CVaR Prediction Model of the Investment Portfolio Based on the Convolutional Neural Network Facilitates the Risk Management of the Financial Market

In summary, firstly, a method for establishing a portfolio model is proposed based on the risk management theory of the financial market. Then, a prediction model for CVaR is established based on the convolutional neural network, and the improved particle swarm algorithm is employed to solve the model. The actual data analysis is implemented to prove the feasibility of CVaR prediction model based on deep learning and particle swarm optimization algorithm in financial market risk management. The test results show that the investment portfolio CVaR prediction model based on the convolutional neural network can obtain the optimal solution in the 18th generation at the fastest after using the improved particle swarm algorithm, which is more effective than the traditional algorithm. The CVaR prediction model of the investment portfolio based on the convolutional neural network facilitates the risk management of the financial market.

Towards an Anatomy of Software Craftsmanship

Context: The concept of software craftsmanship has early roots in computing, and in 2009, the Manifesto for Software Craftsmanship was formulated as a reaction to how the Agile methods were practiced and taught. But software craftsmanship has seldom been studied from a software engineering perspective. Objective: The objective of this article is to systematize an anatomy of software craftsmanship through literature studies and a longitudinal case study. Method: We performed a snowballing literature review based on an initial set of nine papers, resulting in 18 papers and 11 books. We also performed a case study following seven years of software development of a product for the financial market, eliciting qualitative, and quantitative results. We used thematic coding to synthesize the results into categories. Results: The resulting anatomy is centered around four themes, containing 17 principles and 47 hierarchical practices connected to the principles. We present the identified practices based on the experiences gathered from the case study, triangulating with the literature results. Conclusion: We provide our systematically derived anatomy of software craftsmanship with the goal of inspiring more research into the principles and practices of software craftsmanship and how these relate to other principles within software engineering in general.

Trade-based money laundering: development of the «red flags» system

The OECD and the FATF highlight the problem of money laundering via international trade with a view to disguising illicit gains and moving value through the use of trade transactions. For example, inaccurate invoices may be used, which, according to the Global Financial Integrity estimates, resulted in $0,9 trillion to $1,7 trillion losses in 148 countries in 2006–2015. In Russia, the authorities attempt to reduce the risks of money laundering within the framework of international trade through the use of currency regulation, while foreign countries are using a risk-based approach by developing the “red flags” systems that allow financial intelligence agencies, customs and other state bodies as well as subjects of financial market (through which the payments for export-import transactions are made) and the companies participating in international trade themselves to determine whether a transaction entails risks of money laundering. In addition, internal and international inter-agency exchange of information related to money laundering in international trade, including trade and financial data, is being developed.

Research on prediction of China’s financial systematic risk based on the hybrid model

An important feature of the outbreak of systemic financial risk is that the linkage and contagion of risk amongst the various sub-markets of the financial system have increased significantly. In addition, research on the prediction of systemic financial risk plays a significant role in the sustainable development of the financial market. Therefore, this paper takes China’s financial market as its research object, considers the risks co-activity among major financial sub-markets, and constructs a financial composite indicator of systemic stress (CISS) for China, describing its financial systemic stress based on 12 basic indicators selected from the money market, bond market, stock market, and foreign exchange market. Furthermore, drawing on the decomposition and integration technology in the TEI@I complex system research methodology, this paper introduces advanced variational mode decomposition (VMD) technology and extreme learning machine (ELM) algorithms, constructing the VMD-DE-ELM hybrid model to predict the systemic risk of China’s financial market. According to e RMSE , e MAE , and e MAPE , the prediction model’s multistep-ahead forecasting effect is evaluated. The empirical results show that the China’s financial CISS constructed in this paper can effectively identify all kinds of risk events in the sample range. The results of a robustness test show that the overall trend of China’s financial CISS and its ability to identify risk events are not affected by parameter selection and have good robustness. In addition, compared with the benchmark model, the VMD-DE-ELM hybrid model constructed in this paper shows superior predictive ability for systemic financial risk.

Volatility spillovers among G7, E7 stock markets and cryptocurrencies

PurposeThe existence of long memory and persistent volatility characteristics of cryptocurrencies justifies the investigation of return and volatility/shock spillovers between traditional financial market asset classes and cryptocurrencies. The purpose of this paper is to investigate the dynamic relationship between the cryptocurrencies, namely Bitcoin and Ethereum, and stock market indices of G7 and E7 countries to analyze the return and volatility spillover patterns among these markets by means of multivariate (MGARCH) approach.Design/methodology/approachApplying the newly developed VAR-GARCH-in mean framework with the BEKK representation, the empirical results reveal that there exists an evidence of mean and volatility spillover effects among Bitcoin and Ethereum as the proxies for the cryptocurrencies, and stock markets reviewed.FindingsInterestingly, the direction of the return and volatility spillover effects is unidirectional in most E7 countries, but bidirectional relationship was found in most G7 countries. This can be explained as the presence of a strong return and volatility interaction among G7 stock markets and crypto market.Originality/valueOverall, the results of this study are of particular interest for portfolio management since it provides insights for financial market participants to make better portfolio allocation decisions. It is also increasingly important to understand the volatility transmission mechanism across these markets to provide policymakers and regulatory bodies with guidance to eliminate the negative impact of cryptocurrency's volatility on the stability of financial markets.

The Role of Non-Bank Financials in the Formation of Long-Term Resources for Economic Growth in Russia

The development of the activities of non-bank financial institutions that accumulate the resources of the national savings system on a long-term basis is seen as a factor in increasing investment in the Russian economy and its growth rates. When carrying out the study, we used general scientific methods, methods of structural, weigh, and dynamic analysis, and comparisons of performance indicators of non-bank financial institutions. Problems in the activities of organizations in the non-banking sector of the Russian financial market are predetermined by the parameters and trends in the development of the socio-economic situation in Russia, including insufficient efficiency of regulatory practices. The positive dynamics of the development of non-bank financial intermediaries is qualified as unstable; it is not supported by the solution of the structural and institutional problems of the Russian economy. In view of this, an increase in their role in the redistribution process is associated both with decisions of a more general order and with the improvement of the regulatory and supervisory practices implemented by the Bank of Russia. The solution to the identified problems in the development of the non-banking segment of the financial market should be aimed at turning it into an effective mechanism for capital formation to ensure economic growth.

Econometric Modeling to Measure the Efficiency of Sharpe’s Ratio with Strong Autocorrelation Portfolios

Sharpe’s ratio is the most widely used index for establishing an order of priority for the portfolios to which the investor has access, and the purpose of this investigation is to verify that Sharpe’s ratio allows decisions to be made in investment portfolios considering different financial market conditions. The research is carried out by autoregressive model (AR) of the financial series of returns using Sharpe’s ratio for evaluations looking over the priority of financial assets which the investor can access while observing the effects that can cause autocorrelated series in evaluation measures for financial assets. The results presented in this study confirm the hypothesis proposed in which Sharpe’s ratio allows decisions to be made in the selection of investment portfolios under normal conditions thanks to the definition of a robustness function, whose empirical estimation shows an average 73% explanation of the variance in the degradation of the Spearman coefficient for each of the performance measures; however, given the presence of autocorrelation in the financial series of returns, this similarity is broken.