Memmedli, 2012) There are some solutions that are needed such as a good quality of initial data and in sufficient quantity, because principally, the method of prediction is to identify the pattern of data which is based on the existing data, so mathematically predicted results will be relatively accurate (Chang, 2012). One technique that can be considered in pattern recognition neural network for prediction is a clone.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Number of Patients Diseases Using Backpropagation

Rachmad

Anamisa

2016

MATEC Web of Conferences

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Forecasting with various types of disease is important for health centers, because it can be used to help the health center management in conducting strategic planning and decision making. Health Care Center Torjun in Indonesia has made estimationabout the number of patients with various types of diseases, such as Acute Respiratory Infections(ISPA), RA (Rheumatoid Arthritis), diarrhea, HT (Hypertension), Skin Allergies, Conjunctivitis, Asthma, Febrile, TB(Tuberculosis). Lung, scabies, Gastritis, typus and scarlet fever with reports the number of patients with certain diseases in the coming period and prepare the necessary needs both medical services and as well as drugs for use later. In this study, Artificial Neural Network (ANN) is one model that is used to identify patterns of images of people with various kinds of diseases. Backpropagation is one of the popular models of Neural Networkwhich is used for forecasting, prediction, and decision makers based on the input of data entry that has been studied in advance. The resultsis HT (0.35 %)with parameters for forecasting system using Neural Network Backpropagation is the best of the trial results that shows disease HT which are obtained from the experiments. They predict the number of patients with a disease that needs to be watched for in the coming period and prepare all the needs of both medical and medication needed to handle the number of people with the disease.

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A novel model by evolving partially connected neural network for stock price trend forecasting

Cited by 102 publications

References 20 publications

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

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

Evaluating the Employment of Technical Indicators in Predicting Stock Price Index Variations Using Artificial Neural Networks (Case Study: Tehran Stock Exchange)

Forecasting the Number of Patients Diseases Using Backpropagation

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