Research On Portfolio Optimization Based On Quantitative Analysis Of Deep Learning

In the past few years,due to the impact of multiple factors such as the global pandemic,external shocks,and factor structure,China’s economic growth has faced significant pressure,especially under the requirements of high capital investment constraints.Technology and innovation have become the key to improving efficiency.Multiple liquidity management tools need to be continuously created and applied in financial markets to achieve the goal of accurately and effectively improving capital returns and optimizing asset portfolios.At the same time,the transmission speed of risks between international financial markets is constantly accelerating,posing higher requirements for updated risk prediction.Therefore,improving capital market efficiency,optimizing investment portfolios,and effectively measuring and testing risks have become key issues that modern financial analysis urgently needs to address.Portfolio optimization is an important research topic in modern investment theory.With the advent of artificial intelligence and big data,portfolio theory has become an intersection of multiple disciplines such as mathematical statistics,machine learning,and behavioral finance.Exploring the optimization of asset portfolios,risk spillovers,and influencing factors is of great theoretical and practical significance for promoting the healthy development of capital markets and maintaining economic and financial security.In the research and practice of capital returns and portfolio theory,the traditional methods of analyzing returns and risk spillovers in the capital market are no longer applicable.The emergence and successful application of deep learning and dynamic R-Vine Copula functions provide a good solution.Based on this,this article takes asset optimization and portfolio optimization as the starting point,adopts a research method combining mathematical statistics and case analysis,incorporates risk factors in the optimized asset portfolio,investor risk preferences and other behavioral characteristics into the model,comprehensively analyzes the investment portfolio optimization model using deep learning and advanced functions,and accurately measures risk and tests risk spillovers.The main research content of this article includes: firstly,by constructing a stock selection model,the goal of effectively and accurately selecting outstanding stocks is achieved,laying a theoretical and foundation for optimizing asset portfolios;Secondly,in the asset portfolio optimization stage,three different methods are adopted,namely naive strategy,Markowitz model,and Mean CVa R model.Using stocks with high returns,the initial value was set to 1 yuan,and combined with comparative investment results,Markowitz models and Mean CVa R models with different k values were calculated.The data is divided into two parts: within sample and outside sample.Simple investments are calculated based on the within sample data and the rolling window principle,and the differences between the models are compared.It is concluded that the Mean CVa R model can obtain investment portfolios that maximize returns and minimize risks;Once again,considering that the R-Vine Copula function can accurately characterize the risk correlation between high-dimensional financial markets,the R-Vine Copula function is used to analyze the optimization effect of investment portfolios under the constraint of risk contagion,in order to ensure the reliability and accuracy of research on investment portfolio optimization under risk contagion;Finally,based on the quantitative analysis of portfolio risk contagion,the influencing factors of capital market risk management were explored from the perspectives of economic policy uncertainty,external shocks,macroeconomic conditions,etc.The Copula function was used to further explore the correlation between different types of financial assets,represented by emerging digital currency assets in recent years.Research has found that establishing a constrained Boltzmann machine(RBM)and LSTM neural network configuration optimization model,using mean absolute directional loss(MADL)to evaluate performance,can yield potential stocks with high returns;Investors can choose Markowitz models and Mean CVa R models with different K values based on their own risk preferences to obtain corresponding investment returns;The constructed R-Vine Copula function can more effectively characterize the risk contagion relationship between high-dimensional financial assets.Among external influencing factors,financial crises and external shocks have a positive impact,and interest rate differences reduce the risk contagion between assets.Digital currencies have a certain risk hedging ability against traditional stock markets.The theoretical contributions of this article are as follows: Firstly,in situations where conventional low interest rate monetary policies cannot effectively improve market liquidity,increasing asset returns and optimizing investment portfolios can achieve the goal of accelerating capital circulation and promoting economic growth;Secondly,strengthen the construction of financial markets and quickly and accurately estimate the time-varying investment weight coefficients in portfolio optimization;Thirdly,fully consider the correlation and risk contagion between the stock market and the digital currency market,as well as the current economic policy uncertainty,in order to more accurately predict the trend of the international financial market.This article innovatively applies deep learning to quantify financial transactions,providing new ideas and theoretical foundations for the research topic of portfolio optimization;The proposed intelligent models for asset optimization and portfolio optimization have achieved global optimization and strong data pre selection capabilities;The model can more accurately match assets,predict asset risk conditions,and thus derive the optimal investment portfolio optimization plan.