Optimal Investment Allocation Research Articles

Research on cryptocurrency portfolio based on Markowitz model

In recent years, the booming growth of the cryptocurrency market has garnered significant attention from a diverse range of investors with the overall market capitalization experiencing continuous expansion. This surge in popularity, however, is accompanied by challenges arising from the market's inherent high volatility and complexity. The increasing diversity of available assets presents investors with a complex landscape to navigate. The quest to build an optimal investment portfolio has become a focal point, particularly as market participants seek strategies that not only capitalize on the market's potential for substantial returns but also effectively manage the inherent risks. In this paper, nine cryptocurrencies have been chosen to construct the portfolios, which include BTC, Ether, Tether, Binance, ripple, USDC, ADA, DOGE and TRX. The data selected comprises the daily price of the above assets from November 1, 2020, to October 9, 2023. This paper presents a comprehensive framework for constructing and optimizing cryptocurrency investment portfolios, by employing the Markowitz model and Sharpe ratio analysis. We examined these nine selected digital currencies to determine the optimal investment allocation. Our findings suggest that the highest sharpe ratio is achieved when each investment cryptocurrencies (according to the order above) allocates 0, 0.003, 0.762, 0.038, 0.005, 0.172, 0, 0.014 and 0.007, respectively, to each cryptocurrency. And the lower risk portfolio is obtained when each cryptocurrencies accounted for 2.740e-20, 6.315e-20, 0.5, 0, 0.001, 0.499, 0, 1.419e-04 and 4.210e-20, respectively. This study contributes to the sustainable development of the cryptocurrency market by providing valuable insights for financial institutions, policymakers, and other stakeholders. However, limitations of this research come from the small number of selected digital currencies and the exclusion of risk-free assets such as government bonds. Future research should consider expanding the sample size and incorporating risk-free rates to enhance the applicability and reliability of investment strategies. In conclusion, this research offers investors a practical framework to navigate the complexities of the cryptocurrency market and optimize their investment decisions for long-term value appreciation.

Portfolio Theory and COVID-19: A Quantitative Analysis of Investment Products

This study examines the analysis of different investment products after the COVID-19 pandemic, using portfolio theory, with the aim of exploring an optimal investment allocation, assuming that investors are rational and that there are no other contingencies that could affect the upward or downward trend of the investment products. Risk may be quantified and varies in practice. Investment risks are higher for financial instruments with higher volatility, but potential gains and losses are also more significant. Therefore, a person must take more risks to get a larger return on investment, and taking more risks should be rewarded proportionally with higher returns. This research can create a preliminary investment portfolio by thoroughly calculating and analyzing the real situation of various investment goods and by assessing various assets using the portfolio theory.

The Long Shadow of a Major Disaster: Modeled Dynamic Impacts of the Hypothetical HayWired Earthquake on California’s Economy

We develop and apply a dynamic economic simulation model to analyze the multi-regional impacts of, and mechanisms of recovery from, a major disaster, the HayWired scenario — a hypothetical Magnitude 7.0 earthquake affecting California’s San Francisco Bay Area. The model integrates loss pathways: capital stock damage, labor supply shocks due to short-term population displacement and longer-run out-migration from damaged areas, and the exacerbating effects of damage to transportation infrastructure capital, as well as various aspects of static and dynamic economic resilience. With input substitution-based static inherent resilience and dynamic resilience in the form of optimal intertemporal and spatial investment allocation, gross output losses range from 0.5 percent to 6 percent across regions, and welfare losses are 0.4 percent statewide but can be ten times as large in hardest-hit areas. Large-scale reconstruction investment is supported by substantial interregional transfers of resources through intra-state trade. Increased output via firms engaging in the key adaptive resilience tactic of production recapture can alleviate a substantial fraction of losses—but only if upstream and downstream barriers to recovery can be lowered quickly.

Public overspending in higher education

AbstractWe study the trade‐off between governmental investments in pretertiary and tertiary education from an efficiency point of view. We develop a model comprising agents with different incomes and abilities, public and private schools, and public universities that select applicants based on an admission exam. Reallocating governmental resources from tertiary to pretertiary education may positively affect aggregate production and human capital if some conditions are satisfied. For instance, in an economy with a high proportion of credit‐constrained students, a reallocation of expenditure toward public schools benefits many students, compensating for the negative effect of a decrease in public university investments. We also quantitatively investigate the optimal allocation of public investment between pretertiary and tertiary education, and we find that a 10% increase in productivity of public investments in pretertiary education could increase the optimal GDP between 2.1% and 3%.

Understanding the differences between single‐ and multiobjective optimization for the conservation of multiple species

AbstractThe purpose of this study is to understand how solutions from single‐ and multiobjective optimization for the conservation of multiple species are different and what impacts these differences. We identify optimal conservation investment allocations maximizing expected species' habitat ranges for multiple pairs of species using two approaches in the central and southern Appalachian region. We find that disparities between the two approaches are affected by differences in the involved species' expected habitat ranges (i.e., contrasting and similar) and their correlation pattern (i.e., positive, negative, and insignificant). Using a single metric by aggregating species' habitats for multiple species to carry out single‐objective optimization is shown to favor the species with a larger habitat distribution more if the involved species' expected habitat distributions are negatively correlated and their distribution difference is larger. Framing multiple metrics of species' habitats separately using multiobjective optimization for the same set of multiple species, in contrast, does not show such a drastic disparity.

Point-to-point stochastic control of a self-financing portfolio

This paper aims at computing optimal control policies to drive a self-financing portfolio of financial assets from a given initial financial state to a final state in a given time horizon such that for the first case, the functional portfolio financial risk is minimized and, for the second case, the functional portfolio profit is maximized. The optimal control policies are the optimal investment allocation processes, the optimal state process is the optimal investor’s wealth process, also called the system response to the input control and is obtained by solving the combined system of differential equations formed by the state and costate system of differential equations derived and extracted from Pontryagin’s Minimum Principle. Computational simulations are provided to show the effectiveness and the reliability of the approach.

Spatial and taxonomic diversification for conservation investment under uncertainty

SummaryConservation organizations often need to develop risk-diversification strategies that identify not just what species to protect but also where to protect them. The objective of this research is to identify optimal conservation investment allocations for both target sites and species under conditions of uncertainty. We develop a two-step approach using modern portfolio theory (MPT) to estimate percentages of conservation investment (referred to as ‘portfolio weights’) for counties and taxonomic groups in the central and southern Appalachian region under climate and market uncertainties. The portfolio weights across the counties and taxonomic groups from the two steps entail both spatial and taxonomic diversification strategies. Conservation decisions that allow for selecting sites for risk diversification fit the purpose of the first step. Likewise, conservation investments that benefit the biodiversity of particular taxonomic groups for the selected sites are made based on the relative importance of diversifying risk among species in a given area, fitting the purpose of the second step. The two-step MPT approach as a whole allows the greatest flexibility on where and what to protect for conservation investment under uncertainty, and thus would be applicable for the distribution of general conservation funds without predisposition towards protecting either specific sites or species.

A cost-effective optimization model of safety investment allocation for risk reduction of domino effects

In general, the allocation strategy of safety measures in the domino effect is based on technical standards or simplistic rules of thumb, often leading to a subjective and uncertain decision-making process. In this work, a cost-effective risk reduction optimization model is proposed to objectively allocate the safety investment concerning domino effects in view of the risk and economic cost. Different from previous safety barrier performance assessments, we directly relate risk to economic cost through mathematical functions to trade off the economic effectiveness and domino effect risk under lower uncertainty. Not only mitigating measures, but also preventive measures are considered for domino risk reduction in the decision-making process of safety investment allocation in practical applications. The results from the case study demonstrate that the proposed model can help decision makers to identify the optimal safety investment among different investments and find the optimal investment allocation strategy given a specific economic investment in the worst-case domino scenario.

Optimal dynamic investment allocation on construction of intelligent transportation infrastructure and road maintenance with environmental costs

This paper presents the optimal dynamic investment allocation on intelligent transportation infrastructure construction and road maintenance with considering environmental costs. We propose a stochastic optimal control problem with uncertain traffic volume, aiming to minimize the expected system total cost, which consists of the fixed travel cost, the congestion delay cost, the congestion environmental cost and the road maintenance environmental cost. By adopting Hamilton-Jacobi-Bellman equation, we propose the optimal dynamic strategy for investment allocation, which is a feedback control and determined by two observable variables, i.e., traffic volume and road network capacity. We also conduct a case study of Beijing, China, for the proposed dynamic strategy. The numerical results demonstrate that our strategy can lower the congestion delay and related environmental cost significantly, which is enough to remedy the possible increase in the road maintenance environmental cost. Comparing with traditional strategies, the new dynamic strategy provides closely 100% of the benefit cases.

Seasonal change of flower sex ratio and pollinator dynamics in three reproductive ecotypes of protandrous plant

AbstractCimicifuga simplex has three genetically and ecologically distinct pollination morphs with different flowering phenology, flower sex expressions, and selfing rates. A previous study showed that strong protandry in hermaphroditic flowers of C. simplex causes there to be seasonal minority sexes; for example, unisexual female ramets are advantageous in the first half of the flowering season and bloom then (minority sex hypothesis). That study, however, did not distinguish among the three pollination morphs of C. simplex. We investigated seasonal sex ratio changes and pollinator environments of the three morphs to verify and expand the minority sex hypothesis. We investigated flowering phenology, pollinator quantity and quality, seasonal population sex ratio dynamics, and stamen/ovule ratios in hermaphroditic flowers. We also examined the seasonal female reproductive success of hermaphroditic flowers, and the effect of male flower excision on fruit set by morph II hermaphroditic flowers. Morph I (mainly hermaphroditic and female ramets) had high pollinator quality and quantity. Fruit set of hermaphroditic ramets was high throughout the flowering season. Morph II (hermaphroditic and andromonoecious ramets) had low pollinator quality, and few pollinators visited near the beginning and end of the season. Removal of male flowers led to a pollen limitation. Morph III (hermaphroditic ramets and a high selfing rate) had very low pollinator abundance throughout the flowering season, and male and female phases largely overlapped. The scarcity of male flowers in morph I is likely a result of the high pollinator quantity and quality. The high fruit set suggests that no pollen limitation existed. The low pollinator quality and quantity of the morph II population caused it to suffer from pollen limitation and may explain the absence of unisexual female ramets in this morph. The high selfing rate of morph III may be due to the extremely low pollinator abundance. Theoretical studies have indicated that the optimal investment allocation to male and female functions depends on whether a pollen limitation exists. In C. simplex, the observed relationships between pollinator environment and reproductive systems are consistent with these theoretical models.

The Analogue of Regional Economic Models in Quantum Calculus

In this paper, we derive a new formulation for an optimal investment allocation in N-regional economic model using quantum calculus analogue. This model is described as an optimal control model and formulated in both primal and dual models using quantum calculus formulation. This formulation is an extension of regional economic models. Also, the new formulation provides an exact optimal investment allocation. In addition, the classical regional economic model is obtained by choosing q=1. Furthermore, we formulate the primal and the dual regional economic models in quantum calculus. Moreover, we present a new version of the duality theorems for quantum calculus case. Finally, example is provided and solved using MATLAB. in order to show the given new results.

Computation of optimal investment allocations in a sequential portfolio optimisation

Orientation: This article is related to Financial Risk Management, Investment Management and Portfolio Optimisation.Research purpose: The aim is to compute optimal investment allocations from one period to another.Motivation of the study: Financial market systems are governed by random behaviours expressing the complexity of the economy and the politics. Risk Measure and Management are current and major issues for financial market operators and attract the attention of researchers who develop suitable tools and methods to describe and control risk. In this article, financial risk management is considered for an investor operating in the financial market.Research approach/design and method: This research developed Mathematical Models to describe the problem and Computational Simulations to compute, summarise the results and show their reliabilities.Main findings: The results are the investments allocations stored, some tables and the related computational simulations. By going from period one to another, one can notice from the graphs that the portfolio risk is decreasing and the portfolio profit increasing.Practical/managerial implications: The approach used in this article shows a way of solving rigorously any linearly constrained quadratic optimisation problem and any constrained nonlinear problem. It gives the ability of transforming judiciously certain linearly constrained nonlinear programming problems into sequences of linearly constrained quadratic problems and solving them efficiently.Contributions/value-add: This article developed Mathematical Models and Matlab Computer Optimisation Programs to give Computational Simulations. It wrote Computer Programs for a fifth-order autoregressive model to forecast asset profits.

Cybersecurity Investment Allocation for a Multi-Branch Firm: Modeling and Optimization

Network interconnection and information sharing among firms and their departments expose them to cybersecurity breaches. Traditional cybersecurity studies have paid little attention to the reallocation of security investment within firms. This paper proposes a mathematical model for optimal allocation of cybersecurity investment among headquarters and branches with budget constraints. The differences in size of information sets and system interconnection have been taken into account. The responses of optimal allocation to internal and external factors, such as the portion of branch information set, the propagation probability, the budget constraints, and the intrinsic vulnerability, have been studied in deep both theoretically and numerically. Analysis results indicate that the group will give priority to protecting headquarters when the total budget is small and intrinsic vulnerability is high. The security investment allocated to each branch increases with budget, propagation probability and portion of information set, but never exceeds 1 / ( n + 1 ) of total budget. Numerical simulations also verify that security information sharing among headquarters and branches can help improve the efficiency of security investment in the whole system. Furthermore, the findings of this paper will draw attention to the reallocation of cybersecurity investment within a business group and help cybersecurity managers to develop investment allocation strategies and policies.

Minimal Investment Risk with Cost and Return Constraints: A Replica Analysis

Previous studies into the budget constraint of portfolio optimization problems based on statistical mechanical informatics have not considered that the purchase cost per unit of each asset is distinct. Moreover, the fact that the optimal investment allocation differs depending on the size of investable funds has also been neglected. In this paper, we approach the problem of investment risk minimization using replica analysis. This problem imposes cost and return constraints. We also derive the macroscopic theory indicated by the optimal solution and confirm the validity of our proposed method through numerical experiments.

Automated Assessment of the Value of Private Equity Funds on the Basis of an Optimization Software Package

With the development of private equity market in Russia, the creation of a private equity fund (PEF) optimizing the distribution of investment resources in it remains a relevant task. The object of the study in this paper is PEF, the subject is an automated assessment of the value of PEF on the basis of the author’s economic-mathematical model (EMM). The aim of this paper is to study the possibility of using the optimization software for this assessment and its application to the real PEF portfolio of projects. The optimization-based economic- mathematical model underlying the calculations makes it possible to identify the economic potential of the fund’s portfolio (in the form of its net present value), which makes it possible to optimally allocate investments in projects of the real sector of the economy, taking into account the state of the regulatory environment and the interests of investors. As methods of the study in this paper are used: economic-mathematical modeling; numerical experiment using an optimization financial-analytical package of applied programs. The results of the research can be used by fund managers to solve operational, investment and financial problems from the standpoint of increasing the value of funds and optimal investment allocation in various sectors of the economy.

Combining carbon mitigation and climate adaptation goals for buildings exposed to hurricane risks

As climate risks increase, there is a challenge of combining the goals of carbon mitigation and climate adaptation into building designs. These two goals are often misaligned because adaptation measures use additional materials and equipment, which can increase greenhouse gas (GHG) emissions. This phenomenon means that building design involves tradeoffs between enhanced structural resilience and reduced GHG emissions. This paper seeks to identify the optimal investment allocation mechanisms between carbon mitigation and climate adaptation measures for the design of buildings in hurricane-prone regions. A dynamic decision-making model is developed to maximize individual investors’ expected payoffs over a building's lifetime. The model is based on the damage evaluation of non-stationary hurricane occurrence and building emission performance under different mitigation scenarios. The results reveal a transition from long-advocated low-carbon investments to risk-oriented portfolios for building retrofits. A case study on Anne Arundel County, MD, for which a “60-40” resilience/abatement portfolio is recommended, shows the value of enhancing structural resilience. Discretion on the accuracy of insurance premium discounts is needed to support risk mitigation efforts. Meanwhile, subsidies for emission abatements are recommended to accommodate existing emission trading schemes and building property values.

Creating Optimal Portfolio and the Efficient Frontier Using Microsoft Excel ®

Portfolio managers and investors strive to achieve the best possible trade-off between risk and return, and one of the tools they use is constructing mean-variance efficient portfolios. Finance students learn about optimal portfolios and efficient frontiers, though it is difficult to replicate them unless they have access to sophisticated software. This paper develops a teaching module that uses Microsoft Excel® to create mean-variance portfolios and traces out the efficient frontier using real-world data. In the process, the students learn to determine optimal investment allocations in a portfolio, select the optimum investment portfolio given investor’s objectives and preferences and learn about factors that influence different asset allocations. For multiple assets (N>3), the paper uses Matrix algebra in Excel®. The paper enables students and investors to learn how to construct real-world mean-variance efficient portfolios using Excel®.

On the task of allocating investment to facilities preventing unauthorized movement of road vehicles across level crossings for various statistical criteria

Aim. Railway transportation is affected by a whole range of transportation incidents, both related to rolling stock, i. e. vehicle-to-vehicle collisions, derailments, broken cast parts of bogies, etc. , and infrastructure, i. e. broken rail, fires at railway stations and terminals, broken catenary, etc. Among the above incidents, collisions at level crossings are the most likely to cause a public response, as collisions between trains and road vehicles often cause multiple deaths that are reported in national media, which entails significant reputational damage for JSC RZD. Additionally, collisions often cause derailment of vehicles, which may cause deaths and major environmental disasters, if dangerous chemical products are transported. Beside the reputational damage, collisions at level crossings cause significant expenditure related to the repair of damaged infrastructure and rolling stock, as well as damage caused by trains idling due to maintenance machines operation at the location of incident. That brings up the issue of optimal allocation of investment to facilities preventing unauthorized movement of road vehicles across level crossings (hereinafter referred to as protection systems). This problem is of relevance, as replacing level crossings with tunnels and viaducts is not going fast and does not imply the eventual elimination of all level crossing. Hence is the requirement for rational allocation of funds to the installation of protection systems over the extensive railway network. Given the above, the aim of this paper is to develop decision-making guidelines for the reduction of the number of transportation incidents in terms of statistical criteria, i. e. quantile and probabilistic. Methods . The paper uses methods of deterministic equivalent, of equivalent transformations, of the probability theory, of optimization . Results. The problem of maximizing the probability of no incidents is reduced to integer linear programming. For the problem of minimizing the maximum number of incidents guaranteed at the given level of dependability, a suboptimal solution of the initial problem of quantile optimization is suggested that is obtained by solving the integer linear programming problem through the replacement of binomially distributed random values with Poisson values. Conclusions. The examined models not only allow developing an optimal strategy with guaranteed characteristics, but also demonstrate the sufficiency or insufficiency of the investment funds allocated to the improvement of level crossing safety. Decision-making must be ruled by the quantile criterion, as the probability of not a single incident occurring may seem to be high, yet the probability of one, two, three or more incidents occurring may be unacceptable. The quantile criterion does not have this disadvantage and allows evaluating the number of transportation incidents guaranteed at the specified level of dependability.

Risk management for optimal land use planning integrating ecosystem services values: A case study in Changsha, Middle China.

Land-use change has direct impact on ecosystem services and alters ecosystem services values (ESVs). Ecosystem services analysis is beneficial for land management and decisions. However, the application of ESVs for decision-making in land use decisions is scarce. In this paper, a method, integrating ESVs to balance future ecosystem-service benefit and risk, is developed to optimize investment in land for ecological conservation in land use planning. Using ecological conservation in land use planning in Changsha as an example, ESVs is regarded as the expected ecosystem-service benefit. And uncertainty of land use change is regarded as risk. This method can optimize allocation of investment in land to improve ecological benefit. The result shows that investment should be partial to Liuyang City to get higher benefit. The investment should also be shifted from Liuyang City to other regions to reduce risk. In practice, lower limit and upper limit for weight distribution, which affects optimal outcome and selection of investment allocation, should be set in investment. This method can reveal the optimal spatial allocation of investment to maximize the expected ecosystem-service benefit at a given level of risk or minimize risk at a given level of expected ecosystem-service benefit. Our results of optimal analyses highlight tradeoffs between future ecosystem-service benefit and uncertainty of land use change in land use decisions.

The effect of the development of large regions on the efficiency of the Russian economy

The article describes the structure of the Russian economy by sectors, which should be choosen as the regions with the highest gross regional product (GRP). The dynamics of the indicators characterizing the efficiency of the Russian economic development is considered. The impact of the economic sectors and structural shifts on the indicators is assessed. The production functions connecting the main indicators of economic development are constructed for each sector. To study the influence of the meso-level on the macro-level the models, enabling to connect the parameters of the equations of different levels and assess the effect of the structural shifts on the economic efficiency indices, are built. Based on the proposed approach the optimal structure of the Russian economy is defined with certain limitations. The obtained results were compared with the pre-reform period. The research results show that the influence of the structural shifts on the rates of economic growth was the highest in the middle of the 1990s comprising 2 – 3%. Then it dropped down to 0%, slightly increased to 0.2% in 2003–2004 and became negative after the year 2007. Moscow had the biggest impact on the efficiency. An average of 16% of the labor productivity growth in the Russian Federation was connected with the development of the Moscow economy, yet in some periods its value reached up to 40%. The impact of the regions on the elasticity in respect to the funds slightly differed. The impact of Moscow turned out to be unexpectedly high, reaching an average of 65% in the 1990s and 30% in the 2000s. The calculations showed a lack of the optimal investment allocation, which was probably due to the pre-reform policy and the policy of 2000s aimed at the preservation of certain production proportions and support of the weak regions.