This study assesses the portfolio concentration of socially responsible investment(SRI)pension funds,which may be subject to a potentially limited asset universe and have a higher concentration and lower performance t...This study assesses the portfolio concentration of socially responsible investment(SRI)pension funds,which may be subject to a potentially limited asset universe and have a higher concentration and lower performance than conventional funds.Nonetheless,in contrast to previous studies on SRI funds,this study considers the informationadvantage theory,positing that skilled managers should increase their concentration in assets in which they possess valuable information,departing from optimization models to achieve outperformance.This study first compares actual fund concentration with concentration obtained from several traditional and modern portfolio optimization techniques(minimum variance,global minimum variance,optimal portfolio,naïve diversification,risk parity,and reward-to-risk timing)to understand whether SRI pension funds concentrate portfolios and deviate from optimization model solutions.Unlike previous studies,the actual fund assets are considered in the optimization models to take into account the real investment profiles of SRI funds.The results indicate that SRI pension funds are less concentrated than conventional funds,and SRI and conventional pension funds largely diversify their portfolios,presenting lower concentration than portfolios formed with the optimization models.Furthermore,concentration strategies positively influence performance in SRI and conventional funds,revealing the use of information advantage.However,SRI and conventional fund managers present poor skills(picking,timing,and trading)to exploit information advantages due to overconfidence issues,which affect performance with concentration strategies.This situation may be modified if SRI funds follow modern optimization models and conventional funds follow traditional optimization models,improving managers’performance and skills.展开更多
The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies.These advances span fundamental theoretical inquiries into quantum information and the explo...The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies.These advances span fundamental theoretical inquiries into quantum information and the exploration of diverse applications arising from this evolving quantum computing paradigm.The scope of the related research is notably diverse.This paper consolidates and presents quantum computing research related to the financial sector.The finance applications considered in this study include portfolio optimization,fraud detection,and Monte Carlo methods for derivative pricing and risk calculation.In addition,we provide a comprehensive analysis of quantum computing’s applications and effects on blockchain technologies,particularly in relation to cryptocurrencies,which are central to financial technology research.As discussed in this study,quantum computing applications in finance are based on fundamental quantum physics principles and key quantum algorithms.This review aims to bridge the research gap between quantum computing and finance.We adopt a two-fold methodology,involving an analysis of quantum algorithms,followed by a discussion of their applications in specific financial contexts.Our study is based on an extensive review of online academic databases,search tools,online journal repositories,and whitepapers from 1952 to 2023,including CiteSeerX,DBLP,Research-Gate,Semantic Scholar,and scientific conference publications.We present state-of-theart findings at the intersection of finance and quantum technology and highlight open research questions that will be valuable for industry practitioners and academicians as they shape future research agendas.展开更多
This work focuses on the optimization of investment contributions of pension asset with a view to improving contributors’ participation in achieving better return on investment (RoI) of their funds. We viewed some ne...This work focuses on the optimization of investment contributions of pension asset with a view to improving contributors’ participation in achieving better return on investment (RoI) of their funds. We viewed some new regulations on Nigeria’s Contributory Pension Scheme” (CPS) from amended legislation of 2014, some of which are yet to be implemented when their regulations are approved. A mathematical model involving 5 variables, 5 inequality constraints covering regulatory limitations and limitation on scarce resource known as Asset Under Management (AUM), suggested and mathematically shown to be possible through “maximization of return irrespective of risk” while obeying all regulatory controls as our constraints optimized. Optimized portfolio using MatLab shows that the portfolio representing AES 2013 portfolio with a deficit growth of 15.75 m representing 3.27% less than the portfolio’s full growth potential within defined assumptions would have been averted if contributors actually set their targets and investment managers optimize from forecasts of future prices using trend analysis.展开更多
This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search pro...This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search process. The former is essential to enhance the realism of the classical mean-variance model proposed by Harry Markowitz, since portfolio managers often face a number of realistic constraints arising from business and industry regulations, while the latter reflects the fact that portfolio managers are ultimately interested in specific regions or points along the efficient frontier during the actual execution of their investment orders. For the former, this paper proposes an order-based representation that can be easily extended to handle various realistic constraints like floor and ceiling constraints and cardinality constraint. An experimental study, based on benchmark problems obtained from the OR-library, demonstrates its capability to attain a better approximation of the efficient frontier in terms of proximity and diversity with respect to other conventional representations. The experimental results also illustrated its viability and practicality in handling the various realistic constraints. A simple strategy to incorporate preferences into the multi-objective optimization process is highlighted and the experimental study demonstrates its capability in driving the evolutionary search towards specific regions of the efficient frontier.展开更多
Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk...Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk factor in drawing up an efficient frontier and the optimal portfolio. Since semi-variance offers a better estimation of the actual risk portfolio, it was used as a measure to approximate the risk of investment in this work. The optimal portfolio selection is one of the non-deterministic polynomial(NP)-hard problems that have not been presented in an exact algorithm, which can solve this problem in a polynomial time. Meta-heuristic algorithms are usually used to solve such problems. A novel hybrid harmony search and artificial bee colony algorithm and its application were introduced in order to draw efficient frontier portfolios. Computational results show that this algorithm is more successful than the harmony search method and genetic algorithm. In addition, it is more accurate in finding optimal solutions at all levels of risk and return.展开更多
In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial ...In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.展开更多
Tail risk is a classic topic in stressed portfolio optimization to treat unprecedented risks,while the traditional mean–variance approach may fail to perform well.This study proposes an innovative semiparametric meth...Tail risk is a classic topic in stressed portfolio optimization to treat unprecedented risks,while the traditional mean–variance approach may fail to perform well.This study proposes an innovative semiparametric method consisting of two modeling components:the nonparametric estimation and copula method for each marginal distribution of the portfolio and their joint distribution,respectively.We then focus on the optimal weights of the stressed portfolio and its optimal scale beyond the Gaussian restriction.Empirical studies include statistical estimation for the semiparametric method,risk measure minimization for optimal weights,and value measure maximization for the optimal scale to enlarge the investment.From the outputs of short-term and long-term data analysis,optimal stressed portfolios demonstrate the advantages of model flexibility to account for tail risk over the traditional mean–variance method.展开更多
In this study,we analyze three portfolio selection strategies for loss-averse investors:semi-variance,conditional value-at-risk,and a combination of both risk measures.Moreover,we propose a novel version of the non-do...In this study,we analyze three portfolio selection strategies for loss-averse investors:semi-variance,conditional value-at-risk,and a combination of both risk measures.Moreover,we propose a novel version of the non-dominated sorting genetic algorithm II and of the strength Pareto evolutionary algorithm 2 to tackle this optimization problem.The effectiveness of these algorithms is compared with two alternatives from the literature from five publicly available datasets.The computational results indicate that the proposed algorithms in this study outperform the others for all the examined performance metrics.Moreover,they are able to approximate the Pareto front even in cases in which all the other approaches fail.展开更多
The emergence and growing popularity of Bitcoins have attracted the attention of the financial world.However,few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commo...The emergence and growing popularity of Bitcoins have attracted the attention of the financial world.However,few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market.It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio.In this study,we propose a novel ensemble portfolio optimization(NEPO)framework utilized for broad commodity assets,which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation.Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors.In addition,it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies,commodity funds,and indices.展开更多
This article presents a semi-Markov process based approach to optimally select a portfolio consisting of credit risky bonds.The criteria to optimize the credit portfolio is based on l_(∞)-norm risk measure and the pr...This article presents a semi-Markov process based approach to optimally select a portfolio consisting of credit risky bonds.The criteria to optimize the credit portfolio is based on l_(∞)-norm risk measure and the proposed optimization model is formulated as a linear programming problem.The input parameters to the optimization model are rate of returns of bonds which are obtained using credit ratings assuming that credit ratings of bonds follow a semi-Markov process.Modeling credit ratings by semi-Markov processes has several advantages over Markov chain models,i.e.,it addresses the ageing effect present in the credit rating dynamics.The transition probability matrices generated by semi-Markov process and initial credit ratings are used to generate rate of returns of bonds.The empirical performance of the proposed model is analyzed using the real data.Further,comparison of the proposed approach with the Markov chain approach is performed by obtaining the efficient frontiers for the two models.展开更多
For oil company decision-makers,the principal concern is how to allocate their limited resources into the most valuable opportunities.Recently a new management philosophy,＂Beyond NPV＂,has received more and more inte...For oil company decision-makers,the principal concern is how to allocate their limited resources into the most valuable opportunities.Recently a new management philosophy,＂Beyond NPV＂,has received more and more international attention.Economists and senior executives are seeking effective alternative analysis approaches for traditional technical and economic evaluation methods.The improved portfolio optimization model presented in this article represents an applicable technique beyond NPV for doing capital budgeting.In this proposed model,not only can oil company executives achieve trade-offs between returns and risks to their risk tolerance,but they can also employ an ＂operational premium＂ to distinguish their ability to improve the performance of the underlying projects.A simulation study based on 19 overseas upstream assets owned by a large oil company in China is conducted to compare optimized utility with non-optimized utility.The simulation results show that the petroleum optimization model including ＂operational premium＂ is more in line with the rational investors＇ demand.展开更多
In this paper, the discrete mean-variance model is considered for portfolio selection under concave transaction costs. By using the Cholesky decomposition technique, the convariance matrix to obtain a separable mixed ...In this paper, the discrete mean-variance model is considered for portfolio selection under concave transaction costs. By using the Cholesky decomposition technique, the convariance matrix to obtain a separable mixed integer nonlinear optimization problem is decomposed. A brand-and-bound algorithm based on Lagrangian relaxation is then proposed. Computational results are reported for test problems with the data randomly generated and those from the US stock market.展开更多
In the new competitive environment of the electricity market, risk analysis is a powerful tool to guide investors under both contract uncertainties and energy prices of the spot market. Moreover, simulation of spot pr...In the new competitive environment of the electricity market, risk analysis is a powerful tool to guide investors under both contract uncertainties and energy prices of the spot market. Moreover, simulation of spot price scenarios and evaluation of energy contracts performance, are also necessary to the decision maker, and in particular to the trader to foresee opportunities and possible threats in the trading activity. In this context, computational systems that allow what-if analysis, involving simulation of spot price, contract portfolio optimization and risk evaluation are rather important. This paper proposes a decision support system not only for solving the problem of contracts portfolio optimization, by using linear programming, but also to execute risks analysis of the contracts portfolio performance, with VaR and CVaR metrics. Realistic tests have demonstrated the efficiency of this system.展开更多
This study investigates the return dynamics,volatility structure,and risk characteristics of five representative S&P 500 stocks:Johnson&Johnson,Microsoft,NVIDIA,Coca-Cola,and Home Depot,using ARMA-GARCH models...This study investigates the return dynamics,volatility structure,and risk characteristics of five representative S&P 500 stocks:Johnson&Johnson,Microsoft,NVIDIA,Coca-Cola,and Home Depot,using ARMA-GARCH models.Descriptive statistics and diagnostic tests confirm non-normality,negative skewness,fat tails,and volatility clustering,providing strong justification for conditional mean-variance modelling.Optimal model specifications are selected via the Bayesian Information Criterion,with EGARCH frameworks generally outperforming alternative GARCH variants in capturing asymmetric volatility responses.Rolling-window forecasts for 2024Q1 show that the models generate stable and reliable volatility predictions for low-volatility stocks(JNJ,KO),while performance is weaker for highly volatile stocks(NVDA),highlighting structural limitations under extreme market shifts.To evaluate risk management implications,one percent Value-at-Risk and expected shortfall were computed and backtested.Results indicated conservative tail-risk forecasts,with violation rates well within acceptable thresholds.Portfolio applications are further explored by constructing the Global Minimum Variance Portfolio(GMVP)and the Maximum Sharpe Ratio(Max SR)portfolio using rolling covariance estimates.Out-of-sample backtesting demonstrated that the GMVP delivered low volatility but modest returns,whereas the Max SR portfolio achieved significantly higher performance,consistent with the risk-return trade-off.Overall,the findings confirm that ARMA-GARCH models are effective tools for modelling conditional volatility and informing dynamic asset allocation.However,their limited adaptability to jump risk and nonlinear structural breaks underscores the need for more advanced modelling approaches in high-volatility environments.展开更多
This article is concerned with a class of control systems with Markovian switching, in which an It5 formula for Markov-modulated processes is derived. Moreover, an optimal control law satisfying the generalized Hamilt...This article is concerned with a class of control systems with Markovian switching, in which an It5 formula for Markov-modulated processes is derived. Moreover, an optimal control law satisfying the generalized Hamilton-Jacobi-Bellman （HJB） equation with Markovian switching is characterized. Then, through the generalized HJB equation, we study an optimal consumption and portfolio problem with the financial markets of Markovian switching and inflation. Thus, we deduce the optimal policies and show that a modified Mutual Fund Theorem consisting of three funds holds. Finally, for the CRRA utility function, we explicitly give the optimal consumption and portfolio policies. Numerical examples are included to illustrate the obtained results.展开更多
Rule-based portfolio construction strategies are rising as investmentdemand grows, and smart beta strategies are becoming a trend amonginstitutional investors. Smart beta strategies have high transparency, lowmanageme...Rule-based portfolio construction strategies are rising as investmentdemand grows, and smart beta strategies are becoming a trend amonginstitutional investors. Smart beta strategies have high transparency, lowmanagement costs, and better long-term performance, but are at the risk ofsevere short-term declines due to a lack of Risk Control tools. Although thereare some methods to use historical volatility for Risk Control, it is still difficultto adapt to the rapid switch of market styles. How to strengthen the RiskControl management of the portfolio while maintaining the original advantagesof smart beta has become a new issue of concern in the industry. Thispaper demonstrates the scientific validity of using a probability prediction forposition optimization through an optimization theory and proposes a novelnatural gradient boosting (NGBoost)-based portfolio optimization method,which predicts stock prices and their probability distributions based on non-Bayesian methods and maximizes the Sharpe ratio expectation of positionoptimization. This paper validates the effectiveness and practicality of themodel by using the Chinese stock market, and the experimental results showthat the proposed method in this paper can reduce the volatility by 0.08 andincrease the expected portfolio cumulative return (reaching a maximum of67.1%) compared with the mainstream methods in the industry.展开更多
In order to study the effect of different risk measures on the efficient portfolios （fron- tier） while properly describing the characteristic of return distributions in the stock market, it is assumed in this paper ...In order to study the effect of different risk measures on the efficient portfolios （fron- tier） while properly describing the characteristic of return distributions in the stock market, it is assumed in this paper that the joint return distribution of risky assets obeys the multivariate t-distribution. Under the mean-risk analysis framework, the interrelationship of efficient portfolios （frontier） based on risk measures such as variance, value at risk （VaR）, and expected shortfall （ES） is analyzed and compared. It is proved that, when there is no riskless asset in the market, the efficient frontier under VaR or ES is a subset of the mean-variance （MV） efficient frontier, and the efficient portfolios under VaR or ES are also MV efficient; when there exists a riskless asset in the market, a portfolio is MV efficient if and only if it is a VaR or ES efficient portfolio. The obtained results generalize relevant conclusions about investment theory, and can better guide investors to make their investment decision.展开更多
This paper investigates an optimal investment strategy on consumption and portfolio problem, in which the investor must withdraw funds continuously at a given rate. By analyzing the evolving process of wealth, we give...This paper investigates an optimal investment strategy on consumption and portfolio problem, in which the investor must withdraw funds continuously at a given rate. By analyzing the evolving process of wealth, we give the definition of safe-region for investment. Moreover, in order to obtain the target wealth as quickly as possible, using Bellman dynamic programming principle, we get the optimal investment strategy and corresponding necessary expected time. At last we give some numerical computations for a set of different parameters.展开更多
The high-frequency futures market exhibits high volatility and frequent institutional changes,which poses significant challenges for portfolio optimization.Traditional techniques such as the mean-variance model and ri...The high-frequency futures market exhibits high volatility and frequent institutional changes,which poses significant challenges for portfolio optimization.Traditional techniques such as the mean-variance model and risk equalization tend to show decreased returns and increased risks in such non-stationary markets.To address this challenge,an online portfolio optimization framework based on meta-learning was proposed.This framework incorporates cross-market and cross-period experiences into parameter adjustments,enabling it to quickly adapt to new institutional environments.Minute-level futures data from the Chicago Mercantile Exchange,New York Mercantile Exchange,and Shanghai Futures Exchange from 2019 to 2024 were used,and the market states were labeled using the Markov switching model through the rolling window method.Comparative experiments were conducted with mean-variance optimization,risk equalization,and deep reinforcement learning benchmarks.Empirical results show that the proposed framework outperforms the benchmarks in terms of excess returns,Sharpe ratio,and maximum drawdown in most cases,and has a faster convergence speed and stronger generalization ability in high-volatility institutional environments.The conclusion drawn from these results is that meta-learning is an effective method for solving portfolio optimization problems in non-stationary markets,providing theoretical support and empirical contributions for highfrequency quantitative trading and asset pricing research.展开更多
Investing in cryptocurrencies is progressively becoming a norm;however,these assets are excessively volatile and often decrease or increase in value instantly.Thus,rational investors holding cryptocurrencies for exten...Investing in cryptocurrencies is progressively becoming a norm;however,these assets are excessively volatile and often decrease or increase in value instantly.Thus,rational investors holding cryptocurrencies for extended periods firmly search for assets that can diversify their risk,preferably with assets other than cryptocurrencies.In this study,we consider the two most studied cryptocurrencies with the highest capitalization and trading volume/value,namely Bitcoin and Ethereum.Specifically,we examine whether high-performing leading US tech stocks(Facebook,Amazon,Apple,Netflix,Google[FAANG])can provide any diversification benefits to cryptocurrency investors.To do so,we employ dynamic conditional correlation(DCC),asymmetric DCC,time-varying parameter vector autoregression-based connectedness measures,dynamic correlation-based hedge and safe-haven regression analyses,portfolio optimization and hedging strategies,time-and frequency-based wavelet coherence,and high-frequency 10-min intraday data from January 1,2018 to January 31,2023.We find that FAANG stocks can be considered(at least weak)safe havens for Bitcoin and Ethereum during the sample period.Our subperiod analyses reveal that the safehaven role of FAANG stocks,specifically for Bitcoin,has noticeably increased.While the safe-haven property of Facebook is the most promising,for Netflix it is blurred between a weak–safe-haven and a hedge.Our findings may help investors,policymakers,and academicians to invest in cryptocurrencies,formulate relevant investment guidelines,and extend the literature on cryptocurrencies,respectively.展开更多
基金supported by Government of Aragon[Grant S38_20R]Ibercaja and University of Zaragoza[Grants JIUZ-2021-SOC-03 and JIUZ2022-CSJ-24]Ministerio de Ciencia e Innovación and FEDER[PID2022-136818NB-100].
文摘This study assesses the portfolio concentration of socially responsible investment(SRI)pension funds,which may be subject to a potentially limited asset universe and have a higher concentration and lower performance than conventional funds.Nonetheless,in contrast to previous studies on SRI funds,this study considers the informationadvantage theory,positing that skilled managers should increase their concentration in assets in which they possess valuable information,departing from optimization models to achieve outperformance.This study first compares actual fund concentration with concentration obtained from several traditional and modern portfolio optimization techniques(minimum variance,global minimum variance,optimal portfolio,naïve diversification,risk parity,and reward-to-risk timing)to understand whether SRI pension funds concentrate portfolios and deviate from optimization model solutions.Unlike previous studies,the actual fund assets are considered in the optimization models to take into account the real investment profiles of SRI funds.The results indicate that SRI pension funds are less concentrated than conventional funds,and SRI and conventional pension funds largely diversify their portfolios,presenting lower concentration than portfolios formed with the optimization models.Furthermore,concentration strategies positively influence performance in SRI and conventional funds,revealing the use of information advantage.However,SRI and conventional fund managers present poor skills(picking,timing,and trading)to exploit information advantages due to overconfidence issues,which affect performance with concentration strategies.This situation may be modified if SRI funds follow modern optimization models and conventional funds follow traditional optimization models,improving managers’performance and skills.
基金Gerhard Hellstern is partly funded by the Ministry of Economic Affairs,Labour and Tourism Baden-Württemberg in the frame of the Competence Center Quantum Computing Baden-Württemberg(QORA Ⅱ).
文摘The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies.These advances span fundamental theoretical inquiries into quantum information and the exploration of diverse applications arising from this evolving quantum computing paradigm.The scope of the related research is notably diverse.This paper consolidates and presents quantum computing research related to the financial sector.The finance applications considered in this study include portfolio optimization,fraud detection,and Monte Carlo methods for derivative pricing and risk calculation.In addition,we provide a comprehensive analysis of quantum computing’s applications and effects on blockchain technologies,particularly in relation to cryptocurrencies,which are central to financial technology research.As discussed in this study,quantum computing applications in finance are based on fundamental quantum physics principles and key quantum algorithms.This review aims to bridge the research gap between quantum computing and finance.We adopt a two-fold methodology,involving an analysis of quantum algorithms,followed by a discussion of their applications in specific financial contexts.Our study is based on an extensive review of online academic databases,search tools,online journal repositories,and whitepapers from 1952 to 2023,including CiteSeerX,DBLP,Research-Gate,Semantic Scholar,and scientific conference publications.We present state-of-theart findings at the intersection of finance and quantum technology and highlight open research questions that will be valuable for industry practitioners and academicians as they shape future research agendas.
文摘This work focuses on the optimization of investment contributions of pension asset with a view to improving contributors’ participation in achieving better return on investment (RoI) of their funds. We viewed some new regulations on Nigeria’s Contributory Pension Scheme” (CPS) from amended legislation of 2014, some of which are yet to be implemented when their regulations are approved. A mathematical model involving 5 variables, 5 inequality constraints covering regulatory limitations and limitation on scarce resource known as Asset Under Management (AUM), suggested and mathematically shown to be possible through “maximization of return irrespective of risk” while obeying all regulatory controls as our constraints optimized. Optimized portfolio using MatLab shows that the portfolio representing AES 2013 portfolio with a deficit growth of 15.75 m representing 3.27% less than the portfolio’s full growth potential within defined assumptions would have been averted if contributors actually set their targets and investment managers optimize from forecasts of future prices using trend analysis.
文摘This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search process. The former is essential to enhance the realism of the classical mean-variance model proposed by Harry Markowitz, since portfolio managers often face a number of realistic constraints arising from business and industry regulations, while the latter reflects the fact that portfolio managers are ultimately interested in specific regions or points along the efficient frontier during the actual execution of their investment orders. For the former, this paper proposes an order-based representation that can be easily extended to handle various realistic constraints like floor and ceiling constraints and cardinality constraint. An experimental study, based on benchmark problems obtained from the OR-library, demonstrates its capability to attain a better approximation of the efficient frontier in terms of proximity and diversity with respect to other conventional representations. The experimental results also illustrated its viability and practicality in handling the various realistic constraints. A simple strategy to incorporate preferences into the multi-objective optimization process is highlighted and the experimental study demonstrates its capability in driving the evolutionary search towards specific regions of the efficient frontier.
文摘Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk factor in drawing up an efficient frontier and the optimal portfolio. Since semi-variance offers a better estimation of the actual risk portfolio, it was used as a measure to approximate the risk of investment in this work. The optimal portfolio selection is one of the non-deterministic polynomial(NP)-hard problems that have not been presented in an exact algorithm, which can solve this problem in a polynomial time. Meta-heuristic algorithms are usually used to solve such problems. A novel hybrid harmony search and artificial bee colony algorithm and its application were introduced in order to draw efficient frontier portfolios. Computational results show that this algorithm is more successful than the harmony search method and genetic algorithm. In addition, it is more accurate in finding optimal solutions at all levels of risk and return.
基金Project supported by the National Natural Science Foundation of China （Grant Nos.70518001. 70671064）
文摘In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.
文摘Tail risk is a classic topic in stressed portfolio optimization to treat unprecedented risks,while the traditional mean–variance approach may fail to perform well.This study proposes an innovative semiparametric method consisting of two modeling components:the nonparametric estimation and copula method for each marginal distribution of the portfolio and their joint distribution,respectively.We then focus on the optimal weights of the stressed portfolio and its optimal scale beyond the Gaussian restriction.Empirical studies include statistical estimation for the semiparametric method,risk measure minimization for optimal weights,and value measure maximization for the optimal scale to enlarge the investment.From the outputs of short-term and long-term data analysis,optimal stressed portfolios demonstrate the advantages of model flexibility to account for tail risk over the traditional mean–variance method.
文摘In this study,we analyze three portfolio selection strategies for loss-averse investors:semi-variance,conditional value-at-risk,and a combination of both risk measures.Moreover,we propose a novel version of the non-dominated sorting genetic algorithm II and of the strength Pareto evolutionary algorithm 2 to tackle this optimization problem.The effectiveness of these algorithms is compared with two alternatives from the literature from five publicly available datasets.The computational results indicate that the proposed algorithms in this study outperform the others for all the examined performance metrics.Moreover,they are able to approximate the Pareto front even in cases in which all the other approaches fail.
基金supported by the National Natural Science Foundation of China under Grants No.71801213 and No.71988101the National Center for Mathematics and Interdisciplinary Sciences,CAS.
文摘The emergence and growing popularity of Bitcoins have attracted the attention of the financial world.However,few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market.It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio.In this study,we propose a novel ensemble portfolio optimization(NEPO)framework utilized for broad commodity assets,which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation.Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors.In addition,it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies,commodity funds,and indices.
文摘This article presents a semi-Markov process based approach to optimally select a portfolio consisting of credit risky bonds.The criteria to optimize the credit portfolio is based on l_(∞)-norm risk measure and the proposed optimization model is formulated as a linear programming problem.The input parameters to the optimization model are rate of returns of bonds which are obtained using credit ratings assuming that credit ratings of bonds follow a semi-Markov process.Modeling credit ratings by semi-Markov processes has several advantages over Markov chain models,i.e.,it addresses the ageing effect present in the credit rating dynamics.The transition probability matrices generated by semi-Markov process and initial credit ratings are used to generate rate of returns of bonds.The empirical performance of the proposed model is analyzed using the real data.Further,comparison of the proposed approach with the Markov chain approach is performed by obtaining the efficient frontiers for the two models.
基金financial support from National Science and Technology Major Project of the Ministry of Science and Technology of China"Research on Investment estimation tools and economic appraisal system integration and development"(2011ZX05030-006-04)
文摘For oil company decision-makers,the principal concern is how to allocate their limited resources into the most valuable opportunities.Recently a new management philosophy,＂Beyond NPV＂,has received more and more international attention.Economists and senior executives are seeking effective alternative analysis approaches for traditional technical and economic evaluation methods.The improved portfolio optimization model presented in this article represents an applicable technique beyond NPV for doing capital budgeting.In this proposed model,not only can oil company executives achieve trade-offs between returns and risks to their risk tolerance,but they can also employ an ＂operational premium＂ to distinguish their ability to improve the performance of the underlying projects.A simulation study based on 19 overseas upstream assets owned by a large oil company in China is conducted to compare optimized utility with non-optimized utility.The simulation results show that the petroleum optimization model including ＂operational premium＂ is more in line with the rational investors＇ demand.
基金supported by the National Natural Science Foundation of China (Grant Nos.70671064,70518001)
文摘In this paper, the discrete mean-variance model is considered for portfolio selection under concave transaction costs. By using the Cholesky decomposition technique, the convariance matrix to obtain a separable mixed integer nonlinear optimization problem is decomposed. A brand-and-bound algorithm based on Lagrangian relaxation is then proposed. Computational results are reported for test problems with the data randomly generated and those from the US stock market.
文摘In the new competitive environment of the electricity market, risk analysis is a powerful tool to guide investors under both contract uncertainties and energy prices of the spot market. Moreover, simulation of spot price scenarios and evaluation of energy contracts performance, are also necessary to the decision maker, and in particular to the trader to foresee opportunities and possible threats in the trading activity. In this context, computational systems that allow what-if analysis, involving simulation of spot price, contract portfolio optimization and risk evaluation are rather important. This paper proposes a decision support system not only for solving the problem of contracts portfolio optimization, by using linear programming, but also to execute risks analysis of the contracts portfolio performance, with VaR and CVaR metrics. Realistic tests have demonstrated the efficiency of this system.
文摘This study investigates the return dynamics,volatility structure,and risk characteristics of five representative S&P 500 stocks:Johnson&Johnson,Microsoft,NVIDIA,Coca-Cola,and Home Depot,using ARMA-GARCH models.Descriptive statistics and diagnostic tests confirm non-normality,negative skewness,fat tails,and volatility clustering,providing strong justification for conditional mean-variance modelling.Optimal model specifications are selected via the Bayesian Information Criterion,with EGARCH frameworks generally outperforming alternative GARCH variants in capturing asymmetric volatility responses.Rolling-window forecasts for 2024Q1 show that the models generate stable and reliable volatility predictions for low-volatility stocks(JNJ,KO),while performance is weaker for highly volatile stocks(NVDA),highlighting structural limitations under extreme market shifts.To evaluate risk management implications,one percent Value-at-Risk and expected shortfall were computed and backtested.Results indicated conservative tail-risk forecasts,with violation rates well within acceptable thresholds.Portfolio applications are further explored by constructing the Global Minimum Variance Portfolio(GMVP)and the Maximum Sharpe Ratio(Max SR)portfolio using rolling covariance estimates.Out-of-sample backtesting demonstrated that the GMVP delivered low volatility but modest returns,whereas the Max SR portfolio achieved significantly higher performance,consistent with the risk-return trade-off.Overall,the findings confirm that ARMA-GARCH models are effective tools for modelling conditional volatility and informing dynamic asset allocation.However,their limited adaptability to jump risk and nonlinear structural breaks underscores the need for more advanced modelling approaches in high-volatility environments.
基金supported by National Natural Science Foundation of China(71171003)Anhui Natural Science Foundation(10040606003)Anhui Natural Science Foundation of Universities(KJ2012B019,KJ2013B023)
文摘This article is concerned with a class of control systems with Markovian switching, in which an It5 formula for Markov-modulated processes is derived. Moreover, an optimal control law satisfying the generalized Hamilton-Jacobi-Bellman （HJB） equation with Markovian switching is characterized. Then, through the generalized HJB equation, we study an optimal consumption and portfolio problem with the financial markets of Markovian switching and inflation. Thus, we deduce the optimal policies and show that a modified Mutual Fund Theorem consisting of three funds holds. Finally, for the CRRA utility function, we explicitly give the optimal consumption and portfolio policies. Numerical examples are included to illustrate the obtained results.
基金supported by the National Natural Science Foundation of China[Grant Number 61902349].
文摘Rule-based portfolio construction strategies are rising as investmentdemand grows, and smart beta strategies are becoming a trend amonginstitutional investors. Smart beta strategies have high transparency, lowmanagement costs, and better long-term performance, but are at the risk ofsevere short-term declines due to a lack of Risk Control tools. Although thereare some methods to use historical volatility for Risk Control, it is still difficultto adapt to the rapid switch of market styles. How to strengthen the RiskControl management of the portfolio while maintaining the original advantagesof smart beta has become a new issue of concern in the industry. Thispaper demonstrates the scientific validity of using a probability prediction forposition optimization through an optimization theory and proposes a novelnatural gradient boosting (NGBoost)-based portfolio optimization method,which predicts stock prices and their probability distributions based on non-Bayesian methods and maximizes the Sharpe ratio expectation of positionoptimization. This paper validates the effectiveness and practicality of themodel by using the Chinese stock market, and the experimental results showthat the proposed method in this paper can reduce the volatility by 0.08 andincrease the expected portfolio cumulative return (reaching a maximum of67.1%) compared with the mainstream methods in the industry.
基金Supported by the NNSF of China (10571141) the Key Project of the NNSF of China (70531030).
文摘In order to study the effect of different risk measures on the efficient portfolios （fron- tier） while properly describing the characteristic of return distributions in the stock market, it is assumed in this paper that the joint return distribution of risky assets obeys the multivariate t-distribution. Under the mean-risk analysis framework, the interrelationship of efficient portfolios （frontier） based on risk measures such as variance, value at risk （VaR）, and expected shortfall （ES） is analyzed and compared. It is proved that, when there is no riskless asset in the market, the efficient frontier under VaR or ES is a subset of the mean-variance （MV） efficient frontier, and the efficient portfolios under VaR or ES are also MV efficient; when there exists a riskless asset in the market, a portfolio is MV efficient if and only if it is a VaR or ES efficient portfolio. The obtained results generalize relevant conclusions about investment theory, and can better guide investors to make their investment decision.
文摘This paper investigates an optimal investment strategy on consumption and portfolio problem, in which the investor must withdraw funds continuously at a given rate. By analyzing the evolving process of wealth, we give the definition of safe-region for investment. Moreover, in order to obtain the target wealth as quickly as possible, using Bellman dynamic programming principle, we get the optimal investment strategy and corresponding necessary expected time. At last we give some numerical computations for a set of different parameters.
文摘The high-frequency futures market exhibits high volatility and frequent institutional changes,which poses significant challenges for portfolio optimization.Traditional techniques such as the mean-variance model and risk equalization tend to show decreased returns and increased risks in such non-stationary markets.To address this challenge,an online portfolio optimization framework based on meta-learning was proposed.This framework incorporates cross-market and cross-period experiences into parameter adjustments,enabling it to quickly adapt to new institutional environments.Minute-level futures data from the Chicago Mercantile Exchange,New York Mercantile Exchange,and Shanghai Futures Exchange from 2019 to 2024 were used,and the market states were labeled using the Markov switching model through the rolling window method.Comparative experiments were conducted with mean-variance optimization,risk equalization,and deep reinforcement learning benchmarks.Empirical results show that the proposed framework outperforms the benchmarks in terms of excess returns,Sharpe ratio,and maximum drawdown in most cases,and has a faster convergence speed and stronger generalization ability in high-volatility institutional environments.The conclusion drawn from these results is that meta-learning is an effective method for solving portfolio optimization problems in non-stationary markets,providing theoretical support and empirical contributions for highfrequency quantitative trading and asset pricing research.
文摘Investing in cryptocurrencies is progressively becoming a norm;however,these assets are excessively volatile and often decrease or increase in value instantly.Thus,rational investors holding cryptocurrencies for extended periods firmly search for assets that can diversify their risk,preferably with assets other than cryptocurrencies.In this study,we consider the two most studied cryptocurrencies with the highest capitalization and trading volume/value,namely Bitcoin and Ethereum.Specifically,we examine whether high-performing leading US tech stocks(Facebook,Amazon,Apple,Netflix,Google[FAANG])can provide any diversification benefits to cryptocurrency investors.To do so,we employ dynamic conditional correlation(DCC),asymmetric DCC,time-varying parameter vector autoregression-based connectedness measures,dynamic correlation-based hedge and safe-haven regression analyses,portfolio optimization and hedging strategies,time-and frequency-based wavelet coherence,and high-frequency 10-min intraday data from January 1,2018 to January 31,2023.We find that FAANG stocks can be considered(at least weak)safe havens for Bitcoin and Ethereum during the sample period.Our subperiod analyses reveal that the safehaven role of FAANG stocks,specifically for Bitcoin,has noticeably increased.While the safe-haven property of Facebook is the most promising,for Netflix it is blurred between a weak–safe-haven and a hedge.Our findings may help investors,policymakers,and academicians to invest in cryptocurrencies,formulate relevant investment guidelines,and extend the literature on cryptocurrencies,respectively.
