RETRACTED: Incorporating soft information from financial news media for management decisions in dynamic business environments

Abstract

Because the nature of numerical information is intuitive and comprehensible, it has been widely used to form a basis for decision making, yet numerical information based on historical principle does not reflect messages about future corporate performance. To confront this issue, one may consider textual information that can transmit future corporate potential without any hysteresis. The key point is how to digest an extensive amount of textual information and identify those topics most likely to precede changes in operation status. Topic modeling can categorize these textual disclosures based on their underlying content and help examine which topics have a strong relevance to corporate operations. To extract decisive words from textual information, we set up a statistical-based approach with objectivity as opposed to frequently used heuristics (i.e., dictionary-based approaches with human involvement). Joint utilization of topic modelling and a statistical-based approach can compress an excessive amount of textual information into a manageable size in a timely manner and further realize a discrepancy among various topics in terms of relevance and influence on corporate operations. Our results benefit managers and current and future investors in how to structure regulatory filings and how word choices are decisive to them in their decision judgments.

Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model

“…For an unknown domain, users tend to collect as much information as possible to conjecture its real situation. However, too much information will impede/bias their decision making process and increase their cognitive burden (Li et al 2021;Hsu et al 2022b;Chang et al 2022;Kou et al 2022). To overcome this, filtering out redundant and irrelevant factors turns out to be an important pre-process.…”