## 4 Data Processing and Features
Machine learning methods are increasingly being utilized in quantitative finance to create  
Figure 5: Relationship of AI, ML, DL, RL, and DRL. Machine learning, deep learning, and reinforcement learning are subsets of artificial intelligence. Figure 6: Classification, Regression, and Clustering task performed by the machine learning algorithm. more accurate forecasts and enhance the performance of financial models. The following is a selection of applications of machine learning that may be found in quantitative finance:  
**Algorithmic trading:** Machine learning algorithms are used in algorithmic trading to create trading strategies that can automatically assess massive volumes of financial data and execute trades based on the algorithm's predictions. **Risk management:** Machine learning may uncover risky trends in massive financial data sets. This helps financial firms manage risk. **Portfolio optimization:** Using factors such as volatility, risk, and return, machine learning can be used to find the best investments for a portfolio. **Forecasting:** By using machine learning, one may analyze past financial data and forecast future market patterns, exchange rates, and other parameters. **Sentiment analysis:** Machine learning may also be used to look at social media and news to find out what people think about a company, product, or industry and how they feel about it. Traditional financial models may be coupled with machine learning models to enhance forecasts and outcomes. However, it is crucial to remember that not all machine learning models are suited for all issues. The model chosen relies on the problem and the available data, so machine learning and finance professionals should work together to obtain the best results. **Prediction task using AI in quantitative finance:** Using machine learning, Shares Price Prediction reveals the future worth of business stock and other financial assets traded on an exchange. The purpose of stock price prediction is to generate substantial profits. Predicting the performance of the stock market is a difficult endeavor. Other aspects, such as physical and psychological characteristics and rational and illogical conduct, also influence the forecast. All of these variables contribute to the dynamic and volatile nature of share pricing. This makes it very difficult to accurately estimate stock values. **Classification task using AI in quantitative finance:** This recommendation model is supposed to categorize a certain stock as a \"STRONG BUY\" or a \"STRONG SELL,\" as well as \"BUY,\" \"SELL,\" and \"HOLD.\" The imbalanced natures of the different class labels provide the greatest difficulty in this classification issue. The majority of the time, the user will be required to HOLD the stocks, and only a very small percentage of the time will a STRONG BUY or STRONG SELL Signal be present. **Portfolio construction using AI in quantitative finance:** An investor's portfolio consists of diverse assets including stocks, bonds, and cash that are allocated based on many considerations, including investment risk, projected return, and liquidity needs. The goal is to obtain an estimated return with as little risk as possible. The portfolio was chosen based on how well the assets would do in the future, as evaluated by machine learning methods. Table 7 shows the list of the standard machine learning prediction models that were used. Table 8 shows standard deep-learning prediction models that were used. Table 9 shows standard reinforcement learning models that were used and Table 10 shows hybrid prediction models that were used. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Article**} & \multicolumn{1}{c}{**Prediction Model Used**} \\ \hline
[28] &
\begin{tabular}{l} Stochastic gradient descent linear regression, Lasso regression, \\ and XGBoost tree regression \\ \end{tabular} \\ \hline
[51] & SVM, LR, Perceptron \\ \hline
[49] & NB, SMO, IBK, LWL, PART, J48, RF, and DT \\ \hline
[50] & SVM \\ \hline
[66] & RF \\ \hline \hline \end{tabular}
\end{table}