TY  - JOUR
EP  - 125380
KW  - Artificial intelligence
KW  -  financial forecasting
KW  -  deep learning
KW  -  stock market analysis
KW  - 
convolution neural network
KW  -  cryptocurrency
Y1  - 2023/11//
N2  - Artificial intelligence (AI)-based models have emerged as powerful tools in financial markets, capable of reducing investment risks and aiding in selecting highly profitable stocks by achieving precise predictions. This holds immense value for investors, as it empowers them to make data-driven decisions. Identifying current and future trends in multi-class forecasting techniques employed within financial markets, particularly profitability analysis as an evaluation metric is important. The review focuses on examining stud-ies conducted between 2018 and 2023, sourced from three prominent academic databases. A meticulous three-stage approach was employed, encompassing the systematic planning, conduct, and analysis of the se-lected studies. Specifically, the analysis emphasizes technical assessment, profitability analysis, hybrid mod-eling, and the type of results generated by models. Articles were shortlisted based on inclusion and exclusion criteria, while a rigorous quality assessment through ten quality criteria questions, utilizing a Likert-type scale was employed to ensure methodological robustness. We observed that ensemble and hybrid models with long short-term memory (LSTM) and support vector machines (SVM) are being more adopted for financial trends and price prediction. Moreover, hybrid models employing AI algorithms for feature engineering have great potential at par with ensemble techniques. Most studies only employ performance metrics and lack utilization of profitability metrics or investment or trading strategy (simulated or real-time). Similarly, research on multi-class or output is severely lacking in financial forecasting and can be a good avenue for future research.
TI  - A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis
A1  - Khattak, Bilal Hassan Ahmed
A1  - Shafi, Imran
A1  - Khan, Abdul Saboor
A1  - Soriano Flores, Emmanuel
A1  - García Lara, Roberto
A1  - Samad, Md. Abdus
A1  - Ashraf, Imran
SN  - 2169-3536
SP  - 125359
ID  - uninimx9698
UR  - http://doi.org/10.1109/ACCESS.2023.3330156
JF  - IEEE Access
AV  - public
VL  - 11
ER  -