DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

• Main Authors: Suppawong Tuarob, Poom Wettayakorn, Ponpat Phetchai, Siripong Traivijitkhun, Sunghoon Lim, Thanapon Noraset, Tipajin Thaipisutikul
• Format: Article
• Language: English
• Published: SpringerOpen 2021-07-01
• Series: Financial Innovation
• Subjects: Investment support system; Stock data visualization; Time series analysis; Ensemble machine learning; Text mining
• Online Access: https://doi.org/10.1186/s40854-021-00269-7

Abstract The explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.