Artificial intelligence and machine learning in finance: A bibliometric review

Ahmed, Shamima; Alshater, Muneer M.; Ammari, Anis El; Hammami, Helmi

doi:10.1016/j.ribaf.2022.101646

Cited by 189 publications

(68 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To ensure the relevance and validity of research, it is essential to identify and choose high-quality international scientific articles that provide original and reliable information and knowledge sources. Regarding artificial intelligence in the stock market, the Scopus database is systematically searched (Ahmed et al, 2022). We used the following protocol [TITLE-ABS-KEY (artificial AND intelligence+stock+market)].…”

Section: Research Methodology and Data Collectionmentioning

confidence: 99%

A Bibliometric Analysis of Artificial Intelligence-Based Stock Market Prediction

ALI¹,

SURI²

2022

EPESS

View full text Add to dashboard Cite

The primary purpose of this study is to conduct a scientometrics analysis of stock market forecasts based on artificial intelligence. This research examined 1,301 publications that were published between January 2002 and June 2022. We investigated 183 journal articles among 1,329 papers. In addition to entering the keywords into Scopus, a comprehensive dataset of relevant research papers was compiled. These papers discussed the optimization of investment portfolios, artificial intelligence-based stock market forecasts, investor emotions, and market monitoring. We found the most prolific documents by affiliation, the most prolific author, the most cited papers, nations, institutions, co-authorship maps, inter-country co-authorship maps, and keywords occurrences in this study. Co-authorship analysis network maps and keyword occurrence linkages are generated using the VOS-viewer software. According to our findings, it is evident from the review that the body of literature is becoming more specific and extensive. Primarily, neural networks, support vector machines, and neuro-fuzzy systems are employed to predict the future price of a stock market index based on the composite index's historical prices. Artificial intelligence techniques are able to consider challenges facing financial systems when forecasting time series. Our findings provide actionable guidance on how artificial intelligence can be used to predict stock market movements for market participants, including traders, investors, and financial institutions.

show abstract

Section: Research Methodology and Data Collectionmentioning

confidence: 99%

A Bibliometric Analysis of Artificial Intelligence-Based Stock Market Prediction

ALI¹,

SURI²

2022

EPESS

View full text Add to dashboard Cite

show abstract

“…ML is a technique that automates data analysis by resorting to the power of statistical tools [ 21 , 31 ] and has become a favoured framework to cope with big data by producing predictive models [ 24 ] across several fields [ 1 , 43 , 51 , 56 ]. However, those models are known for lacking interpretability and explainability [ 17 ] and this, in turn, reduces their accountability because issues relating to risk assessment and safe adoption are overlooked [ 39 ].…”

Section: Final Thoughtsmentioning

confidence: 99%

Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning

et al. 2022

View full text Add to dashboard Cite

In The hitchhiker’s guide to responsible machine learning, Biecek, Kozak, and Zawada (here BKZ) provide an illustrated and engaging step-by-step guide on how to perform a machine learning (ML) analysis such that the algorithms, the software, and the entire process is interpretable and transparent for both the data scientist and the end user. This review summarises BKZ’s book and elaborates on three elements key to ML analyses: inductive inference, causality, and interpretability.

show abstract

“…Hassanein and Mostafa [16] used the Web of Science database and retrieved 464 scientific articles on Islamic banking and finance published between 1990 and 2019, indicating weak collaboration between countries. Other studies explored AI and finance or banking like Shamima et al [19] who used the Scopus database and retrieved 348 documents published on Artificial intelligence and finance between 2011 and 2021. The authors indicated that AI was applied in forecasting bankruptcies, stock prices, and agricultural prices.…”

Section: Of 11mentioning

confidence: 99%

Artificial Intelligence in the Islamic Finance: A bibliometric Analysis

Tayachi¹,

Brahimi²,

Essafi³

et al. 2022

Preprint

View full text Add to dashboard Cite

The paper demonstrates the future of artificial intelligence in the Islamic Banks of Saudi Arabia which introduces with the meaning of artificial intelligence and the benefits which AI may bring with itself in the future. Further, how banks differ in their use of AI from the banks that do not use AI or use original intelligence (Artificial Neural Network) and the acknowledgement of 4 purely Islamic banks in KSA. The literature has analyzed the pros of AI in the Islamic banks taking into consideration machine learning, natural language processing, text mining through neural network of deep learning, algorithmic trading and robotics. It has further analyzed AI’s limitations with regards to its costs, technological unemployment, lack of human creativity and its accountability and regulation. Lastly, the literature has stated AI from the sharia’a point of view. In order to test hypothesis concerning future studies, it was important to collect data using purely Islamic banks. Upon various interpretations concerning AI’s accountability and its impact on sharia’a board, robot’s impact in the long run, AI’s ethics and its uses which people have already encountered and the time period for which AI may imitate human learning in the future- we find that generation Z and millennials are very influenced by AI and have positive views about its scope in the future. However, it is important to note that research limitations include no access to banking authorities due to the recovery after the pandemic of COVID-19 besides.

show abstract

Artificial intelligence and machine learning in finance: A bibliometric review

Cited by 189 publications

References 65 publications

A Bibliometric Analysis of Artificial Intelligence-Based Stock Market Prediction

A Bibliometric Analysis of Artificial Intelligence-Based Stock Market Prediction

Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning

Artificial Intelligence in the Islamic Finance: A bibliometric Analysis

Contact Info

Product

Resources

About