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.

Cryptocurrencies and portfolio diversification in an emerging market

PurposeThis paper examines the effect of cryptocurrencies on the portfolio risk-adjusted returns of traditional and alternative investments within an emerging market economy.Design/methodology/approachThe paper employs daily arithmetic returns from August 2015 to October 2018 of traditional assets (stocks, bonds, currencies), alternative assets (commodities, real estate) and cryptocurrencies. Using the mean-variance analysis, the Sharpe ratio, the conditional value-at-risk and the mean-variance spanning tests.FindingsThe paper documents evidence to support the diversification benefits of cryptocurrencies by utilising the mean-variance tests, improving the efficient frontier and the risk-adjusted returns of the emerging market economy portfolio of investments.Practical implicationsThis paper firmly broadens the Modern Portfolio Theory by authenticating cryptocurrencies as assets with diversification benefits in an emerging market economy investment portfolio.Originality/valueAs far as the authors are concerned, this paper presents the first evidence of the effect of diversification benefits of cryptocurrencies on emerging market asset portfolios constructed using traditional and alternative assets.

The Risk Measurement under the Variance-Gamma Process with Drift Switching

The paper discusses an extension of the variance-gamma process with stochastic linear drift coefficient. It is assumed that the linear drift coefficient may switch to a different value at the exponentially distributed time. The size of the drift jump is supposed to have a multinomial distribution. We have obtained the distribution function, the probability density function and the lower partial expectation for the considered process in closed forms. The results are applied to the calculation of the value at risk and the expected shortfall of the investment portfolio in the related multivariate stochastic model.

Financial Asset Management Using Artificial Neural Networks

Investors typically build portfolios for retirement. Investment portfolios are typically based on four asset classes that are commonly managed by large investment firms. The research presented in this article involves the development of an artificial neural network-based methodology that investors can use to support decisions related to determining how assets are allocated within an investment portfolio. The machine learning-based methodology was applied during a time period that included the stock market crash of 2008. Even though this time period was highly volatile, the methodology produced desirable results. Methodologies such as the one presented in this article should be considered by investors because they have produced promising results, especially within unstable markets.

Research on Optimal Investment Reinsurance of Insurance Companies under Delayed Risk Model

The reinsurance and investment portfolio of insurance companies has always been a hot issue in insurance business. In insurance practice, it is inevitable for insurance companies to invest their own funds in order to expand their capital scale and enhance market competitiveness so as to obtain greater returns. At the same time, in order for insurance companies to disperse insurance risks and to avoid too concentrated claims or catastrophes caused by failure to perform compensation responsibilities, the purchase of reinsurance business has also become an important way. Stochastic control theory is widely used in reinsurance and investment issues. Based on the reinsurance system architecture, this paper establishes a reinsurance delay risk investment model, which reduces the amount of claims to be borne by buying proportional reinsurance to avoid bankruptcy caused by the excessive amount of claims. By using the delayed venture capital model to describe the earnings of insurance companies, the optimal investment and reinsurance strategy are solved under the optimization criterion of minimizing the probability of bankruptcy. By analyzing the model parameter data, the influence of each parameter on optimal investment strategy and optimal reinsurance strategy is discussed.

تحليل العائد والمخاطر لأسهم المحفظة الاستثمارية لعينة المصارف التجارية العراقية الخاصة للمدة 2015-2019

The importance of the research lies in revealing the ability of private Iraqi commercial banks to maximize bank returns by attracting investments in the Iraq Stock Exchange, and the aim of the research is to how to manage the investment portfolio of private Iraqi commercial banks in the Iraq Stock Exchange and in a manner that achieves the highest return and the least risk, in addition In order to know which of those banks has an optimal portfolio, the research assumed that there was an effect of forming an investment portfolio in maximizing the return and reducing the risks for the banks. Statistical methods such as the variance rate, standard deviation and (beta) coefficient were used in measuring the return and risk of stocks to analyze the data. In the fluctuation of the achieved return of shares and the achieved return of the market from one year to another within the period, as well as the change in risk for bank shares from one year to another and during the period and the researcher made some proposals in the form of recommendations to increase the effectiveness of these banks in their investments, the most important of which was how to manage the investment portfolio in banks and increase investment awareness The investors and the need to adhere to the instructions of the Iraq Stock Exchange, as well as work to attract harm Invest in order to increase circulation in the market.

Two-Stage Robust Optimization Model for Uncertainty Investment Portfolio Problems

Investment portfolio can provide investors with a more robust financial management plan, but the uncertainty of its parameters is a key factor affecting performance. This paper conducts research on investment portfolios and constructs a two-stage mixed integer programming (TS-MIP) model, which comprehensively considers the five dimensions of profit, diversity, skewness, information entropy, and conditional value at risk. But the deterministic TS-MIP model cannot cope with the uncertainty. Therefore, this paper constructs a two-stage robust optimization (TS-RO) model by introducing robust optimization theory. In case experiments, data crawler technology is used to obtain actual data from real websites, and a variety of methods are used to verify the effectiveness of the proposed model in dealing with uncertainty. The comparison of models found that, compared with the traditional equal weight model, the investment benefits of the TS-MIP model and the TS-RO model proposed have been improved. Among them, the Sharpe ratio, Sortino ratio, and Treynor ratio have the largest increase of 19.30%, 8.25%, and 7.34%, respectively.