Daily LGARCH model estimation using high frequency data

<abstract> <p>In this paper, we introduce the intraday high frequency data to estimate the daily linear generalized autoregressive conditional heteroscedasticity (LGARCH) model. Based on the volatility proxies constructed from the intraday high frequency data, the quasi maximum likelihood estimation (QMLE) of the daily LGARCH model and its asymptotic distribution are studied under some regular assumptions. One criterion is also given to choose the optimal volatility proxy according to the asymptotic results. Simulation studies show that the QMLE of the parameters performs well. It is also found that introducing the intraday high frequency data can significantly improve the estimation precision. The proposed method is applied to analyze the SSE 50 Index, which consists of the 50 largest and most liquid A-share stocks listed on Shanghai Stock Exchange. Empirical results show the method is of potential application value.</p> </abstract>

The usage of logistic regression and artificial neural networks for evaluation and predicting property-liability insurers' solvency in Egypt

<abstract> <p>Unlike prior solvency prediction studies conducted in Egypt, this study aims to set up a real picture of companies' financial performance in the Egyptian insurance market. Therefore, 11 financial ratios commonly used by NAIC, AM BEST Company, and S &amp; P Global Ratings were calculated for all property-liability insurance companies in Egypt from 2010 to 2020. They have been used to measure those companies' financial performance efficiency levels by comparing these ratios with the international standard limits. The financial analysis results for those companies revealed that property-liability insurers in Egypt do not have the same level of financial performance efficiency where those companies are classified into three groups: excellent, good, and poor. Furthermore, this paper investigates using the stepwise logistic regression model to determine the most factors among these selected financial ratios that influence those companies' financial performance. The results suggest that only three ratios were statistically significant predictors: "Risk retention rate", "Insurance account receivable to total assets", and "Net profit after tax to total assets". Finally, this paper presents the multi-layers artificial neural network with a backpropagation algorithm as a new solvency prediction model with perfect classifying accuracy of 100%. The trained ANN could predict the next fiscal year with a prediction accuracy of 91.67%, and this percent is a good and favorable result comparing to other solvency prediction models used in Egypt.</p> </abstract>

Dynamic tail dependence on China's carbon market and EU carbon market

<abstract> <p>This study explores the dynamic relationship between the European carbon emission price (EUA) and the Shenzhen carbon emission price (SZA) in the time and frequency domain. Since they represent major carbon emission rights prices in the markets, they show a close correlation and tail correlation between them. Given the current global implementation to reduce carbon economy and China's implementation of a dual-carbon policy, it is of great value to explore the dynamic relationship between the two major carbon markets. Firstly, this paper uses a wavelet method to decompose the returned sequence into different frequency components to certify the dependent construction under different time scales. Secondly, this paper uses a wide range of static and time-varying link functions to describe the tail-dependent. The empirical results show that under different time scales, the dependence construction between EUA and SZA has significant time variation. The results of this study have important policy implications for understanding the transmission of carbon prices between different markets, as well as for investors and policy makers.</p> </abstract>

Bankruptcy prediction using machine learning and an application to the case of the COVID-19 recession

<abstract> <p>Bankruptcy prediction is an important problem in finance, since successful predictions would allow stakeholders to take early actions to limit their economic losses. In recent years many studies have explored the application of machine learning models to bankruptcy prediction with financial ratios as predictors. This study extends this research by applying machine learning techniques to a quarterly data set covering financial ratios for a large sample of public U.S. firms from 1970–2019. We find that tree-based ensemble methods, especially XGBoost, can achieve a high degree of accuracy in out-of-sample bankruptcy prediction. We next apply our best model, using XGBoost, to the problem of predicting the overall bankruptcy rate in USA in the second half of 2020, after the COVID-19 pandemic had necessitated a lockdown, leading to a deep recession. Our model supports the prediction, made by leading economists, that the rate of bankruptcies will rise substantially in 2020, but it also suggests that this elevated level will not be much higher than 2010.</p> </abstract>

Is there any heterogeneous impact of mandatory disclosure on corporate social responsibility dimensions? Evidence from a quasi-natural experiment in China

<abstract> <p>The corporate social responsibility (CSR) report is an important carrier of non-financial information disclosure of enterprises and an important bridge of communication between enterprises and interested parties. Compulsory disclosure has promoted the improvement of CSR levels to some extent. While, for interested parties, their attention to various dimensions of CSR has significant differences, which leads to the heterogeneous impact of mandatory disclosure policy on its different dimensions. Through regression discontinuity design model (RDD), as well as using quasi-natural experiments of Chinese mandatory disclosure policies issued in 2008, we are going to get the following conclusions by analyzing the heterogeneous impact of mandatory disclosure on CSR with the environment (CER), social (SOC) and economic (ECO) three-dimension on the basis of verifying that mandatory disclosure policy has a positive impact on CSR. (1) The effects of mandatory disclosure on the three dimensions of CSR are heterogeneous, that is, the significant effects and directions are significantly different in the three dimensions. (2) The heterogeneity of mandatory disclosure on CSR is reflected in the changing trend, and there is no significant difference at the turning point of the trend. (3) The heterogeneity of the impact mechanism of mandatory disclosure on CSR is reflected in the different mediating variables of policy on different dimensions impact, that is, the mediating variables of CER and ECO are the environmental disclosure information and return on assets. (4) The impact of mandatory disclosure on different dimensions of CSR is heterogeneous when the nature of industries and property rights are different.</p> </abstract>

A review of data mining methods in financial markets

<abstract><p>Financial activities are closely related to human social life. Data mining plays an important role in the analysis and prediction of financial markets, especially in the context of the current era of big data. However, it is not simple to use data mining methods in the process of analyzing financial data, due to the differences in the background of researchers in different disciplines. This review summarizes several commonly used data mining methods in financial data analysis. The purpose is to make it easier for researchers in the financial field to use data mining methods and to expand the application scenarios of it used by researchers in the computer field. This review introduces the principles and steps of decision trees, support vector machines, Bayesian, K-nearest neighbors, k-means, Expectation-maximization algorithm, and ensemble learning, and points out their advantages, disadvantages and applicable scenarios. After introducing the algorithms, it summarizes the use of the algorithm in the process of financial data analysis, hoping that readers can get specific examples of using the algorithm. In this review, the difficulties and countermeasures of using data mining methods are summarized, and the development trend of using data mining methods to analyze financial data is predicted.</p></abstract>