# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 2 Proposed Methodology  
An extensive literature review is conducted to identify several effective deep learning architectures commonly employed for time-series data modeling and prediction. The subsequent phase involves collecting time-series data from actual-world situations after a diverse array of architectures has been acquired. The third stage of this study consists of pre-processing the collected dataset, which involves normalizing the values based on the various ranges of current stock indices values. This article proposes a hybrid architecture to predict the close price of the Asian Stock Exchange. The architecture incorporates the random and temporal fluctuations of the exchange rate and the vulnerability to external market dynamics. A two-stage feature extraction technique is employed on the dataset to streamline the architecture's creation process and improve its capacity to accurately predict outcomes beyond the data it was trained on. This is necessary because of the abundance of influential factors and the unpredictable nature of exchange rates. One application of Auto-Encode (AE) is to simplify complex elements involved in influencing. Due to the volatility of stock exchange rates, the Self-Organize Map (SOM) is employed to cluster related data from the training set into one category. The Convolutional Neural Network (CNN) addresses traditional neural networks' limitations in effectively extracting features from time-series data. Due to its capacity to accurately predict based on time-series data, it is a highly potent tool. Bi-directional Gated Recurrent Unit is recognized as a very accurate and effective technique for non-linear integration, as it excels in accurately considering the weight of each propagation to predict the close price of Asian stock indices as shown in Figure 2.  
For this study, Yahoo Finance [[https://finance.yahoo.com/](https://finance.yahoo.com/)]([https://finance.yahoo.com/](https://finance.yahoo.com/)) (accessed on 1 April 2024) is the source of the time-series data obtained for each of the six Asian stock markets with corresponding tickers BSE (BSESN), Hongkong (HSI), Taiwan Index (TWII), Japan Index (JPXGY), Indonesia Index (JKSE), and Korea Index (KS11). The reason for choosing Yahoo Finance over other sources is its availability of an open-source library named \"YFinance\". Compared to many paid services, YFinance is free, making it an appealing option for researchers with limited resources. Further, YFinance seamlessly integrates with Python version 3.6 a popular language among data scientists and researchers. This allows users to  
Figure 2: Proposed system architecture. easily combine it with other Python libraries, such as Pandas, NumPy, and Matplotlib, for data analysis and visualization. The dataset contains six features: open price, close price, high price, low price, adjusted close price, and volume. During this investigation's third and final part, the dataset obtained is modified to correspond to the various percentage ranges of stock market index values. Pre-processing is the term that is most generally used to describe this method. In the following step, the dataset passes through the feature normalization and scaling, followed by the processed data being separated into two unique sets: training and testing. Before applying the models to time-series data that contain several variables, the subsequent stage is to construct and refine the deep learning structures that have been chosen. The training dataset is now fed into the series of deep learning models named the convolutional recurrent neural network (ConvRNN), Convolutional LSTM model, Convolutional GRU model, Convolutional Bi-directional RNN model, Convolutional Bi-directional LSTM model, and Convolutional Bi-directional GRU model. After completing the training phase of all six deep learning models, followed by a performance evaluation, the trained models are tested on the stock exchange's test data to predict the exchange's index value.