Stock market prediction: A big data approach

Attigeri, Girija; Pai, M. M. Manohara; Pai, Radhika M.; Nayak, Aparna

doi:10.1109/tencon.2015.7373006

Cited by 64 publications

(25 citation statements)

References 13 publications

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“…The authors in [13] used financial news and Twitter data to make future predictions about stock values and correlation was used to find the relationship between stock values and sentiments values derived from news, tweets. They have developed the model to make predictions in real time, using Big Data Analytics by applying machine learning algorithms [33,34].…”

Section: B Big Data Framework and Machine Learning Techniquesmentioning

confidence: 99%

“…The stock exchange is fully packed with unreliability and influenced by numerous factors [13]. Share market is one of the leading unpredictable places of high interest within the finance and business [4].…”

Section: Introductionmentioning

confidence: 99%

“…If online news or tweets for a specific firm or company is positive, capitalist get the firm's stock to obtain profit else sell the equivalent stock [9]. It's forever ascertained that stock exchange values are always reaching the utmost low or maximum top values if financial news is broadcasting headlines [9].In last twenty years, with the expansion of storing and trailing systems [13], an enormous quantity of past data is out there for examination hence machine learning techniques elected as the main weapon for innovative jobs. Previously, several different machine learning techniques are applied with varying level of achievement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Short Term Stock Movements with Big Data and Market Sentiments Analytics

Kalra¹,

Gupta²,

Prasad³

2019

IJITEE

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Machine Learning Techniques and Big Data analytics are two central points of data science. Big Data is important for organizations to gain insights into it and machine learning techniques are one of the substantial assets for analyzing a massive amount of data. In this paper, a framework has been proposed to improve the short term stock trend prediction accuracy using Logistic Regression model by means of qualitative and quantitative data. This paper makes a comprehensive survey of stock market trend prediction with the accumulation of various data sources by applying machine learning techniques and by using big data analytics approach. The model has been implemented in Big data Framework with Hadoop and Apache Spark. For qualitative data Tweets sentiments and news sentiments has been taken in to account and for quantitative data Google trends and historical data are considered. The proposed system has enhanced the prediction accuracy about 3-4 % in comparison to existing models by supplying Google trend as input data in addition to market sentiments and historical data. The implemented model can help the investors to take short term decisions to make money in the security market and the survey would help in finding the most effective resources which overly influence the stock prices.

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Section: B Big Data Framework and Machine Learning Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Short Term Stock Movements with Big Data and Market Sentiments Analytics

Kalra¹,

Gupta²,

Prasad³

2019

IJITEE

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“…When research focuses on a specific area, it becomes necessary to focus on data pertinent to the research area. (Attigeri, MM, Pai, & Nayak, 2015) suggested that automatic labelling step could be improved for better sentiment calculations to achieve the required dataset which enhances the accuracy of the classification. Thus, the researchers used cumulative assessment of the sentiments regarding news article or tweets for sentiment analysis.…”

Section: Labelling Techniquementioning

confidence: 99%

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Alkubaisi¹,

Kamaruddin²,

Husni³

2018

CIS

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Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naïve Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.

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“…Current stock market classification models are still suffering low accuracy in classification [41,19,7]. The low accuracy in classification have direct effects on the reality and reliability of stock market indicators like a series of statistical figures and financial reports which explain the stock behavior in existing stock market [6,11,27]. There are many factors that affect the accuracy of classification model results such as features of data, sample size, period of collecting data, and data classification techniques [5,23,32,40].…”

Section: Introductionmentioning

confidence: 99%

Conceptual Framework for Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naïve Bayes Classifiers

Alkubaisi¹,

Kamaruddin²,

Husni³

2018

IJET

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Sentiment analysis has gained a lot of importance in last decade especially on the availability of data from Twitter that has created more interest for research in this field. Nevertheless, stock market classification models still suffer less accuracy and this has affected negatively the stock market indicators. In this paper, a new framework related to sentiment analysis from Twitter posts is proposed. The proposed framework represents an improved design of classification model that works to improve the classification accuracy to support decision makers in the domain of stock market exchange. This model starts with data collection part and in second phase filtration is done on data to get only the relevant data. The most important phase is the labelling part in which polarity of data is determined and negative, positive or neutral values are assigned to statements of people. The fourth part is the classification phase in which suitable patterns of stock market will be identified by hybridizing NBCs. The last phase is performance and evaluation. This study proposes to a Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification, hence represents a useful study for investors, companies and researchers and will help them to formulate their policies according to sentiments of people.

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Stock market prediction: A big data approach

Cited by 64 publications

References 13 publications

Short Term Stock Movements with Big Data and Market Sentiments Analytics

Short Term Stock Movements with Big Data and Market Sentiments Analytics

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Conceptual Framework for Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naïve Bayes Classifiers

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