This study develops, implements, and evaluates a multi-label text classification algorithm that extracts textual information from the annual reports of all publicly listed USA companies. Specifically, the proposed system can automatically identify 25 types of frequently mentioned risk factors in...

This paper proposes a simple analysis for examining an agent's optimal decisions in a principal-agency problem. Unlike the standard approach, the target firm's expected return and risk are modeled as a parametric curve in terms of a critical business decision. A general condition is derived for...

This study develops, implements, and evaluates a multi-label text classification algorithm called the multi-label categorical K - nearest neighbor (ML-CKNN). The proposed algorithm is designed to automatically identify 25 types of risk factors with specific meanings reported in Section 1A of SEC...

In today's big data environment, missing values continues to be a problem that harms the data quality. The bias caused by missing values raises the highest concern as it cannot be eliminated simply by increasing the sample size. Although the statistics literature has developed approaches to...