TrendFocus: Visualization of trends in financial news with indicator sets

Abstract Motivated by the recent availability of extensive electronic news databases and advent of new empirical methods, there has been renewed interest in investigating the impact of financial news on market outcomes for individual stocks. We develop the information processing hypothesis of return volatility to investigate the relation between firm-specific news and volatility. We propose a novel dynamic econometric specification and test it using time series regressions employing a machine learning model selection procedure. Our empirical results are based on a comprehensive dataset comprised of more than 3 million news items for a sample of 28 large U.S. companies. Our proposed econometric specification for firm-specific return volatility is a simple mixture model with two components: public information and private processing of public information. The public information processing component is defined by the contemporaneous relation with public information and volatility, while the private processing of public information component is specified as a general autoregressive process corresponding to the sequential price discovery mechanism of investors as additional information, previously not publicly available, is generated and incorporated into prices. Our results show that changes in return volatility are related to public information arrival and that including indicators of public information arrival explains on average 26% (9–65%) of changes in firm-specific return volatility.

Download Full-text

Dependency Parsing of Financial News to Improve Sentiment Analysis for Predicting Market Prices

2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) ◽

10.1109/taai51410.2020.00009 ◽

2020 ◽

Author(s):

Jenq-Haur Wang ◽

Hung-Hsuan Wang ◽

Long Wang

Keyword(s):

Sentiment Analysis ◽

Dependency Parsing ◽

Market Prices ◽

Financial News

Download Full-text

Research on Knowledge Discovery and Stock Forecasting of Financial News Based on Domain Ontology

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622019500160 ◽

2019 ◽

Vol 18 (03) ◽

pp. 953-979 ◽

Cited By ~ 1

Author(s):

Lingling Zhang ◽

Minghui Zhao ◽

Zili Feng

Keyword(s):

Knowledge Acquisition ◽

Knowledge Discovery ◽

Tacit Knowledge ◽

Explicit Knowledge ◽

Academic Research ◽

Online News ◽

Domain Ontology ◽

General Mechanism ◽

Financial News ◽

Knowledge Reasoning

In the era of big data, how to obtain useful knowledge from online news and utilize it as an important basis to make investment decision has become the hotspot of industrial and academic research. At present, there have been research and practice on explicit knowledge acquisition from news, but tacit knowledge acquisition is still under exploration. Based on the general mechanism of domain knowledge, knowledge reasoning, and knowledge discovery, this paper constructs a framework for discovering tacit knowledge from news and applying the knowledge to stock forecasting. The concrete work is as follows: First, according to the characteristics of financial field and the conceptual cube, the conceptual structure of industry–company–product is constructed, and the framework of domain ontology is put forward. Second, with the construction of financial field ontology, the financial news knowledge management framework is proposed. Besides, with the application of attributes in ontology and domain rules extracted from news text, the knowledge reasoning mechanism of financial news is constructed to achieve financial news knowledge discovery. Finally, news knowledge that reflects important information about stock changes is integrated into the traditional stock price forecasting model and the newly proposed model performs well in the empirical analysis of polyester industry.

Download Full-text