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

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.

A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data

“…Developing a holistic framework for cryptocurrency price prediction remains one possible future work. In particular, a unified framework that uses variables associated with prices, including on-chain data and social media data, should not only be developed, but a comprehensive aggregation model should also be created to model the price dynamics of the cryptocurrency market [6], [82]. In addition, a realtime price prediction model that uses various input data to make more frequent predictions (like hourly or per-minute) is worth being developed.…”

Formalization of a new stock trend prediction methodology based on the sector price book value for the Colombian market

“…Avoiding investment risks and making accurate predictions about the stock market is essential for investors seeking more reliable, accurate, and real-time information to improve their profit margins. Researchers in this area of knowledge understand the preponderant role of these markets in the economy ( Tuarob et al., 2021 ).…”

“…The risks and uncertainties are inherent in financial totals to stabilize economic and economic policies aimed at providing economic and social benefits, in addition to the monetary gains associated with a successful decision-making process in this market. It is critical to developing inferences that lessen society's uncertainty ( Tuarob et al., 2021 ).…”

“…In this context, to find and clarify tangibly exploitable patterns by investors in estimating stock market behavior, Technical Analysis makes predictions of future stock prices based on quantitative analysis of time series, such as trading traffic and opening and closing stock prices ( Tuarob et al., 2021 ).…”

“…The future and operations will be influenced by the unstable, non-seasonal, and fluctuating nature of the markets themselves and the diversity and non-uniformity of the internal elements of each country in which these markets operate. It is purely economic and financial, culturally, socially, politically, and regulatory ( Tuarob et al., 2021 ).…”