Systemic Risk-Driven Portfolio Selection

How can we construct portfolios that perform well in the face of systemic events? The global financial crisis of 2007–2008 and the coronavirus disease 2019 pandemic have highlighted the importance of accounting for extreme form of risks. In “Systemic Risk-Driven Portfolio Selection,” Capponi and Rubtsov investigate the design of portfolios that trade off tail risk and expected growth of the investment. The authors show how two well-known risk measures, the value-at-risk and the conditional value-at-risk, can be used to construct portfolios that perform well in the face of systemic events. The paper uses U.S. stock data from the S&P500 Financials Index and Canadian stock data from the S&P/TSX Capped Financial Index, and it demonstrates that portfolios accounting for systemic risk attain higher risk-adjusted expected returns, compared with well-known benchmark portfolio criteria, during times of market downturn.

Research on management evaluation of enterprise sales cash flow percentage method based on the application of quadratic linear regression equations

This article introduces an improved sales percentage method to quantitatively calculate the evaluation process of the corporate sales cash flow percentage method in order to obtain more evidence-based financial data and increase the accuracy of the evaluation results. At the same time, the paper uses SPSS to perform regression analysis on related financial indicators and sales revenue and obtains quadratic regression equations and linear regression equations. The thesis predicts other financial index data based on the predicted future sales revenue, uses the revised linear regression equation to obtain the company's future net cash flow and calculates the company value.

Analysis of Enterprise Financial and Economic Impact Based on Background Deep Learning Model under Business Administration

Enterprise finance has become an indispensable financial channel for people to invest in their lives, and business management can provide a better economic environment for the development of enterprise finance. The structure of enterprises is gradually becoming more and more complex, and business administration shoulders considerable responsibilities and obligations in the organization and supervision of today’s social management structure. How can China play its functions under the new situation after the world economic exchanges are more frequent is an important link to promote the stable development of financial markets. In view of the problems of economic activity behavior and certainty of financial index system under the background of existing business administration, this paper puts forward the deep learning model to make risk analysis, income analysis, profit and loss analysis, and so on. The formula of deep learning model is used to calculate the data graph of financial economy, and finally, various data are compared to get the research of several business management methods on the development of enterprise financial economy. Among them, the model of current management mode belongs to two modes: e-commerce and EPR management. They not only have very unique management characteristics but also greatly promote the development of modern management, and their roles also well interpret the characteristics of modern management. The experiment also analyzes the financial data under the four algorithms for uncertainty comparison, profit and loss comparison, discreteness comparison, volatility comparison, and possibility analysis. Finally, after the source of uncertainty, the risk prediction and risk management are carried out by constructing decision trees, and these structural models are used to bring comprehensive analysis to the financial economy of enterprises and to build the impact of good trends and development prospects.

Financial Risk Analysis and Early Warning Research Based on Data Mining and Statistical Analysis Technology

Using the advantage of decision tree algorithm in the screening work, the traditional ID3 algorithm is improved and optimized, and a new and simplified financial index system is constructed. At the same time, combined with the unique value of artificial neural network in early warning model and data analysis, B-P model is used to build a mixed financial early warning model. In the model study, the HFPM model and Z-score model were compared and analyzed by using test samples and training samples, and the superior warning ability of the former was effectively verified.

Digital inclusive finance and rural consumption structure – evidence from Peking University digital inclusive financial index and China household finance survey

PurposeThis paper examines the impact and mechanism of China's digital inclusive finance on rural consumption upgrade. First, the impact of the development of digital inclusive finance on the upgrading of rural household consumption structure is to be theoretically analyzed and empirically tested. Secondly, in terms of heterogeneity analysis, it pays attention to the age heterogeneity of users that digital inclusive finance influencing rural residents' developmental consumption upgrade, which is related to the issue of intergenerational “digital gap”. Thirdly, the mechanism of digital inclusive finance in promoting rural consumption upgrade is to be investigated. Finally, how to promote the role of digital inclusive finance in upgrading the structure of rural consumption to a developmental demand level will be showed.Design/methodology/approachFrom the perspective of the micro-household, this study is conducted by using the instrumental variable (IV) method, with 2SLS model and IV-Tobit model, based on the matched city-level data of Digital Inclusive Financial Index (DIFI) with the Chinese Household Financial Survey (CHFS). “The relief degree of land surface” is an ideal instrumental variable of digital inclusive finance, for including regional altitude difference and terrain factors of regional area, has theoretical influence on the development of digital inclusive finance, and is not affected by other economic variables.FindingsThe conclusions show that the digital inclusive finance plays a significant role in promoting the rural households' developmental consumption, but has no significant effect on the rural households' survival-type consumption and hedonistic consumption. Furthermore, this paper examines the impact and mechanism of China's digital inclusive finance on rural consumption upgrade. First, the impact of the development of digital inclusive finance on the upgrading of rural household consumption structure is to be theoretically analyzed and empirically tested. Secondly, the mechanism of digital inclusive finance in promoting rural consumption upgrade is to be investigated. Finally, how to promote the role of digital inclusive finance in upgrading the structure of rural consumption to a developmental demand level will be showed. Thirdly, these conclusions reveal that the digital inclusive finance does affect the consumption of rural residents through three channels such as: the income and wealth effects, constraints of liquidity and methods of payment.Originality/valueThe current research on the relationship between digital inclusive finance and rural consumption only stays at the level of total rural consumption and has not stressed the structural problems of rural consumption. Can digital inclusive finance promote the upgrade of rural consumption structure? To what level can digital inclusive finance promote the upgrading of rural consumption structure? Therefore, it is of great theoretical value to study the upgrading of rural consumption structure from the micro level. Can the current digital inclusive finance benefit the elderly and help break the vulnerability of the elderly to enjoy finance? In this regard, evidence of heterogeneity remains to be provided.

Refined Composite Multivariate Multiscale Fractional Fuzzy Entropy: Measuring the Dynamical Complexity of Multichannel Financial Data

Refined composite multivariate multiscale fractional fuzzy entropy (RCmvMFFE), which aims to sensitively discriminate different short noisy multichannel financial data, is proposed as a new measure to quantify the complexity dynamics of multichannel time series in this work. To better comprehend the RCmvMFFE measure, the dynamical complexity analyses of multichannel synthetic dataset are comparatively studied with multivariate multiscale fuzzy entropy (mvMFE), refined composite multivariate multiscale fuzzy entropy (RCmvMFE), and refined composite multivariate multiscale fractional fuzzy entropy (RCmvMFFE). Then, these measures are firstly employed to explore actual multichannel financial index series to the best of our knowledge. The empirical analyses report that RCmvMFFE measure is able to deeply and sensitively dig up the market information hidden in the multichannel financial data and can better discriminate markets in different area compared to the traditional measures to some extent.

Dividend Payout Policy and Global Financial Crisis: A Study on Malaysian Non-Financial Listed Companies

Manuscript type: Research paper Research aims: This paper examines the impact of the global financial crisis on Malaysia non-financial index firms’ dividend policies. Design/Methodology/Approach: This paper used panel data of 495 firm-year observations of Malaysian non-financial index firms from 2006 to 2016. Research findings: Our findings indicate that firms adjust their dividend policies during the pre-crisis and post-crisis periods; more profitable and larger firms are more likely to distribute their dividend payouts, whereas firms with higher leverage are more likely to omit their dividends. Moreover, dividend policies that will increase firms’ valuation are adopted in Malaysia. This is reflected in the signalling theory with evidence that higher profitability exerts a positive influence on firms’ propensity to increase and/or maintain dividends over different study periods, implying that markets attach a high valuation to firms that can pay, especially during the crisis period. We also find the role of catering theory and smoothing hypothesis lost relevance in both crisis and non-crisis periods. Thus, the catering theory and smoothing hypothesis were not supported in Malaysia. Theoretical contribution/Originality: This study investigates the impact of the global financial crisis on Malaysia non-financial index firms’ dividend policies. This paper suggestion can act as a catalyst to more comprehensive and detailed researches and studies on dividend policy in any economic landscapes. Practitioner/Policy implications: This paper may also guide companies on the structure and use of dividend distribution over the precrisis, during the crisis, and post-crisis periods. Research limitation/Implications: One limitation of the study is that the measures used for dividend payout determinants are only based on the theory investigated. These measures may not completely reflect all the payout determinants. Future research could address this limitation by employing other factors in the study of dividend policy such as inflation, economic growth, and corporate governance.