A review on Data Mining Applications to the Performance of Stock Marketing

Setty, D. Venugopal; Rangaswamy, T.M.; Subramanya, K. N.

doi:10.5120/88-187

Cited by 16 publications

(7 citation statements)

References 4 publications

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“…In general, neural network modeling is among data processing techniques called data mining. Setty et al [24] reviewed the applications of data mining techniques to evaluate the performance of stock markets and concluded that there is a rising gap between storage and retrieval systems. Since storage is more powerful, a technological leap needs to be made to prioritize information about end-user problems.…”

Section: Performances Of Neural Network Modelsmentioning

confidence: 99%

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

Mahmoudabadi¹,

Kanaani

Ghazimahalleh³

2022

HighTech. Innov. J.

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Investment experts, who deal with stock price estimation, commonly look for the most accurate and appropriate statistical techniques to make decisions on investment. The aim of this study is to improve the accuracy of stock price prediction models through modifying the structure of a combined neural network model with time-series data, in which the main contribution is to insert the time-series analysis prediction into the hidden layer of the neural network. The proposed structure is made up of neural networks and time-series analysis, with variable reduction used to remove attributes with inter-correlations. Data has been collected over six years (72 months) from the Iranian stock market, including the number of trades, new-coin price, gold-18 price, US Dollar and Euro equivalent currencies, oil-index price, Brent-oil price, industry index, and balanced stock index, followed by developing the prediction models. Comparing the performance criteria of the proposed structure to the traditional ones in terms of the mean square and mean absolute errors revealed that inserting time-series estimated variables into hidden layers would improve the performance of neural network models to estimate stock prices for making investment decisions. Doi: 10.28991/HIJ-2022-03-01-05 Full Text: PDF

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Section: Performances Of Neural Network Modelsmentioning

confidence: 99%

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

Mahmoudabadi¹,

Kanaani

Ghazimahalleh³

2022

HighTech. Innov. J.

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“…If a company makes profit, then all shareholders get share of this profit according to their percentage of ownership and their stock price goes up. In the same way if the company is in loss then all shareholders have share in loss as well and their stock price goes down ( Setty, Rangaswamy & Subramanya, 2010 ).…”

Section: Key Conceptsmentioning

confidence: 99%

News sensitive stock market prediction: literature review and suggestions

Usmani¹,

Shamsi²

2021

PeerJ Computer Science

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Stock market prediction is a challenging task as it requires deep insights for extraction of news events, analysis of historic data, and impact of news events on stock price trends. The challenge is further exacerbated due to the high volatility of stock price trends. However, a detailed overview that discusses the overall context of stock prediction is elusive in literature. To address this research gap, this paper presents a detailed survey. All key terms and phases of generic stock prediction methodology along with challenges, are described. A detailed literature review that covers data preprocessing techniques, feature extraction techniques, prediction techniques, and future directions is presented for news sensitive stock prediction. This work investigates the significance of using structured text features rather than unstructured and shallow text features. It also discusses the use of opinion extraction techniques. In addition, it emphasizes the use of domain knowledge with both approaches of textual feature extraction. Furthermore, it highlights the significance of deep neural network based prediction techniques to capture the hidden relationship between textual and numerical data. This survey is significant and novel as it elaborates a comprehensive framework for stock market prediction and highlights the strengths and weaknesses of existing approaches. It presents a wide range of open issues and research directions that are beneficial for the research community.

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“…Data mining is a method of extracting unknown projecting information from large databases which is a widespread technology that helps organizations to focus on the most important information in data repositories with great potential [7,8,9,10]. Data analysis tools predict future trends and behavior, helping organizations in active business solutions to knowledge driven decisions [6,11,12].…”

Section: Data Miningmentioning

confidence: 99%

Efficient Predictive Model for Determining Critical Factors Affecting Commodity Price: The Case of Coffee in Ethiopian Commodity Exchange (ECX)

Degife¹,

Sinamo²

2019

IJIEEB

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In this paper, we have focused on the data mining technique on market data to establish meaningful relationships or patterns to determine the determinate critical factors of commodity price. The data is taken from Ethiopia commodity exchange and 18141 data sets were used. The dataset contains all main information. The hybrid methodology is followed to explore the application of data mining on the market dataset. Data cleaning and data transformation were used for preprocessing the data. WEKA 3.8.1 data mining tool, classification algorithms are applied as a means to address the research problem. The classification task was made using J48 decision tree classification algorithms, and different experimentations were conducted. The experiments have been done using pruning and unpruning for all attributes. The developed models were evaluated using the standard metrics of accuracy, ROC area. The most effective model to determine the determinate critical factors for the commodity has an accuracy of 88.35% and this result is a good experiment result. The output of this study is helpful to support decisionmaking activities in the area of the Ethiopia Commodity Exchange. The study support commodity suppliers to take care of the determinant factors work towards maintaining quality. Ethiopia Commodity Exchange (ECX), as the main facilitator of commodity exchanges, can also use the model for setting price ranges and regulations.

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A review on Data Mining Applications to the Performance of Stock Marketing

Cited by 16 publications

References 4 publications

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

News sensitive stock market prediction: literature review and suggestions

Efficient Predictive Model for Determining Critical Factors Affecting Commodity Price: The Case of Coffee in Ethiopian Commodity Exchange (ECX)

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