Textual-based factors have been widely regarded as a promising feature that can be applied to financial issues. This study focuses on extracting both basic and semantic textual features to supplement the traditionally used financial indicators. The main is to improve Chinese listed companies’ financial distress prediction (FDP). A unique paradigm is proposed in this study that combines financial and multi-type textual predictive factors, feature selection methods, classifiers, and time spans to achieve the optimal FDP. The frequency counts, TF-IDF, TextRank, and word embedding approaches are employed to extract frequency count-based, keyword-based, sentiment, and readability indicators. The experimental results prove that financial domain sentiment lexicons, word embedding-based readability analysis approaches, and the basic textual features of Management Discussion and Analysis can be important elements of FDP. Moreover, the finding highlights the fact that incorporating financial and textual features can achieve optimal performance 4 or 5 years before the expected baseline year; applying the RF-GBDT combined model can also outperform other classifiers. This study makes an innovative contribution, since it expands the multiple text analysis method in the financial text mining field and provides new findings on how to provide early warning signs related to financial risk. The approaches developed in this research can serve as a template that can be used to resolve other financial issues.

Purpose: Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.Design/methodology/approach: This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.Findings: Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.Originality/value: Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

Establishing an early warning model for corporate financial crises is important for managing risks and ensuring the continued stability of the capital market. A financial crisis early warning indicator system for listed companies was constructed, which includes financial indicators, management indicators and annual report text tone features. Using techniques such as web crawlers and text sentiment analysis, we collected data related to 820 listed companies in mainland China from 2017 to 2021. Six models were then constructed and their results were compared. The results of the comparative analysis showed that: there is room for AutoML to be applied and explored in this area; the model performance and inference speed of integrated learning CatBoost are substantially improved compared with traditional methods; feature importance rankings help to understand the formation of corporate financial distress. Thus, textual information such as corporate annual reports can help predict financial crises.