Improving the Criteria of the Investment on Stock Market Using Data Mining Techniques: The Case of S&amp;P500 Index

Montenegro, Carlos; Molina, Marco

doi:10.18178/ijmlc.2020.10.2.936

Cited by 6 publications

(5 citation statements)

References 29 publications

(40 reference statements)

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“…, 2020). The data processing process mainly includes four stages: problem definition, data preparation, pattern extraction, result from interpretation and evaluation (Montenegro and Molina, 2020; Chen and Liu, 2021). Figure 2 shows its basic workflow. Problem definition .…”

Section: Methodsmentioning

confidence: 99%

“…According to the results of the processed data, it may be necessary to go back to a step to redo the operation (Yildirim et al, 2018;Zinovyeva et al, 2020). The data processing process mainly includes four stages: problem definition, data preparation, pattern extraction, result from interpretation and evaluation (Montenegro and Molina, 2020;Chen and Liu, 2021). Figure 2 shows its basic workflow.…”

Section: Data Mining Based On Rbf Neural Network Algorithmmentioning

confidence: 99%

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Analyzing the Internet financial market risk management using data mining and deep learning methods

Feng

Xiao-e

2021

JEIM

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PurposeTo identify and analyze the occurrence of Internet financial market risk, data mining technology is combined with deep learning to process and analyze. The market risk management of the Internet is to improve the management level of Internet financial risk, improve the policy of Internet financial supervision and promote the healthy development of Internet finance.Design/methodology/approachIn this exploration, data mining technology is combined with deep learning to mine the Internet financial data, warn the potential risks in the market and provide targeted risk management measures. Therefore, in this article, to improve the application ability of data mining in dealing with Internet financial risk management, the radial basis function (RBF) neural network algorithm optimized by ant colony optimization (ACO) is proposed.FindingsThe results show that the actual error of the ACO optimized RBF neural network is 0.249, which is 0.149 different from the target error, indicating that the optimized algorithm can make the calculation results more accurate. The fitting results of the RBF neural network and ACO optimized RBF neural network for nonlinear function are compared. Compared with the performance of other algorithms, the error of ACO optimized RBF neural network is 0.249, the running time is 2.212 s, and the number of iterations is 36, which is far less than the actual results of the other two algorithms.Originality/valueThe optimized algorithm has a better spatial mapping and generalization ability and can get higher accuracy in short-term training. Therefore, the ACO optimized RBF neural network algorithm designed in this exploration has a high accuracy for the prediction of Internet financial market risk.

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Section: Methodsmentioning

confidence: 99%

Section: Data Mining Based On Rbf Neural Network Algorithmmentioning

confidence: 99%

Analyzing the Internet financial market risk management using data mining and deep learning methods

Feng

Xiao-e

2021

JEIM

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“…Nevertheless, recent articles and research emphasise the significance of understanding various aspects of the stock market. The study of Montenegro and Molina (2020) proposed a strategy that combines deep learning neural networks with feature selection analysis to support investment decisions in the stock market, showing promising results in improving decision making of investors. Dospatliev et al (2022) conducted novel empirical analysis to investigate how the Bulgarian stock market was affected by the COVID-19 pandemic.…”

Section: Review Of the Scientific Literaturementioning

confidence: 99%

Montenegrin Stock Exchange Market on a Short-Term Perspective

Backović

Karadžić

Gričar

et al. 2023

JRFM

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The objective of this study is to analyse the constitution of the emerging Montenegrin stock exchange. Four methodological time-series econometric steps are involved: the augmented Dickey–Fuller (ADF) test, run test, autocorrelation function (ACF) test, and Hurst test. The study utilises a daily data vector from 5 January 2004 to 20 June 2023, with a specific focus on the period encompassing the growth and peak of market stocks in 2007, followed by the significant 2008 financial crisis and subsequent developments thereafter. The analysis culminates on 28 May 2018, which is considered one of the lowest points in the Montenegrin stock exchange market in a comparative time-series assessment. The results of the tests conducted in this study do not provide empirical evidence supporting the random walk theory and its returns on aggregated shocks in the Montenegrin stock exchange market. By reviewing previous empirical studies and presenting new empirical findings, this study confirms the presence of stochastic trends in co-movements in finance, contributing to a deeper understanding of emerging stock exchange markets. Study implications support greater reliance on market efficiency, risk management, and portfolio diversification.

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“…Montenegro and Molina [4] started from the fact that the neural networks appeared superior to other methods in modeling nonlinear data and worked on the prediction of the day-to-day stock value of the S&P 500 Index. By creating a data set from the daily market activity values of the stocks between June 7, 2013, and June 6, 2018, for each company in the S&P 500 Index, the Deep Learning Neural Network method was used in the training of the network and the Feature Selection Analysis method in determining the behavioral tendencies of the companies.…”

Section: Studies Related To Efficiency Assessment In Stock Marketsmentioning

confidence: 99%

“…Processing big data of past investments in a specified field can be very useful for making efficient investment decisions. Data mining is one of these processing methods [4]. Stock exchanges, which can provide large amounts of historical data related to previous investments, are a suitable resource for applying data mining approaches [5].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Investment Behavior in the Iranian Stock Exchange during the COVID-19 Pandemic Using Hybrid DEA and Data Mining Techniques

Sarfaraz

Yazdi

Hanne

et al. 2022

Mathematical Problems in Engineering

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The main purpose of this paper is to investigate the effects of COVID-19 regarding the efficiency of industries based on data in the Tehran stock market. A hybrid model of Data Envelopment Analysis (DEA) and data mining techniques is used to analyze the investment behavior in Tehran stock market. Particularly during the COVID-19 pandemic, many companies face financial crises. That is why companies with inferior performance must be benchmarked with efficient companies. First, the financial data of investments on selective companies are analyzed using data mining approaches to recognize the behavioral patterns of investors and securities. Second, customers are clustered into 3 selling and 4 buying groups using data mining techniques. Then, the efficiency of active companies in stock exchange is evaluated using input-oriented DEA. The results indicate that, among 23 industries listed on the stock market in Iran, solely nine were efficient in 2019. Moreover, in 2020, the number of efficient industries further decreased to six industries. Comparing the obtained results with those of another study which was conducted in 2018 by other researchers revealed that COVID-19 strongly affects the performance of an industry and some industries which were efficient in the past such as the bank industry became inefficient in the following year.

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Improving the Criteria of the Investment on Stock Market Using Data Mining Techniques: The Case of S&P500 Index

Cited by 6 publications

References 29 publications

Analyzing the Internet financial market risk management using data mining and deep learning methods

Analyzing the Internet financial market risk management using data mining and deep learning methods

Montenegrin Stock Exchange Market on a Short-Term Perspective

Analyzing the Investment Behavior in the Iranian Stock Exchange during the COVID-19 Pandemic Using Hybrid DEA and Data Mining Techniques

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