## 4 Data Processing and Features
### Deep Learning
#### 5.2.1 Artificial Neural Network  
Deep learning techniques use artificial neural networks (ANNs), a type of machine learning. Artificial neural networks (ANNs) have a node layer, input layer, hidden layer, and output layer. Each artificial neuron has a weight and threshold and connects to others. Nodes deliver data to the next tier if their output exceeds the threshold. No data pass to the next network layer. Figure 7 shows the basic structure of ANN. To imitate the human brain, artificial neural networks (ANNs) use layers of input, hidden, and output neurons (nodes) that are interconnected [25; 27; 33; 57; 61; 65; 74; 76].  
Figure 7: The basic structure of an artificial neural network consists input layer, a hidden layer, and an output layer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name of Method** & \multicolumn{2}{c}{**Advantages**} & **Disadvantages** & **Article** \\ \hline \multirow{4}{*}{**DT**} & \multirow{4}{*}{+} & Comparatively, they are & \multirow{4}{*}{possible over-fitting} \\  & & fast and efficient. & & \\  & & We do not care about & & \\  & & the decision tree’s feature & & \\  & & scalability. & & \\  & & + & It shows us the relevance & & \\  & & of attributes. & & \\ \hline \multirow{4}{*}{**RF**} & \multirow{4}{*}{+} & Due to its random & \multirow{4}{*}{Creates more trees,} \\  & & modeling of the feature & \multirow{4}{*}{-} & \\  & & space and its architecture & & \\  & & including many decision & & therefore more \\  & & trees, this technique is & & processing time and \\  & & robust for use in & & storage space are needed. \\  & & forecasting and & \multirow{4}{*}{-} & \\  & & classification tasks. & & \\  & & Can be used with either & & \\  & & continuous or & & \\  & & discrete data. & & \\ \hline \multirow{4}{*}{**kNN**} & \multirow{4}{*}{+} & Resistant to the presence & \multirow{4}{*}{-} & The number of the closest \\  & & of noisy training data. & & neighbors is the first \\ \cline{1-1}  & & Extremely effective when & & thing that has to be \\ \cline{1-1}  & & working with huge & & \\ \cline{1-1}  & & training datasets. & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: _Cont._Using a combination of financial statistics, technical indicators, and public opinion, 26 efficient factors are chosen to create a factor database. Second, construct a stock selection model using a Gated Recurrent Unit (GRU) neural network trained with the Cuckoo Search (CS) optimization method. The suggested multi-factor deep learning stock selection model based on intelligent optimization is then put to the test in the real world, culminating in the development of a quantitative investing strategy. The back-test findings demonstrate that the model-based quantitative trading strategy generated a Sharpe ratio of 127.08%, an annualized rate of return of 40.66%, an excess return of 13.13%, and a maximum drawdown rate of 17.38% [20].  
**Advantages of using ANN**[85]  
* Outstanding capability in dealing with complicated nonlinear patterns.
* Extremely precise group-data modeling. The model is adaptable to both linear and non-linear dynamics.
* Ability to accommodate missing and noisy data without breaking the model.  
**Disadvantages of using ANN**[85]  
* Overfitting.
* ANNs just provide projected target values for some unknown data without any variance information to evaluate the accuracy of the prediction. These models are sensitive to parameter selection.  
Deep Learning (DL) has quickly become a formidable technique for modeling and predicting volatile financial markets globally. Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) are among the many DL approaches used in diverse applications.