Natural Language Processing Stock Prediction Model Inclusion Innovation

Abstract

In the burgeoning era of technology, Artificial Technology plays a pivotal role across various sectors, including the financial market for a responsive institution. With the implementation of AI tools, the financial market is expected to function more efficiently while simultaneously reducing costs and time. The financial industry, grappling with biases in stock analysis and limited stock prediction tools, seeks an integrated solution merging technical analysis with current information through advancements like Natural Language Processing (NLP) to enhance the accuracy and efficiency of stock trading, considering investors' preferences and time constraints. In the current manual processes, investors often spend substantial time reading articles and processing information before making decisions. This approach is inefficient, consuming excessive time and energy, thereby reducing the precious time that should be saved for personal relationships. Moreover, suboptimal decision-making could be made due to frequently gathered of inaccurate information. This research aims to discover the impact of Natural Language Processing integration with the stock prediction model on the financial market and evaluates the acceptance of the public towards the employment of NLP tools in their investment process for inclusive innovation. The evaluation will examine 4 different perspectives which are the factors that drive them to invest in the stock market, assessing the model's effectiveness, and the user experience respectively. This study utilized a mixed-method approach, which consists of quantitative and qualitative surveys. The respondents evidence the results and are being analyzed using SmartPLS, a statistical tool. Most respondents indicated a willingness to utilise NLP provided it effectively helps them achieve their financial goals and fosters positive experiences. With the implementation of NLP, respondents anticipated that NLP would significantly reduce the time for investment research and analysis. Consequently, investors could achieve higher profits while dedicating more time to their families. Besides, by identifying the trends through the textual data, NLP is expected to enhance the accuracy of stock prediction results. Thus, the potential opportunities can be uncovered with reduced downside risks.