Data Mining and Neural Network Techniques in Stock Market Prediction : A Methodological Review

Das, Debashish; Uddin, Mohammad Shorif

doi:10.5121/ijaia.2013.4109

Cited by 17 publications

(6 citation statements)

References 14 publications

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“…The research in data mining and neural networks techniques [7] has employed traditional methods that may not ensure the reliability of the prediction. In this paper, They basically go over the two techniques namely mining of data and the neural network in artificial intelligence.…”

Section: Literature Surveymentioning

confidence: 99%

Sentiment Analysis of Stocks Based on News Headlines Using NLP

Saxena¹,

Jain²,

Sharma³

et al. 2023

Atlantis Highlights in Intelligent Systems

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In todays' world everyone starting from a child to an adult is studying stocks and is finding ways to earn more by studying the patterns of the market. Stock market is a compound interrelated system of various investors. It fluctuates frequently and hence is hard to predict what is yet to come. All the companies worldwide rely on these forecasts and speculations so that they can increase their profits. Everyday news on the economic front plays a vital role in defining the jump or drop in the prices of stocks. The market news helps the investor excessively in determining his bidding as it is immensely rich in information. In this study, we extract useful information from news headlines of a particular company to investigate the immediate impact of it on the company's stock growth. Having the text based dataset we use NLP and compare two approaches using two different algorithms which both collectively determine the sentiment of the news headline (whether it is positive/negative/neutral) in lieu with the company stocks.

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Section: Literature Surveymentioning

confidence: 99%

Sentiment Analysis of Stocks Based on News Headlines Using NLP

Saxena¹,

Jain²,

Sharma³

et al. 2023

Atlantis Highlights in Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Considering the set of research papers included in the analysis, we observed the first approach of NN techniques to the stock market dates to the year 1991 (Trippi & Desieno, 1991). The range of applications of NNs in the stock market includes the prediction of stock market indices (Das & Uddin, 2013; Nayak et al, 2014; Ou & Wang, 2009), prediction of future signs of stock market returns (Lahmiri, 2011), prediction of stock price (Arasu et al, 2014; Hargreaves & Hao, 2013; Hsieh et al, 2011; Kim & Han, 2000), prediction of abnormal stock market returns (Safer, 2003), financial time‐series forecasting (Chen et al, 2006; Shaikh & Chhajed, 2012), stock market trading (Vanstone et al, 2010), and uncovering the predictive relationships of numerous financial and economic variables (Thawornwong & Enke, 2004). Macroeconomic variables, external events, twitter sentiments, and financial news, for instance, affecting stock markets are considered external factors.…”

Section: Applicability Of Data Mining Techniquesmentioning

confidence: 99%

Application and performance of data mining techniques in stock market: A review

Kaur

Dharni

2022

Intell Sys Acc Fin Mgmt

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Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.

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“…Neural network stock market prediction has achieved better results than conventional statistical techniques [13,14]. Schoeneburg [15] and Kaastra et al [16] used back-propagation neural networks to predict the stock market in the short term (days) and achieved 89% accuracy.…”

Section: Stock Market Prediction Using Social Mediamentioning

confidence: 99%

COVID-19 Pandemic Data Predict the Stock Market

Almehmadi¹

2021

Computer Systems Science and Engineering

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Unlike the 2007-2008 market crash, which was caused by a banking failure and led to an economic recession, the 1918 influenza pandemic triggered a worldwide financial depression. Pandemics usually affect the global economy, and the COVID-19 pandemic is no exception. Many stock markets have fallen over 40%, and companies are shutting down, ending contracts, and issuing voluntary and involuntary leaves for thousands of employees. These economic effects have led to an increase in unemployment rates, crime, and instability. Studying pandemics' economic effects, especially on the stock market, has not been urgent or feasible until recently. However, with advances in artificial intelligence (AI) and the inter-connectivity that social media provides, such research has become possible. In this paper, we propose a COVID-19-based stock market prediction system (C19-SM2) that utilizes social media. Our AI system enables economists to study how COVID-19 pandemic data influence social media and, hence, the stock market. C19-SM2 gathers COVID-19 infection and death cases reported by the authorities and social media data from a geographic area and extracts the sentiments and events that occur in that area. The information is then fed to the support vector machine (SVM) and random forest and random tree classifiers along with current stock market values. Then, the system produces a projection of the stock market's movement during the next day. We tested the system with the Dow Jones Industrial Average (DJI) and the Tadawul All Share Index (TASI). Our system achieved a stock market prediction accuracy of 99.71%, substantially higher than the 89.93% accuracy reported in the related literature; the inclusion of COVID-19 data improved accuracy by 9.78%.

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Data Mining and Neural Network Techniques in Stock Market Prediction : A Methodological Review

Cited by 17 publications

References 14 publications

Sentiment Analysis of Stocks Based on News Headlines Using NLP

Sentiment Analysis of Stocks Based on News Headlines Using NLP

Application and performance of data mining techniques in stock market: A review

COVID-19 Pandemic Data Predict the Stock Market

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