MAOA-DRN: Mayfly Adam Optimization Based Deep Residual Network - A Framework for Stock Market Prediction

Stock market plays a vital role in country's economy. The estimation of stock market trends and price is important for the investors. Stock forecasting has piqued the interest of investors worldwide. Investors are looking for a forecasting model that is accurate and reliable enough to account for volatile and nonlinear market behavior. The problem of stock price prediction becomes a complicated one due to multiple inputs like technical indicators, financial factors, trends of international markets, and social media data. This research seeks to collect and correlate multiple inputs to analyse their impact on the stock market and improve stock price prediction accuracy. Researchers are attempting to refine effective indicators to aid in stock market forecasting by taking the advantage of ever-growing world wide web. Effective indicators like stock-related events and public sentiment towards the stock plays a significant role in stock volatility, hence can be used in improving prediction accuracy. The paper provides an efficient framework for optimizing the error in short-term stock market prediction with diverse data sources, based on a novel Mayfly Adam Optimization Algorithm (MAOA) and Deep Residual Network (DRN). This novel approach uses Deep Q Network (DQN) with dice coefficient similarity for feature fusion and DRN for prediction. We have considered historical stock market prices of Apple Inc. (AAPL), commodity time-series dataset, news articles and reviews on social media to evaluate the model's effectiveness. The proposed technique accuracy is found to be 96%, with average accuracy of 98.27%. The existing techniques like, Deep Neural Network (DNN), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Random Forest (RF) models provided accuracy of 82.4%, 85.5%, 88.8%, and 92.39%, respectively. This shows that the proposed model is 13.6% better than DNN, 10.5% better than ANN, 7% better than LSTM and 3.61% better than RF in terms of accuracy.