Deep Learning-based Stock Market Prediction

Malar, A. Christy Jeba; Priya, M. Deva; Kumar, M. Kavin; Arunsankar, S. Mangala; Bilal, K. V.; Karthik, S.

doi:10.1007/978-981-16-7118-0_60

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Volatility is the rate at which the price of a stock increases or decreases over a particular period. The volatility is calculated using (11):…”

Section: Volatilitymentioning

confidence: 99%

“…The financial market correlation structure was studied using the minimum spanning tree (MST) [9], [10]. The correlation-based network offers a visual representation of extracted data and multivariate time series, enabling the observation of market trends, particularly during crises [11]. Japanese and Asian markets are integrated, and Malaysia, New Zealand, Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  Australia, Hong Kong, and Singapore are highly integrated with the Japanese market [12].…”

Section: Introductionmentioning

confidence: 99%

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Analyzing interconnections between national stock exchange India and global markets using data analytics

Shaikh,

Ramadass

2023

IJEECS

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Financial networks have become increasingly complex and interconnected in the global economy. In this context, the use of data analytics has emerged as a valuable tool for understanding and analyzing the relationships between different financial markets. This paper presents a study using Pearson and partial correlation analysis to explore the interconnections between the National Stock Exchange India (NSEI) and global stock markets. The analysis aims to provide insights into the dynamics and patterns or cross-markets interactions and to identify potential opportunities for investment and risk management that to considering the world’s 43 major indices based on imports and exports. The historical data from 1st January 2008 to 30th June 2022 was obtained from the public domain. In this study, a Python framework was developed to investigate Pearson and partial correlation-based networks in financial markets. The results showed that these two types of networks are significantly different from each other. Furthermore, the study discovered that the NSEI is closely connected to the Singapore market and is integrated with other markets. These findings emphasize the complexities to financial market relationships and have important implications for investors and policymakers.

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“…Volatility is the rate at which the price of a stock increases or decreases over a particular period. The volatility is calculated using (11):…”

Section: Volatilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing interconnections between national stock exchange India and global markets using data analytics

Shaikh,

Ramadass

2023

IJEECS

View full text Add to dashboard Cite

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“…Christy Jeba Malar et al (2022), Stock experts and Finance experts are always working on this study in order to forecast future stock prices, which aids in selecting whether to sell or purchase stocks for profit. Stock markets encourage investment by pooling resources, allowing businesses to raise capital to expand their operations.…”

Section: Related Workmentioning

confidence: 99%

Iraqi Stock Market Prediction using Proposed Model of Convolution Neural Network

Abdulhussein¹,

Al-Anber²,

Atee³

2022

Journal of Computer Science

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The stock market, sometimes known as the share market, is a marketplace for stock sellers and buyers. A stock exchange is defined as a mechanism through which stockbrokers can purchase and sell shares, bonds, and other assets. Many businesses, regardless of their industries or domains, make their shares or stocks available on the stock exchange. For value investors, predicting the price direction of a company is crucial. Due to its volatile nature, forecasting the stock market is a challenging task. Accurate stock forecasting is essential for building important trading systems that help customers purchase and sell equities. Due to the dynamic nature and nonstationary nature of data, stock price prediction and modeling is a difficult endeavor. By decreasing investment risks, developing a successful stock prediction system would assist shareholders in making profitable investment decisions. Using deep learning-based methodologies, this research provides a deep learning-based strategy for dramatically boosting stock forecasting accuracy. This study is a comparison between the results of LeNet and the proposed model for the prediction of Iraqi stock marketing for five years (from 2017 to 2021). The results of the proposed model demonstrate the average training accuracy is 99%, the average validation accuracy is 95%, the average training loss is 0.28% and for validation, loss is 0.014%.

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Data Driven Artificial Neural Network LSTM Hybrid Predictive Model Applied for International Stock Index Prediction

Safari

2022

2022 8th International Conference on Web Research (ICWR)

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Deep Learning-based Stock Market Prediction

Cited by 3 publications

References 9 publications

Analyzing interconnections between national stock exchange India and global markets using data analytics

Analyzing interconnections between national stock exchange India and global markets using data analytics

Iraqi Stock Market Prediction using Proposed Model of Convolution Neural Network

Data Driven Artificial Neural Network LSTM Hybrid Predictive Model Applied for International Stock Index Prediction

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