## 3 Overview  
In this section of the article, we are going to talk about the document that is going to be the primary focus of our study in this specific piece of research. All of the material, including this one, has been searched for and compiled from a wide variety of sources, including Google scholar. A literature search was conducted utilizing academic and scientific databases to discover articles from well-known research journals and publications (e.g., Scopus, Google Scholar, Springer, IEEE explore, Science Direct, and Web of Science). The keywords that were chosen for these literary analyses were: \"Artificial Intelligence in quantitative finance\", \"Machine learning in quantitative finance\", \"Deep Learning in quantitative finance\", \"Reinforcement Learning in quantitative finance\", \"Q-Learning in stock trading strategy\", \"Deep Q-Learning in stock trading strategy\", \"reinforcement learning in stock trading\", \"Artificial Intelligence in stock market\", \"Machine learning in stock market\", \"Using AI to make a prediction of the stock market\", \"AI-based stock forecasting\", \"Survey paper on machine learning in stock trading\", \"Deep reinforcement learning in trading\", and \"Neural Network for financial time series forecasting\". Research papers that were less related have not been taken for the review/survey process. Table 1 shows the year-wise article surveyed in this review paper and also mentions the article references. Figure 3 describes the overall process of the stock market using artificial intelligence. Including Different types of Stock Market Data, Machine Learning Tasks, Prediction Models, Model Performance Evaluation Metrics, and portfolio performance evaluation metrics. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Count** & **Article** \\ \hline
2022 & 17 & [2; 9; 10; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] \\ \hline
2021 & 14 & [1; 4; 7; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42] \\ \hline
2020 & 16 & [43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61] \\ \hline
2019 & 9 & [62; 63; 64; 65; 66; 67; 68; 69; 70; 71] \\ \hline
2018 & 1 & [72] \\ \hline
2017 & 2 & [8; 73] \\ \hline Others & 5+ & [74; 75; 76] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Counts of the research papers published over the years are included in this analysis. We have focused on more recent papers so that we can know about the advancement in this field, and we can analyze the recent development. We analyzed more than 70 papers and found that about 7% of the researchers used only machine learning while about 20% of the researchers used only deep learning. Most of the researchers have used reinforcement learning, around 39%, while the percentage of researchers using the hybrid model, i.e., two or more models is around 25%. This study includes the market index of the US (S&P 500, Dow Jones Industrial Average, NASDAQ Composite, NYSE Composite), the market index of ASIA (NIFTY50, BSE30, CSI Composite, HIS, SSE Composite index (000001.SS), market index of Europe (Euronext 100 (N100), and the Deutsche Boese DAX index (GDAXI)) market index of England, Germany, Africa, and Nigeria. Table 2 shows the important stock indices and their country. Table 3 shows Journal titles and the total number of papers under consideration are shown below. \begin{table}
\begin{tabular}{l c} \hline \hline
**Index** & **Country** \\ \hline S\&P 500 & US \\ \hline Dow Jones Industrial Average & US \\ \hline CSI Composite & China \\ \hline HIS & Hong Kong \\ \hline NIFTY50 & India \\ \hline BSE30 & India \\ \hline FTSE 100 & England \\ \hline DAX & Germany \\ \hline NASDAQ Composite & US \\ \hline NYSE Composite & US \\ \hline Euronext 100 (N100) & Europe \\ \hline Deutsche Boese DAX index (GDAXI) & Europe \\ \hline SSE Composite index (000001.SS) & Asia \\ \hline Nikkei 225 (N225) & Asia, Japan \\ \hline Global X MSCI Nigeria ET (NGE) and & Nigeria \\ \hline FTSE/JSE Africa index (J580.JO) & Africa \\ \hline \hline \end{tabular}
\end{table}