@misc{lin_stock_2023, address = {Rochester, NY}, type = {{SSRN} {Scholarly} {Paper}}, title = {Stock {Market} {Prediction} {Using} {Artificial} {Intelligence}: {A} {Systematic} {Review} of {Systematic} {Reviews}}, shorttitle = {Stock {Market} {Prediction} {Using} {Artificial} {Intelligence}}, url = {https://papers.ssrn.com/abstract=4341351}, doi = {10.2139/ssrn.4341351}, abstract = {There are many systematic reviews on predicting stock. However, each of them reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review and conclude the systematic reviews on AI and stock to provide particularly useful predictions for making future strategies for stock markets. Keywords that would fall under the broad headings of AI and stock prediction were looked up in two databases, Scopus and Web of Science. We screened 69 titles and read 43 systematic reviews which include more than 379 studies before retaining 10 of them.}, language = {en}, urldate = {2023-03-22}, author = {Lin, Coka Chinyang and Marques, Joao Alexandre Lobo}, month = jan, year = {2023}, keywords = {Deep Learning, Long Short-Term Memory (LSTM), Machine Learning, Neural Networks (NN), Support Vector Machines (SVM)}, }