Application of Particle Swarm Optimization in Optimal Asset Allocation

Optimal Asset Location and Allocation with Taxable and Tax-Deferred Investing

Optimal Asset Allocation deals with how to divide the investor's wealth across some asset-classes in order to maximize the investor's gain. We consider the Optimal Asset Allocation in a multi-period investment settings: Optimal Dynamic Asset Allocation provides the (optimal) re-balancing policy to accomplish some investment's criteria. Given a sequence of target sets, which represent the portfolio specifications at each re-balancing time, an optimal portfolio allocation is synthesized for maximizing the joint probability for the portfolio to fulfill the target sets requirements. The approach pursued is based on Dynamic Programming. The optimal solution is shown to conditionally depend on the portfolio realization, thus providing a practical scheme for the dynamic portfolio re-balancing. Finally some case studies are given to show the proposed methodology.

Financial planners and advisors increasingly recognize that human capital must be taken into account when building optimal portfolios for individual investors. But human capital is not simply another pre-endowed asset class; it contains a unique mortality risk in the form of the loss of future income and wages in the event of the wage earner's death. Life insurance hedges this mortality risk, so human capital affects both optimal asset allocation and demand for life insurance. Yet, historically, asset allocation and life insurance decisions have been analyzed separately. This article develops a unified framework based on human capital that enables individual investors to make these decisions jointly.

This paper focuses on asset allocation decisions of life insurance companies in emerging markets. Mature market insurers allocate only a small fraction of their assets to emerging markets because of regulatory constraints, rating pressures, and currency risk. However, global insurers invest directly in emerging markets by setting up subsidiaries rather than through portfolio investment, and this trend is increasing. Local insurers largely remain captive investors of local instruments and provide stability to the domestic securities market. The regulatory regime and the liquidity and depth of local markets play an important role in asset allocation decisions of insurers. Insurance companies are increasingly adopting asset liability management and risk control measures. However, insufficiently developed local markets and regulatory interventions on the liabilities side often limit optimal asset allocation.

The current structural low-interest environment is encouraging institutional investors to rethink their asset allocation strategies and increase their exposure towards alternative asset classes, such as real estate and infrastructure. This is particularly the case for insurance companies, which come under pressure to reduce their investment in currently low-yielding government bonds, given the high interest rate guarantees associated with obligations from existing life insurance contracts. As a consequence, insurance companies may be forced to rearrange their assets appropriately. However, the forthcoming Solvency II Directive could counteract this scenario. After Solvency II comes into effect, insurance companies will be subject to higher capital requirements aimed at ensuring insurance-protection even in the case of macroeconomic shocks. The Solvency Capital Requirements (SCR) varies by asset class and is determined by the Solvency II standard formula, which also determines the methodology used to aggregate the respective required equity position. Therefore, it may become necessary for insurers to minimize the SCR by means of their asset allocation, depending on their capitalization, profitability and the general competitive dynamics. If the results of such an optimization are not in accordance with those of conventional optimal asset allocation, Solvency II will lead to inefficient capital allocation in practice. As a result, the portfolio risk would increase, which contradicts the original purpose of the regulation. Furthermore, changing investment behavior from insurers might also have direct effects on the pricing and the product range offered on the capital markets, e.g. for real estate investments.We therefore analyze, whether the Solvency II standard formula could indeed cause the abovementioned incentive incompatibility with respect to the asset allocation process. The main focus lies on the potential shift of direct real estate and direct infrastructure weights within the portfolios of insurers under the Solvency II regime.

Debiased Expert Opinions in Continuous Time Asset Allocation: Supplementary Material

Optimal dynamic asset allocation of pension fund in mortality and salary risks framework

This paper explores the use of a combined approach of Markowitz portfolio theory and Monte Carlo simulations for optimizing asset allocation and risk management in financial markets. First, we introduce the portfolio optimization framework of the Markowitz theory, focusing on how to construct an efficient portfolio by minimizing risk and maximizing expected return. Next, by integrating the Monte Carlo simulation method, the paper simulates asset returns and risk fluctuations under different market scenarios, aiming to assess the potential risks of various asset portfolios. To make the model more realistic, we assume an efficient market, rational investors, and neglect transaction costs and market frictions. Additionally, we consider the covariance matrix between assets, assuming its stability during the analysis period. Finally, based on a single-period investment model, the paper proposes an optimized asset allocation strategy, offering a more scientifically grounded decision-making approach for investors. The results show that combining these two methods can effectively improve the accuracy of asset allocation and reduce the overall risk of the portfolio. This study provides a new perspective and practical solution for financial risk assessment and asset management.

This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges.

The background of this research is related to the boycott of companies that support Israel, which affects the composition of stock portfolios on the American Stock Exchange. The focus of this research is on key companies such as Apple (AAPL), Starbucks (SBUX), Amazon (AMZN), Google (GOOGL), and McDonald's (MCD). The problem to be solved is the identification of changes in optimal asset allocation in investment portfolios before and after the boycott. Using a mean-variance portfolio optimization model, historical stock price data is analyzed to model the transformation of portfolio composition as well as the associated risk level. The purpose of this study is to provide an in-depth understanding of the impact of the boycott on the investment portfolio structure of related companies on the American Stock Exchange. The result of this research is that there is a change in the allocation of assets held against stocks before the boycott and after the boycott. This research is expected to provide useful insights for investors, financial analysts, and other stakeholders in managing their investment portfolios, especially in anticipating and adjusting investment strategies amid dynamic changes in the stock market.

Canner, Mankiw and Weil (1997) point out that the popular financial advisors on portfolio allocation among cash, bonds and stocks appear not to follow the mutual-fund separation theorem and call the inconsistency between separation theorem and popular financial advice ”an asset allocation puzzle.” For solving the asset allocation puzzle, we provide an analysis of the optimal dynamic asset allocation strategy for a long-horizon investor who has nontraded assets under an economic environment with stochastic investment opportunities and incomplete financial markets. We propose another distinguishing hedging component of the dynamic asset allocation for the stock index fund: the human capital hedging component, the hedging demand which characterizes the demand arising from the desire to hedge against changes in the labor income in contrast to Merton (1973). When we incorporate nontraded assets with intertemporal hedging, we can solve the asset allocation puzzle successfully.

Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring (SBM) for real-time assessment of GNSS signal quality. However, proximity in the frequencies of LEO navigation signals and SBM can lead to significant interference, necessitating isolated transmission and reception. This separation requires that SBM and navigation signal transmission be carried out by different satellites within the constellation, thus demanding a strategic allocation of satellite resources. Given the vast number of satellites and their rapid movement, the visibility among LEO, medium-earth-orbit (MEO), and geostationary orbit (GEO) satellites is highly dynamic, presenting substantial challenges in resource allocation due to the computational intensity involved. Therefore, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. In this algorithm, a network model for the allocation of navigation resources in LEO constellations is initially established. Under the constraints of visibility time windows and onboard transmission and reception isolation, the objective is set to minimize the number of LEO satellites used while achieving effective navigation signal transmission and SBM. The constraints of resource allocation and the mathematical expression of the optimization objective are derived. A dynamic programming approach is then employed to determine the optimal resource allocation scheme. Analytical results demonstrate that compared to Greedy and Divide-and-Conquer algorithms, this algorithm achieves the highest resource utilization rate and the lowest computational complexity, making it highly valuable for future resource allocation in LEO constellations.

The widely used mean-variance criteria is actually not the optimal solution for asset allocation as the joint distribution of asset returns are distributed in asymmetric ways rather than in the assumed normal distribution. It is a computationally challenging task to model the asymmetries and skewness of joint distributions of returns in high dimensional space due to their own complicated structural complexities. This paper proposes to use a new form of canonical vine to produce the complex joint distribution of asset returns. Then, we use the utility function of Constant Relative Risk Aversion to determine the optimal allocation of the assets. The importance of using the asymmetries information is assessed by comparing the performance of a portfolio based on the mean-variance criteria and that of a portfolio based on the new canonical vine. The results show that the investors using the forecasts of these asymmetries can make better portfolio decisions than those who ignore the asymmetries information.KeywordsCanonical VineMean Variance CriterionFinancial Return

Multi-constellation integrated navigation receiver will increase the number of visible satellites and improve positioning accuracy of the receiver. If all-in-view satellites are used for receiver positioning, the computational burden of the receiver will be increased. The traditional satellite selection algorithm is traversal algorithm; however, as the number of visible satellites increases, the traversal algorithm exists huge computation. In order to the problem, the improved particle swarm optimization (PSO) is given for satellite selection, in the proposed algorithm, each satellite subset is considered a particle without mass in search space, and the selected objective function is the geometric dilution of precision (GDOP). Particles update their position based on the proposed algorithm model. Moreover, the optimal satellite subset and the corresponding GDOP value are obtained. The performance of the algorithms is compared based on real navigation data. The simulation results show that proposed algorithm can improve satellite selection speed, and the satellite selection accuracy is better than that of the basic PSO satellite selection algorithm.

International asset allocation with time varying risk: an analysis and implementation