Stock price trend prediction using Artificial Neural Network techniques: Case study: Thailand stock exchange

Lertyingyod, Weerachart; Benjamas, Nunnapus

doi:10.1109/icsec.2016.7859878

Cited by 16 publications

(10 citation statements)

References 7 publications

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“…To take full advantage of the strengths of advanced machine learning techniques to produce broader impacts, effective practical implementations of predictive systems must incorporate the use of innovative technologies. Stock prices prediction can be transferred to two types of problems: (1) decision making or classification problems for price trend prediction, such as fuzzy rule-based systems (ElAal et al, 2012), neural networks (ElAal et al, 2012, Lertyingyod & Benjamas, 2017, and random forests with imbalance learning (Zhang et al, 2018), and (2) time series prediction (TSP) problems for price value prediction. Various machine learning techniques have been applied for TSP problems (Jadhav et al, 2015,He & Qin, 2010.…”

Section: Machine Learning Techniques For Computational Financementioning

confidence: 99%

Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA

Zheng

2021

Expert Systems with Applications

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Section: Machine Learning Techniques For Computational Financementioning

confidence: 99%

Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA

Zheng

2021

Expert Systems with Applications

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“…Traditional forecasting methods have the limitations to balance the randomness and regularity of controlling price changes [19]. In recent years, the artificial neural network (ANN) has achieved remarkable results in the field of artificial intelligence algorithm whose predictive analysis capability has greatly promoted the application of technologies like big data.…”

Section: Research On Stock Forecastingmentioning

confidence: 99%

Stock Forecasting Model FS-LSTM Based on the 5G Internet of Things

Hua

et al. 2020

Wireless Communications and Mobile Computing

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This paper analyzed the development of data mining and the development of the fifth generation (5G) for the Internet of Things (IoT) and uses a deep learning method for stock forecasting. In order to solve the problems such as low accuracy and training complexity caused by complicated data in stock model forecasting, we proposed a forecasting method based on the feature selection (FS) and Long Short-Term Memory (LSTM) algorithm to predict the closing price of stock. Considering its future potential application, this paper takes 4 stock data from the Shenzhen Component Index as an example and constructs the feature set for prediction based on 17 technical indexes which are commonly used in stock market. The optimal feature set is decided via FS to reduce the dimension of data and the training complexity. The LSTM algorithm is used to forecast closing price of stock. The empirical results show that compared with the LSTM model, the FS-LSTM combination model improves the accuracy of prediction and reduces the error between the real value and the forecast value in stock price prediction.

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“…And these two indicators define different algorithms. Weerachart and N.Benamas [3 ] presented a predictive model that uses Data Mining techniques to forecast share price trends.The author used the Gain Ratio Attribute in this study to compare the efficacy of feature selection with the Ranker Search Method and Wrapper Selection using Greedy…”

Section: Priyanka Garg Santosh K Vishwakarmamentioning

confidence: 99%

An Efficient Prediction of Share Price using Data Mining Techniques

Garg*¹,

Vishwakarma²

2019

IJEAT

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The prediction of share prices is the function of deciding the future price of a company stock or other commercial tool traded. Prediction of some movements allowed from some patterns can be found. People are always attracted to invest in share market and stock exchanges as they provide huge financial profits, which is also an important for finance research. Prediction of share price is very difficult issue it depends upon such huge numbers of factors such organization financial status and national policy and so on. Nowadays stock costs are influenced because of numerous reasons such as organizationrelated news, political, socially efficient conditions and cataclysmic events. Many studies have been performed for the prediction of stock index value and daily direction of change in the stock index. Such huge numbers of models have been created for foreseeing the future stock costs yet everyone has their own weaknesses. This paper expects to study, develop and assess different techniques so as to foresee future stock trades. The experimental results states that different classification techniques can be successfully deploy for share price prediction.

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Stock price trend prediction using Artificial Neural Network techniques: Case study: Thailand stock exchange

Cited by 16 publications

References 7 publications

Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA

Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA

Stock Forecasting Model FS-LSTM Based on the 5G Internet of Things

An Efficient Prediction of Share Price using Data Mining Techniques

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