Stock Trading by Modelling Price Trend with Dynamic Bayesian Networks

Jangmin, O; Lee, Jae Won; Park, Sung-Bae; Zhang, Byoung-Tak

doi:10.1007/978-3-540-28651-6_118

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…These variables include short term and long term interest rates, inflation rate, Foreign Direct Investment (FDI), unemployment rate, Gross Domestic Product (GDP), Consumer Price Index (CPI), Industrial Production (IP), Government Consumption (GC), Private Consumption (PC), Gross National Product (GNP), Money Supply, Oil Prices, Exchange Rates etc. [13], [22], [27], [39][40][41][42][43][44][45][46][47][48].…”

Section: The Important Variables Used In Predicting Share Performancementioning

confidence: 99%

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Kamley¹,

Jaloree²,

Thakur³

2016

IJECE

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Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.

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Section: The Important Variables Used In Predicting Share Performancementioning

confidence: 99%

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Kamley¹,

Jaloree²,

Thakur³

2016

IJECE

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“…In 2013, Zheng Li et al used DBN to explore the dependence structure of elements that influence stock prices [10]. And in 2014, Jangmin O built a price trend model under the DBN framework [7]. Auto regression is an important factor that contribute to the fluctuation of stock prices and has been studied among researchers in financial mathematics [14] [12] [13].…”

Section: Related Workmentioning

confidence: 99%

“…The method achieved wide application in gesture recognition [20] [17], acoustic recognition [3] [22], image segmentation [9] and 3D reconstruction [6]. The temporal evolving feature also makes the model suitable to model the stock market [7]. The classic DBN model assumes the observed variable only depend on latent variables, and we know for the instance of stock market, auto regression widely exists in neighboring observed stock prices due to the momentum of market atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Auto Regressive Dynamic Bayesian Network and Its Application in Stock Market Inference

Duan

2016

IFIP Advances in Information and Communication Technology

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In this paper, auto regression between neighboring observed variables is added to Dynamic Bayesian Network(DBN), forming the Auto Regressive Dynamic Bayesian Network(AR-DBN). The detailed mechanism of AR-DBN is specified and inference method is proposed. We take stock market index inference as example and demonstrate the strength of AR-DBN in latent variable inference tasks. Comprehensive experiments are performed on S&P 500 index. The results show the AR-DBN model is capable to infer the market index and aid the prediction of stock price fluctuation.

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Section: The Important Variables Used In Predicting Share Performancementioning

confidence: 99%

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Kamley¹,

Jaloree²,

Thakur

2016

IJECE

View full text Add to dashboard Cite

<p>Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.</p>

show abstract

Stock Trading by Modelling Price Trend with Dynamic Bayesian Networks

Cited by 10 publications

References 2 publications

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Auto Regressive Dynamic Bayesian Network and Its Application in Stock Market Inference

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

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