Bank-Affiliated Venture Capital and the Financial Distress Risk of Portfolio Firms

2012 ◽  
Author(s):  
Annalisa Croce ◽  
Diego D'Adda ◽  
Elisa Ughetto
Keyword(s):  
Venture Capital ◽  
Financial Distress ◽  
Distress Risk

Operational Risk, Financial Distress Risk and Equity Returns

2011 ◽  
Author(s):  
Ramya Rajajagadeesan Aroul ◽  
Mishuk Chowdhury ◽  
Peggy E. Swanson
Keyword(s):  
Financial Distress ◽  
Operational Risk ◽  
Equity Returns ◽  
Distress Risk

Financial Distress Risk and Stock Price Crashes

2019 ◽  
Author(s):  
Christoforos Andreou ◽  
Panayiotis C. Andreou ◽  
Neophytos Lambertides
Keyword(s):  
Financial Distress ◽  
Stock Price ◽  
Distress Risk

Are Firms with Negative Book Equity in Financial Distress?

2015 ◽  
Vol 18 (03) ◽  
pp. 1550016 ◽  
Author(s):  
Tze Chuan Chewie ANG
Keyword(s):  
Failure Rate ◽  
Financial Distress ◽  
Operating Performance ◽  
Large Magnitude ◽  
Small Magnitude ◽  
First Report ◽  
Distress Risk ◽  
Financial Characteristics ◽  
And Control ◽  
Negative Earnings

This study examines whether negative book equity (BE) firms are in financial distress by analyzing their operating performance, financial characteristics, distress risk, and survivability when they first report negative BE. Firms with small magnitude of negative BE (SNBE firms) suffer from persistent negative earnings and financial distress, while firms with large magnitude of negative BE (LNBE firms) experience a temporary non-distress related earnings shock. LNBE firms report consecutive years of negative BE, but have lower distress risk and failure rate than both SNBE and control firms. However, all negative BE stocks have abysmal returns subsequent to their first report of negative BE.

scholarly journals Does corporate social responsibility reduce financial distress risk?

2020 ◽  
Vol 91 ◽  
pp. 835-851 ◽  
Author(s):  
Sabri Boubaker ◽  
Alexis Cellier ◽  
Riadh Manita ◽  
Asif Saeed
Keyword(s):  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Financial Distress ◽  
Distress Risk ◽  
Corporate Social

scholarly journals Distress risk and leverage puzzles: Evidence from Taiwan

2016 ◽  
Vol 6 (2) ◽  
pp. 72-78
Author(s):  
Kung-Cheng Ho ◽  
Shih-Cheng Lee ◽  
Po-Hsiang Huang ◽  
Ting-Yu Hsu
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Cross Section ◽  
Financial Distress ◽  
Distress Risk ◽  
The Cross

Financial distress has been invoked in the asset pricing literature to explain the anomalous patterns in the cross-section of stock returns. The risk of financial distress can be measured using indexes. George and Hwang (2010) suggest that leverage can explain the distress risk puzzle and that firms with high costs choose low leverage to reduce distress intensities and earn high returns. This study investigates whether this relationship exists in the Taiwan market. When examined separately, distress intensity is found to be negatively related to stock returns, but leverage is found to not be significantly related to stock returns. The results are the same when distress intensity and leverage are examined simultaneously. After assessing the robustness by using O-scores, distress risk puzzle is found to exist in the Taiwan market, but the leverage puzzle is not.

scholarly journals OPINI AUDIT GOING CONCERN: SUDUT PANDANG LIKUIDITAS, LEVERAGE, FINANCIAL DISTRESS RISK, TAX RISK

2021 ◽  
Vol 17 (1) ◽  
pp. 122
Author(s):  
Yogy Wira Utama ◽  
Ahmad Syakur ◽  
Amrie Firmansyah
Keyword(s):  
Financial Distress ◽  
Going Concern ◽  
Distress Risk

The extraction of early warning features for the predicting financial distress based on XGboost model and shap framework

2021 ◽  
pp. 2141004
Author(s):  
He Yang ◽  
Emma Li ◽  
Yi Fang Cai ◽  
Jiapei Li ◽  
George X. Yuan
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Financial Distress ◽  
Prediction Accuracy ◽  
Financial Risk ◽  
Learning Algorithm ◽  
Listed Companies ◽  
Gradient Boosting ◽  
Distress Risk ◽  
Extreme Gradient Boosting

The purpose of this paper is to establish a framework for the extraction of early warning risk features for the predicting financial distress based on XGBoost model and SHAP. It is well known that the way to construct early warning risk features to predict financial distress of companies is very important, and by comparing with the traditional statistical methods, though the data-driven machine learning for the financial early warning, modelling has a better performance in terms of prediction accuracy, but it also brings the difficulty such as the one the corresponding model may be not explained well. Recently, eXtreme Gradient Boosting (XGBoost), an ensemble learning algorithm based on extreme gradient boosting, has become a hot topic in the area of machine learning research field due to its strong nonlinear information recognition ability and high prediction accuracy in the practice. In this study, the XGBoost algorithm is used to extract early warning features for the predicting financial distress for listed companies, with 76 financial risk features from seven categories of aspects, and 14 non-financial risk features from four categories of aspects, which are collected to establish an early warning system for the predication of financial distress. With applications, we conduct the empirical testing respect to AUC, KS and Kappa, the numerical results show that by comparing with the Logistic model, our method based on XGBoost model established in this paper has much better ability to predict the financial distress risk of listed companies. Moreover, under the framework of SHAP (SHAPley Additive exPlanations), we are able to give a reasonable explanation for important risk features and influencing ways affecting the financial distress visibly. The results given by this paper show that the XGBoost approach to model early warning features for financial distress does not only preform a better prediction accuracy, but also is explainable, which is significant for the identification of early warning to the financial distress risk for listed companies in the practice.

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