Analysis of Stock Market using Streaming data Framework

Umadevi, K. S.; Gaonka, Abhijitsingh; Kulkarni, Ritwik; Kannan, R. Jagadeesh

doi:10.1109/icacci.2018.8554561

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…In this work, Umadevi et al (2018) have made an effort to develop a stock exchange forecasting system that takes into account various equity-specific factors. The equity ratings were obtained, and then the analysis was performed.…”

Section: Methodsmentioning

confidence: 99%

A systematic literature survey on recent trends in stock market prediction

Balasubramanian,

P.,

Badarudeen

et al. 2024

PeerJ Computer Science

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Prediction of the stock market is a challenging and time-consuming process. In recent times, various research analysts and organizations have used different tools and techniques to analyze and predict stock price movements. During the early days, investors mainly depend on technical indicators and fundamental parameters for short-term and long-term predictions, whereas nowadays many researchers started adopting artificial intelligence-based methodologies to predict stock price movements. In this article, an exhaustive literature study has been carried out to understand multiple techniques employed for prediction in the field of the financial market. As part of this study, more than hundreds of research articles focused on global indices and stock prices were collected and analyzed from multiple sources. Further, this study helps the researchers and investors to make a collective decision and choose the appropriate model for better profit and investment based on local and global market conditions.

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Section: Methodsmentioning

confidence: 99%

A systematic literature survey on recent trends in stock market prediction

Balasubramanian,

P.,

Badarudeen

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…The potential for AI to reduce the frequency and impact of cyber-attacks is highlighted, making it a valuable tool for organizations seeking to improve their security and efficiency [14].in their study, Umadevi et al (2018), utilized analytical techniques to develop a prediction model for stock market data. Using six months of stock prices for Google, Apple, and Microsoft, they analyzed four attributes (low, high, open, and close) and employed stock scores and candlelight plots to visualize the data parameters [15]. Gegic et al (2019), utilized machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and Random Forest to predict second-hand car prices in Bosnia-Herzegovina.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey on Business Intelligence Approach Based on Deep Learning

2023

IJCCCE

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The article provides an overview of two recent developments in technology: Business Intelligence (BI) and Deep Learning (DL). In order to support decision-making processes, BI entails gathering, integrating, and analyzing data from various sources, while DL uses artificial neural networks to learn and generate predictions from complicated datasets. This paper introduces the concepts and principles and highlights recent developments and applications in different domains of research: education, organizations, stock market, forecasting, decision-making in real-time, and security. However, the fundamental problem with the business intelligence approach is that there is no learning involved. Other limitations and challenges include the capacity that affects the data analysis process, the variety of data in results, and the need for a complete presentation of results in the form of dashboards, scorecards, reports, and portals. The approach choice hinges on the problem's context and requirements and the nature and characteristics of the data. Although BI and DL are widespread, alternative methods may suit well too, such as machine learning, data mining, and statistical analysis. Justifying the selection based on precise needs and goals is crucial. Recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent units (GRU), and Business intelligence tools are used in the research problem to address these limitations and explore the potential advantages and difficulties of integrating BI and DL to achieve an advantage in a given sector. Index Terms— Business Intelligence Tools; decision making; deep learning algorithms.

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Stock Market Prediction Using a Hybrid Model

Akhtar

Khursheed

2020

Communications in Computer and Information Science

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Analysis of Stock Market using Streaming data Framework

Cited by 10 publications

References 9 publications

A systematic literature survey on recent trends in stock market prediction

A systematic literature survey on recent trends in stock market prediction

A Survey on Business Intelligence Approach Based on Deep Learning

Stock Market Prediction Using a Hybrid Model

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