Stock market prediction with political data Analysis (SP-PDA) model for handling big data

The ability to accurately predict the stock market is a crucial financial topic. The basic assumption is that future stock returns can be somewhat predicted based on publicly available data from the past. Economic variables like interest rates and currency rates, information related to political influences, and company-specific data like statements of income and dividends are some examples of this information. Yet, attempting to forecast stock returns goes against the idea that markets are generally efficient. A prediction model has been created that leverages machine learning, big data analytics, and social media analytics to predict stock market trends regularly. The main objective of this work is to propose a novel approach for stock market prediction for handling big data. This research develops a new SP-PDA (Stock Price Prediction with Political Data Analysis) which includes preprocessing, feature extraction, and prediction. In this work, we employed three types of data (Stock data, News data and Political data). For the stock data, preprocessing was done using Z-score normalization, while tokenization and lemmatization were used for the news and political data. Next, to handle the big data, we used a Map-Reduce architecture. Here, feature extraction from preprocessed stock, news, and political data occurs in the mapper function. The reduction face yields the final extracted feature set. Commodity channel index (CCI), Chaikin Volatility (CV), and Donchian Channel (DC) are examples of technical indicator-based features that are extracted for stock data during the feature extraction phase. Mutual Information (MI), Improved Pointwise mutual information (IPMI), Term Frequency-Inverse Document Frequency (TF-IDF), and correlation features are retrieved for news and political data. Stock market prediction is based on the features extracted. To predict stock prices, we employed an ensemble classification model that incorporates classifiers such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Gated Recurrent Unit (Bi-GRU), Long-short term memory (LSTM), and Deep Maxout. An improved score level fusion is carried out to define the final prediction outcome from the obtained intermediate prediction results. The suggested Dwarf Mongoose Updated War Strategy-based Generalized Normal Distribution (DMUWS-based GND) Optimization Algorithm is used to optimizing the weights of ensemble classifiers. Finally, the performance of the proposed model is evaluated over existing models in terms of error measures like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Squared Log Error (MSLE). The SP-PDA achieved the desired MAE of 0.012, which is low when compared to the other traditional systems for accurate prediction of the stock market.