Scientometric review and analysis of recent approaches to stock market forecasting: Two decades survey

Kehinde, T.O.; Chan, Felix T. S.; Chung, Sung-Taek

doi:10.1016/j.eswa.2022.119299

Cited by 33 publications

(10 citation statements)

References 124 publications

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“…Various tools, such as VOSviewer, Gephi, CiteSpace, Sci2, and HistCite, serve this purpose. Gephi, an open-source program, visualizes graphs and networks, while CiteSpace analyzes trends in academic literature (Kehinde et al, 2023).…”

Section: Choice Of Science Mapping Tool: Navigating the Scientific La...mentioning

confidence: 99%

Exploring the Evolution of Auditing and Digital Accounting Research within the Digital Business Landscape: A Bibliometric Analysis

Abu Huson

2024

Preprint

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This study aims to explore significant present and future trends in accounting and corporate sustainability by examining publications from the Web of Science database spanning from 2010 to 2023. With a particular focus on research related to the digital business environment, the study employs bibliometric analysis grounded in the resource-based view theory to investigate the evolution of auditing and digital accounting research. Utilizing the VOSviewer tool, the study analyzes keyword relationships and their geographical distribution based on a comprehensive examination of 700 publications from the Web of Science. The findings highlight the transformative impact of auditing and digital accounting on business operations in response to rapid digitization. Key clusters identified shed light on digital transformation and the intersection between information technology and corporate management, revealing the evolving research landscape. The study discusses both practical and theoretical implications arising from these findings.

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Section: Choice Of Science Mapping Tool: Navigating the Scientific La...mentioning

confidence: 99%

Exploring the Evolution of Auditing and Digital Accounting Research within the Digital Business Landscape: A Bibliometric Analysis

Abu Huson

2024

Preprint

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“…The stock market is the market for trading or making investments in stocks. It is sometimes referred to as the equity market and is a pool of financial organizations where regular buying and selling of shares and securities occur [1]. Therefore, simply stated, stock market investing is a process of buying and selling stocks or buying and selling ownership in a company, as purchasing the stock of a corporation entitles an investor to own part of the corporation, with money-making or money-loss potential.…”

Section: What Is Stock Market Investing?mentioning

confidence: 99%

The Fundamentals of Stock Market Investing in the Twenty-First Century

Tairu Bello

2024

Financial Literacy in Today´s Global Market

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This chapter discusses the fundamentals of stock marketing investing in the twenty-first century. In a nutshell, this chapter seeks to provide a fundamental understanding of stock market investing to interested laypersons across the world. It examines the various issues of critical importance in stock market investing. It dissects the experience of beginners, the most vulnerable participants in the stock market, and equips them with some knowledge cushion. It discusses several strategies for managing the various pitfalls inherent in stock market investing. Finally, it discussed the revolutionary impact of technology on stock market investing.

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“…The prediction of stock prices has always been challenging due to the dynamic and complex nature of financial markets ( Kehinde, Chan & Chung, 2023 ; Sheth & Shah, 2023 ). Researchers and analysts in the financial industry have been looking for ways to increase the reliability of stock price prediction models ( Chudziak, 2023 ; Han, Kim & Enke, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

Bagalkot,

Naik

2024

PeerJ Computer Science

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Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to stock price volatility. Most studies examined the manual method for parameter selection in GARCH and ARIMA models. These procedures are time-consuming and based on trial and error. To overcome this, we considered a GWO method for finding the optimal parameters in GARCH and ARIMA models. The motivation behind considering the grey wolf optimizer (GWO) is one of the popular methods for parameter optimization. The novel GWO-based parameters selection approach for GARCH and ARIMA models aims to improve stock price prediction accuracy by optimizing the parameters of ARIMA and GARCH models. The hierarchical structure of GWO comprises four distinct categories: alpha (α), beta (β), delta (δ) and omega (ω). The predatory conduct of wolves primarily encompasses the act of pursuing and closing in on the prey, tracing the movements of the prey, and ultimately launching an attack on the prey. In the proposed context, attacking prey is a selection of the best parameters for GARCH and ARIMA models. The GWO algorithm iteratively updates the positions of wolves to provide potential solutions in the search space in GARCH and ARIMA models. The proposed model is evaluated using root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE). The GWO-based parameter selection for GARCH and ARIMA improves the performance of the model by 5% to 8% compared to existing traditional GARCH and ARIMA models.

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Scientometric review and analysis of recent approaches to stock market forecasting: Two decades survey

Cited by 33 publications

References 124 publications

Exploring the Evolution of Auditing and Digital Accounting Research within the Digital Business Landscape: A Bibliometric Analysis

Exploring the Evolution of Auditing and Digital Accounting Research within the Digital Business Landscape: A Bibliometric Analysis

The Fundamentals of Stock Market Investing in the Twenty-First Century

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

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