# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 3 Dataset
### Training-Testing  
For training purposes, 80% data points are used out of roughly 770-800 data points for different exchange markets that represent the daily progression of stock market index values from 1 April 2020 to 31 March 2024. Using this information and up to nineteen data points daily, forecasts regarding the stock market index value fluctuations during March 2024 are created. This study utilizes the Python artificial intelligence libraries such as PyTorch, Scikit-Learn, TensorFlow, and Keras to create six deep learning models: ConvRNN, ConvLSTM, ConvGRU, ConvBRNN, ConvBLSTM, and ConvBGRU. The experimental methods are conducted as an ancillary inquiry utilizing a personal computer with an Intel Core i7 CPU, 32 gigabytes of random access memory (RAM), and the Windows 11 operating system. At the same time, the exhaustive parameter settings for every single deep learning model utilized in this investigation are presented in Table 2. For Convolutional RNN, Convolutional LSTM, and Convolutional GRU, we use a single 1-D layer of 64 distinct convolutional filters with a maximum pool size of 2 followed by another 1-D CNN layer of 128 distinct convolutional filters with a pool size of 2 and another 1-D CNN layer of 64 distinct convolutional filters with a pool size of 2. For Convolutional Bi-directional RNN, Convolutional Bi-directional LSTM, and Convolutional Bi-directional GRU, we use a single 1-D layer of 64 distinct convolutional filters with a maximum pool size of 2 followed by another 1-D CNN layer of 128 distinct convolutional filters with a pool size of 2 and another 2-D CNN layer of 64 distinct convolutional filters with a pool size of 2. We use the same stride of 3 for every model to preserve more information in the pattern. We use a minimal learning rate of 1e-5 followed by the RELU activation function to minimize the error rate at every weight updation for each model. Further, every model has processed the training samples hundreds of times and updated their weights based on each sample's error. The training samples, forty in number, are used in the forward pass for Conv. RNN, Conv. LSTM, and Conv. GRU, whereas forty training samples are used in forward and backward passes for Conv. BRNN, Conv. BLSTM, and Conv. BGRU. The choices of parameter values in all the six deep learning models for predicting BSE stock prices [(43)] and in the studies [(44; 41)] on how people moved around during the COVID-19 pandemic and how deep learning models were used to predict Asian stock market indices show how parameters are set up and structured.  
Each of the six models is trained using a subset of the collected financial time-series dataset. The subsequent stage involves comparing the current values of each stock market index with their respective forecasts. It is determined that the Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) are the most effective methods for determining the accuracy of each model's predictions in this  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stock Exchange** & **Country** & **Start Date** & **End Date** & **Points** & **Indicators** \\ \hline BSE & India & 1 April 2020 & 31 March 2024 & 992 & OHLCAV \\ HSI & Hong Kong & 1 April 2020 & 31 March 2024 & 986 & OHLCAV \\ TWII & Taiwan & 1 April 2020 & 31 March 2024 & 974 & OHLCAV \\ JKSE & Indonesia & 1 April 2020 & 31 March 2024 & 970 & OHLCAV \\ JPXGY & Japan & 1 April 2020 & 31 March 2024 & 1003 & OHLCAV \\ KS11 & Korea & 1 April 2020 & 31 March 2024 & 986 & OHLCAV \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data points taken from six Asian stock exchanges.  
investigation [45]. This study evaluates the deep learning models' performance using one-step prediction accuracy instead of multistep predictions. Pragmatic considerations drive the decision to use a one-step prediction technique. Due to the inclination of stock market investments to prioritize short-term goals, it is crucial to have more accurate projections for this specific time frame.