The use of data mining and neural networks for forecasting stock market returns

Enke, David; Thawornwong, Suraphan

doi:10.1016/j.eswa.2005.06.024

Cited by 393 publications

(160 citation statements)

References 45 publications

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“…They are especially valuable where inputs are highly correlated, missing, or there are non-linear systems [11]. Artificial neural networks are currently extensively used in various technical SHS Web of Conferences 39, 01028 (2017) DOI: 10.1051/shsconf/20173901028 IES2017 applications and become a common method for providing various solutions in optimization, regression and problem estimation [12].…”

Section: Shs Web Of Conferences 39 01028 (2017) Doi: 101051/shsconfmentioning

confidence: 99%

Effect of the economic outturn on the cost of debt of an industrial enterprise

Vochоzka

2017

SHS Web Conf.

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Abstract. The cost of debt is referred to as the key factor determining profitability. It is a decisive factor in decision making of the management, especially in strategy development. The purpose of this paper is to establish the relationship between the volume of debt and the economic outturn of industrial enterprises. Using artificial neural networks, the relationship between interest costs and three profit categories is examined. Data of 5622 Czech processing enterprises in the years 2015-2017 are used. Multilayer perceptron neural networks and neural networks of basic radial functions are used for processing. A total of 10,000 neural structures are generated for each cost-interest relationship and the corresponding profit, of which 5 are retained, showing the best results. The results indicate that in all cases of profit there is no dependence between the interest and the amount of profit generated. Profiting companies do not get debt cheaper than other businesses.

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Section: Shs Web Of Conferences 39 01028 (2017) Doi: 101051/shsconfmentioning

confidence: 99%

Effect of the economic outturn on the cost of debt of an industrial enterprise

Vochоzka

2017

SHS Web Conf.

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“…Along clustering, classification and neural networks were employed by Enke [18] with the aim of obtaining accurate results in forecasting the stock market prices. A review of the clustering methods used in finance was proposed by Cai [19], showing practical examples to emphasize the differences between these methods from different angles.…”

Section: Related Workmentioning

confidence: 99%

A Clustering Of Listed Companies Considering Corporate Governance And Financial Variables

Moldovan

Moca

2015

International Conference KNOWLEDGE-BASED ORGANIZATION

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“…Predicting the stock market due to its importance and popularity among the masses and also small and large companies due to financial benefits and its low risk is a growing topic in research [1]. Despite the risk of falling too much value per share due to market fluctuations rarely happens, but again, the risk is there.…”

Section: Introductionmentioning

confidence: 99%

A Linear Regression Approach to Prediction of Stock Market Trading Volume: A Case Study

Gharehchopogh¹,

Bonab²,

Khaze³

2013

IJMVSC

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Predicting daily behavior of stock market is a serious challenge for investors and corporate stockholders and it can help them to invest with more confident by taking risks and fluctuations into consideration. In this paper, by applying linear regression for predicting behavior of S&P 500 index, we prove that our proposed method has a similar and good performance in comparison to real volumes and the stockholders can invest confidentially based on that.

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The use of data mining and neural networks for forecasting stock market returns

Cited by 393 publications

References 45 publications

Effect of the economic outturn on the cost of debt of an industrial enterprise

Effect of the economic outturn on the cost of debt of an industrial enterprise

A Clustering Of Listed Companies Considering Corporate Governance And Financial Variables

A Linear Regression Approach to Prediction of Stock Market Trading Volume: A Case Study

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