Relevant bibliographies by topics / Optimal portfolio strategy

Academic literature on the topic 'Optimal portfolio strategy'

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Published: 11 March 2023

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Journal articles on the topic "Optimal portfolio strategy"

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Mroua, Mourad, and Fathi Abid. "Portfolio revision and optimal diversification strategy choices." International Journal of Managerial Finance 10, no. 4 (August 26, 2014): 537–64. http://dx.doi.org/10.1108/ijmf-07-2012-0085.

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Purpose – Since equity markets have a dynamic nature, the purpose of this paper is to investigate the performance of a revision procedure for domestic and international portfolios, and provides an empirical selection strategy for optimal diversification from an American investor's point of view. This paper considers the impact of estimation errors on the optimization processes in financial portfolios. Design/methodology/approach – This paper introduces the concept of portfolio resampling using Monte Carlo method. Statistical inferences methodology is applied to construct the sample acceptance regions and confidence regions for the resampled portfolios needing revision. Tracking error variance minimization (TEVM) problem is used to define the tracking error efficient frontiers (TEEF) referring to Roll (1992). This paper employs a computation method of the periodical after revision return performance level of the dynamic diversification strategies considering the transaction cost. Findings – The main finding is that the global portfolio diversification benefits exist for the domestic investors, in both the mean-variance and tracking error analysis. Through TEEF, the dynamic analysis indicates that domestic dynamic diversification outperforms international major and emerging diversification strategies. Portfolio revision appears to be of no systematic benefit. Depending on the revision of the weights of the assets in the portfolio and the transaction costs, the revision policy can negatively affect the performance of an investment strategy. Considering the transaction costs of portfolios revision, the results of the return performance computation suggest the dominance of the global and the international emerging markets diversification over all other strategies. Finally, an assessment between the return and the cost of the portfolios revision strategy is necessary. Originality/value – The innovation of this paper is to introduce a new concept of the dynamic portfolio management by considering the transaction costs. This paper investigates the performance of a revision procedure for domestic and international portfolios and provides an empirical selection strategy for optimal diversification. The originality of the idea consists on the application of a new statistical inferences methodology to define portfolios needing revision and the use of the TEVM algorithm to define the tracking error dynamic efficient frontiers.
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Kashif, Muhammad, Francesco Menoncin, and Iqbal Owadally. "Optimal portfolio and spending rules for endowment funds." Review of Quantitative Finance and Accounting 55, no. 2 (November 18, 2019): 671–93. http://dx.doi.org/10.1007/s11156-019-00856-x.

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AbstractWe investigate the role of different spending rules in a dynamic asset allocation model for university endowment funds. In particular, we consider the fixed consumption-wealth ratio (CW) rule and the hybrid rule which smoothes spending over time. We derive the optimal portfolios under these two strategies and compare them with a theoretically optimal (Merton) strategy. We show that the optimal portfolio with habit is less risky compared to the optimal portfolio without habit. A calibrated numerical analysis on U.S. data shows, similarly, that the optimal portfolio under the hybrid strategy is less risky than the optimal portfolios under both the CW and the classical Merton strategies, in typical market conditions. Our numerical analysis also shows that spending under the hybrid strategy is less volatile than the other strategies. Thus, endowments following the hybrid spending rule use asset allocation to protect spending. However, in terms of the endowment’s wealth, the hybrid strategy comparatively outperforms the conventional Merton and CW strategies when the market is highly volatile but under-performs them when there is strong stock market growth and low volatility. Overall, the hybrid strategy is effective in terms of stability of spending and intergenerational equity because, even if it allows short-term fluctuation in spending, it ensures greater stability in the long run.
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Li, Longqing. "Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets." Applied Economics and Finance 5, no. 5 (July 13, 2018): 1. http://dx.doi.org/10.11114/aef.v5i4.3376.

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Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.
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Li, Longqing. "Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets." Applied Economics and Finance 5, no. 5 (July 13, 2018): 1. http://dx.doi.org/10.11114/aef.v5i5.3376.

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Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.
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Nur Safitri, Indah Nur, Sudradjat Sudradjat, and Eman Lesmana. "STOCK PORTFOLIO ANALYSIS USING MARKOWITZ MODEL." International Journal of Quantitative Research and Modeling 1, no. 1 (February 2, 2020): 47–58. http://dx.doi.org/10.46336/ijqrm.v1i1.6.

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A common problem that often occurs in investment is the selection of the optimal portfolio according to the wishes of investors. This thesis ueds the Markowitz Model as a basis to formed a model to choose the optimal portfolio that provided the lowest risk. Efforts to minimize risk were carried out by conducting a diversification strategy. After the selection of several companies with the criteria of capitalization value and DER (Debt Equity Ratio), a combination of stocks is formed to form a portfolio. The formed portfolio was then analyzed to determine the optimal proportion of each stock. Using the Markowitz model, which is then solved by Non Linear Programming, an optimal portfolio is obtained with the proportion of each stock minimizing risk. In general, the results of this analysis indicate that portfolios with more stocks will produce lower risks compared to portfolios with fewer stocks, thus providing optimal diversification solutions, namely portfolios with members of five stocks with optimal risk of 0.886%.
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Demos, Guilherme, Thomas Pires, and Guilherme Valle Moura. "Rebalanceamento Endógeno para Portfólios de Variância Mínima." Brazilian Review of Finance 13, no. 4 (October 25, 2015): 544. http://dx.doi.org/10.12660/rbfin.v13n4.2015.49112.

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Investment managers often rebalance portfolios using it ad-hoc criteria due the trade-off between gains from updating optimal weight and costs incurred from changing the portfolio composition. A common solution for this stalemate is rebalancing the portfolio based on some exogenous criteria. By monitoring the optimal weights of the portfolio through control charts, the authors propose a portfolio rebalance strategy based solely on endogenous information. The optimal portfolio weights are thus monitored daily and if statistical significant changes are detected we either rebalance or not the portfolio thus avoiding transaction costs. The performance of the rebalancing strategy is then compared with different periodical strategies based on indicators such as Turnover and Sharpe-Ratio. Our results suggest that rebalancing strategies based on signals from control charts outperform those based solely on exogenous criteria, thus yielding economical gains to the investor.
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Gunning, Wade, and Gary van Vuuren. "Optimal omega-ratio portfolio performance constrained by tracking error." Investment Management and Financial Innovations 17, no. 3 (September 29, 2020): 263–80. http://dx.doi.org/10.21511/imfi.17(3).2020.20.

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The mean-variance framework coupled with the Sharpe ratio identifies optimal portfolios under the passive investment style. Optimal portfolio identification under active investment approaches, where performance is measured relative to a benchmark, is less well-known. Active portfolios subject to tracking error (TE) constraints lie on distorted elliptical frontiers in return/risk space. Identifying optimal active portfolios, however defined, have only recently begun to be explored. The Ω – ratio considers both down and upside portfolio potential. Recent work has established a technique to determine optimal Ω – ratio portfolios under the passive investment approach. The authors apply the identification of optimal Ω – ratio portfolios to the active arena (i.e., to portfolios constrained by a TE) and find that while passive managers should always invest in maximum Ω – ratio portfolios, active managers should first establish market conditions (which determine the sign of the main axis slope of the constant TE frontier). Maximum Sharpe ratio portfolios should be engaged when this slope is > 0 and maximum Ω – ratios when < 0.
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Maslov, Sergei, and Yi-Cheng Zhang. "Optimal Investment Strategy for Risky Assets." International Journal of Theoretical and Applied Finance 01, no. 03 (July 1998): 377–87. http://dx.doi.org/10.1142/s0219024998000217.

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We design an optimal strategy for investment in a portfolio of assets subject to a multiplicative Brownian motion. The strategy provides the maximal typical long-term growth rate of investor's capital. We determine the optimal fraction of capital that an investor should keep in risky assets as well as weights of different assets in an optimal portfolio. In this approach both average return and volatility of an asset are relevant indicators determining its optimal weight. Our results are particularly relevant for very risky assets when traditional continuous-time Gaussian portfolio theories are no longer applicable.
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Mercurio, Peter Joseph, Yuehua Wu, and Hong Xie. "Portfolio Optimization for Binary Options Based on Relative Entropy." Entropy 22, no. 7 (July 9, 2020): 752. http://dx.doi.org/10.3390/e22070752.

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The portfolio optimization problem generally refers to creating an investment portfolio or asset allocation that achieves an optimal balance of expected risk and return. These portfolio returns are traditionally assumed to be continuous random variables. In An Entropy-Based Approach to Portfolio Optimization, we introduced a novel non-parametric optimization method based on Shannon entropy, called return-entropy portfolio optimization (REPO), which offers a simple and fast optimization algorithm for assets with continuous returns. Here, in this paper, we would like to extend the REPO approach to the optimization problem for assets with discrete distributed returns, such as those from a Bernoulli distribution like binary options. Under a discrete probability distribution, portfolios of binary options can be viewed as repeated short-term investments with an optimal buy/sell strategy or general betting strategy. Upon the outcome of each contract, the portfolio incurs a profit (success) or loss (failure). This is similar to a series of gambling wagers. Portfolio selection under this setting can be formulated as a new optimization problem called discrete entropic portfolio optimization (DEPO). DEPO creates optimal portfolios for discrete return assets based on expected growth rate and relative entropy. We show how a portfolio of binary options provides an ideal general setting for this kind of portfolio selection. As an example we apply DEPO to a portfolio of short-term foreign exchange currency pair binary options from the NADEX exchange platform and show how it outperforms leading Kelly criterion strategies. We also provide an additional example of a gambling application using a portfolio of sports bets over the course of an NFL season and present the advantages of DEPO over competing Kelly criterion strategies.
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Zibri, Arben, and Agim Kukeli. "Does GMVP Strategy Reduce Risk? A Global Asset Approach." Journal of Applied Business Research (JABR) 30, no. 6 (October 29, 2014): 1873. http://dx.doi.org/10.19030/jabr.v30i6.8899.

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<p>This paper studies the out of sample risk reduction of global minimum variance portfolio. The analysis are drown from the discussions of Jagannathan and Ma (2003) regarding the risk reduction in US stock portfolios using portfolio constraints. We estimate the covariance matrix using the sample covariance matrix approach and derive optimal minimum variance portfolios considering upper/lower bounds and no restrictions. Results are shown under different revision frequency and transaction costs assumed. The data used are monthly indices of stocks, bonds, gold oil and spreads from 1996 until 2013. Unconstrained GMVPs result in the lowest out of sample variance, while unconstrained GMVPs of global bond portfolios performs the best in terms of risk reduction. Diversification through global asset classes result in a better strategy than international stock diversification regarding risk, as suggested by the literature.</p>
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Dissertations / Theses on the topic "Optimal portfolio strategy"

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廖智生 and Chi-sang Liu. "A study of optimal investment strategy for insurance portfolio." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31227636.

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Gabih, Abdelali, and Ralf Wunderlich. "Optimal portfolios with bounded shortfall risks." Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200401202.

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This paper considers dynamic optimal portfolio strategies of utility maximizing investors in the presence of risk constraints. In particular, we investigate the optimization problem with an additional constraint modeling bounded shortfall risk measured by Value at Risk or Expected Loss. Using the Black-Scholes model of a complete financial market and applying martingale methods we give analytic expressions for the optimal terminal wealth and the optimal portfolio strategies and present some numerical results.
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Gabih, Abdelali, Matthias Richter, and Ralf Wunderlich. "Dynamic optimal portfolios benchmarking the stock market." Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200501244.

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The paper investigates dynamic optimal portfolio strategies of utility maximizing portfolio managers in the presence of risk constraints. Especially we consider the risk, that the terminal wealth of the portfolio falls short of a certain benchmark level which is proportional to the stock price. This risk is measured by the Expected Utility Loss. We generalize the findings our previous papers to this case. Using the Black-Scholes model of a complete financial market and applying martingale methods, analytic expressions for the optimal terminal wealth and the optimal portfolio strategies are given. Numerical examples illustrate the analytic results.
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Li, Zejing [Verfasser], and N. [Akademischer Betreuer] Bäuerle. "Optimal Portfolios in Wishart Models and Effects of Discrete Rebalancing on Portfolio Distribution and Strategy Selection / Zejing Li. Betreuer: N. Bäuerle." Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/1033351482/34.

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Matamba, Itani. "Estimating the cost of deposit insurance for a commercial bank following an optimal investment strategy." University of Western Cape, 2020. http://hdl.handle.net/11394/7845.

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>Magister Scientiae - MSc
Commercial banks play a dominant role in facilitating the economic growth of a country by acting as an intermediary between the de cit spending unit (borrowers) and the surplus spending unit (lenders). In particular, they transform short-term deposits into medium and long-term loans. Due to their important role in the economy and the nancial system as a whole, commercial banks are subject to high regulation standards in most countries. According to an international set of capital standards known as the Basel Accords, banks are required to hold a minimum level of capital as a bu er to protect their depositors and the nancial market in an event of severe unexpected losses caused by nancial risk. Moreover, government regulators aim to maintain public con dence and trust in the banking system through the use of a deposit insurance scheme (DIS). Deposit insurance (DI) has the e ect of eliminating mass withdrawals of deposits in an event of a bank failure. However, DI comes at a cost. The insuring agent is tasked with estimating a fairly priced premium that the bank should be charged for DI.
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Ramkrishnan, Karthik. "Optimal Investment Strategy for Energy Performance Improvements in Existing Buildings." Thesis, Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19855.

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Current global efforts for energy conservation and optimization are focused on improvements in energy supply and production systems, and on encouraging the adoption of energy-efficient devices and equipment. However, systematic assessments of economic and technical implications when adopting energy-efficient alternative systems in buildings have not yet been explored thoroughly. The uncertainty about the consequences of investing in alternative energy-efficient systems has led to a prolonged utilization of obsolete building systems (underperforming HVAC systems, inefficient lighting systems, badly maintained and equipment, and so forth). This has led to overall poor energy efficiency, creating considerable burden on the building operation budget. This research discusses the procedure for formulating an investment strategy to improve existing building energy performance. The approach is suitable for large building portfolios where a plethora of potential refurbishment interventions can be considered. This makes our approach especially suited for use on university campuses and most of this report will focus on that particular application utilization protocols especially for use on campuses. This investment model only looks at the energy related savings versus investments; it is well understood that the ultimate selection of the optimal set of improvement options of a portfolio will be determined by additional considerations, such as overall value, occupant satisfaction, productivity improvements, aesthetics, etc. Nevertheless, many campus managers are confronted with the question how much energy they can save with a given investment amount. This is exactly what our approach helps to answer. The investment optimization strategy is implemented in software "InvEnergy," which systematically calculates the costs and benefits of all possible building-technology pairings, taking uncertainties in the saving/investment calculations and estimates into account. This tool empowers decision makers in facility management to make complex investment decisions during continuous building commissioning.
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Prezioso, Luca. "Financial risk sources and optimal strategies in jump-diffusion frameworks." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/254880.

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An optimal dividend problem with investment opportunities, taking into consideration a source of strategic risk is being considered, as well as the effect of market frictions on the decision process of the financial entities. It concerns the problem of determining an optimal control of the dividend under debt constraints and investment opportunities in an economy with business cycles. It is assumed that the company is to be allowed to accept or reject investment opportunities arriving at random times with random sizes, by changing its outstanding indebtedness, which would impact its capital structure and risk profile. This work mainly focuses on the strategic risk faced by the companies; and, in particular, it focuses on the manager's problem of setting appropriate priorities to deploy the limited resources available. This component is taken into account by introducing frictions in the capital structure modification process. The problem is formulated as a bi-dimensional singular control problem under regime switching in presence of jumps. An explicit condition is obtained in order to ensure that the value function is finite. A viscosity solution approach is used to get qualitative descriptions of the solution. Moreover, a lending scheme for a system of interconnected banks with probabilistic constraints of failure is being considered. The problem arises from the fact that financial institutions cannot possibly carry enough capital to withstand counterparty failures or systemic risk. In such situations, the central bank or the government becomes effectively the risk manager of last resort or, in extreme cases, the lender of last resort. If, on the one hand, the health of the whole financial system depends on government intervention, on the other hand, guaranteeing a high probability of salvage may result in increasing the moral hazard of the banks in the financial network. A closed form solution for an optimal control problem related to interbank lending schemes has been derived, subject to terminal probability constraints on the failure of banks which are interconnected through a financial network. The derived solution applies to real bank networks by obtaining a general solution when the aforementioned probability constraints are assumed for all the banks. We also present a direct method to compute the systemic relevance parameter for each bank within the network. Finally, a possible computation technique for the Default Risk Charge under to regulatory risk measurement processes is being considered. We focus on the Default Risk Charge measure as an effective alternative to the Incremental Risk Charge one, proposing its implementation by a quasi exhaustive-heuristic algorithm to determine the minimum capital requested to a bank facing the market risk associated to portfolios based on assets emitted by several financial agents. While most of the banks use the Monte Carlo simulation approach and the empirical quantile to estimate this risk measure, we provide new computational approaches, exhaustive or heuristic, currently becoming feasible, because of both new regulation and the high speed - low cost technology available nowadays.
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Wheeler, Douglas J. "Contributing factors to optimal project portfolio selection." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61988/2/Douglas_Wheeler_Thesis.pdf.

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A modified Delphi approach has been applied in this study to investigate best practice and to determine the factors that contribute to optimal selection of projects. There are various standards and practices that some may recognise as representing best practice in this area. Many of these have similar characteristics and this study has found no single best practice. The study identified the factors that contribute to the optimal selection of projects as: culture, process, knowledge of the business, knowledge of the work, education, experience, governance, risk awareness, selection of players, preconceptions, and time pressures. All these factors were found to be significant; to be appropriate to public sector organisations, private sector organisations and government owned corporations; and to have a strong linkage to research on strategic decision making. These factors can be consolidated into two underlying factors of organisation culture and leadership.
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Mtemeri, Nyika. "A model of pension portfolios with salary and surplus process." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_2931_1364203235.

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Essentially this project report is a discussion of mathematical modelling in pension funds, presenting sections from Cairns, A.J.D., Blake, D., Dowd, K., Stochastic lifestyling: Optimal dynamic asset allocation for defined contribution pension plans, Journal of Economic Dynamics and Control, Volume 30, Issue 2006, Pages 843-877, with added details and background material in order to demonstrate the mathematical methods. In the investigation of the management of the investment portfolio, we only use one risky asset together with a bond and cash as other assets in a 
continuous time framework. The particular model is very much designed according to the members&rsquo
preference and then the funds are invested by the fund manager in the financial market. At the end, we are going to show various simulations of these models. Our methods include stochastic control for utility maximisation among others. The optimisation problem entails the optimal 
investment portfolio to maximise a certain power utility function. We use MATLAB and MAPLE programming languages to generate results in the form of graphs and tables

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Kacelenga, Evans. "Towards an optimal product portfolio of liquid fuels for the Malawi energy market : development of a strategic framework for enhancing pathways of ethanol production and use." Thesis, University of Bolton, 2017. http://ubir.bolton.ac.uk/2001/.

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Ethanol has been blended with petrol in Malawi for over thirty years. However the strategic decisions for energy security regarding liquid fuels conspicuously omit ethanol. Fossil fuels continue to occupy first place in spite of the acknowledged fact that fossils reserves are getting exhausted and unsustainable. The goal of the research was to develop a strategic framework for sustainably promoting ethanol production so as to make it a significant part of the liquid fuels portfolio and reduce fossil fuel dependence in Malawi. The purpose of the research was to find possible pathways for increasing the production of and use of ethanol. Five pathways for increased ethanol production and use emerged from the interviews. An analysis of the interview findings identified three pathways for increased ethanol production. These were increasing feedstock for ethanol production, increasing sugarcane yields and increasing land under sugarcane. The analysis of the interviews identified two pathways for increasing ethanol use, one was government incentives and the other was the reduction of the ethanol price. Three interventions by government for achieving an optimal liquid fuel portfolio were identified as the introduction of ethanol driven vehicles, importation of flexi-fuel vehicles and the inclusion of ethanol tanks in the strategic fuel storage plan. There has been no research which explored strategically increasing ethanol in the liquid fuels portfolio in the Malawi context, as such this represents a significant contribution to knowledge. Specifically seventeen sustainability criteria for ethanol production and use were ranked and six were found to be most relevant. The positive economic contribution criterion was seen as the most relevant by the respondents in contrast to the European Union, Brazil, America and elsewhere where green house gas (GHG) mitigation is number one. The land use change (LUC) or indirect land use change criterion had mixed responses signifying that it is not well known. Both the goal and purpose of the research were achieved. A strategic framework was developed and pathways identified.
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Books on the topic "Optimal portfolio strategy"

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Golan, Amos. Foundations of Info-Metrics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.001.0001.

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This book provides a framework for info-metrics—the science of modeling, inference, and reasoning under conditions of noisy and insufficient information. Info-metrics is an inherently interdisciplinary framework that emerged from the intersection of information theory, statistical inference, and decision-making under uncertainty. It allows us to process the available information with minimal reliance on assumptions that cannot be validated. This book focuses on unifying all information processing and model building within a single constrained optimization framework. It provides a complete framework for modeling and inference, rather than a problem-specific model. The framework evolves from the simple premise that our available information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure. The book contains many multidisciplinary applications that demonstrate the simplicity and generality of the framework in real-world settings: These include initial diagnosis at an emergency room, optimal dose decisions, election forecasting, network and information aggregation, weather pattern analyses, portfolio allocation, inference of strategic behavior, incorporation of prior information, option pricing, and modeling an interacting social system. This book presents simple derivations of the key results that are necessary to understand and apply the fundamental concepts to a variety of problems. Derivations are often supported by graphical illustrations. The book is designed to be accessible for graduate students, researchers, and practitioners across the disciplines, requiring only basic quantitative skills and a little persistence.
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Book chapters on the topic "Optimal portfolio strategy"

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Yan, Guangchen. "Optimal Portfolio Strategy Research Based on Convolutional Neural Network." In Atlantis Highlights in Intelligent Systems, 664–70. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-010-7_68.

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Jaffar Sadiq Abdullah, Muhammad, and Norizarina Ishak. "An Optimal Control Approach to Portfolio Diversification on Large Cap Stocks Traded in Tokyo Stock Exchange." In Control Theory in Engineering [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.100613.

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In this chapter, Markowitz mean-variance approach is proposed for examining the best portfolio diversification strategy within three subperiods which are during the global financial crisis (GFC), post-global financial crisis, and during the non-crisis period. In our approach, we used 10 securities from five different industries to represent a risk-mitigation parameter. In this way, the naive diversification strategy is used to serve as a comparison for the approach used. During the computation process, the correlation matrices revealed that the portfolio risk is not well diversified during non-crisis periods, meanwhile, the variance-covariance matrices indicated that volatility can be minimized during portfolio construction. On this basis, 10 efficient portfolios were constructed and the optimal portfolios were selected in each subperiods based on the risk-averse preference. Performance-wise that optimal portfolio dominated the naïve strategy throughout the three subperiods tested. All the optimal portfolios selected are yielding more returns compared to the naïve portfolio.
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D. Navas, Raúl, Sónia R. Bentes, and Helena V. G. Navas. "Optimized Portfolios: All Seasons Strategy." In Quality Control in Intelligent Manufacturing [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.95122.

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Our study explores the efficient frontier of optimal investment, taking behind the Markowitz’s theory, while advocating a diversified portfolio to reduce risk. To perform it, six portfolio models are proposed, and its formation are made by a solver, where the selected solving method is the GRG Nonlinear engine for linear solver problems. Our main goal is to design portfolios that resists to financial crisis but at the same time persists in a wealthy period. We analyze the decade where we assisted to two crashes (2000–2010) and a semi-decade where we assist to a wealthy period (2011–2018). The assets used are varied, such as Equities indexes form various countries, sector equities, bonds, commodities, EURUSD exchange and VIX. Results show that the GRG Nonlinear engine is powerful, providing excess returns in all six models.
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Lyu, Yiyang, Kangnong Hu, Haonan Xu, and Biao Zhang. "Gold or BTC: The Best Trading Strategy." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221051.

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This paper focuses on the Trading strategy issues of gold, US dollar and bitcoin in the financial markets.We are supposed to deal with the C problem of 2022 Mathematical Contest In Modeling. To solve this problem, we firstly build a VMD(CEEMD)-LSTM-AdaBoost-SVM model to predict the price of gold and BTC. Based on the forecast data, we build the optimal investment dynamic planning problem by using Floyd algorithm to find the maximum profit route of daily optimal trading strategy from September 11, 2016 to September 11, 2021. Our assets went from an initial $1,000 to $52,048 with an annual profit margin of 219.58%. Then, we performed sensitivity analysis and error analysis of the developed model. Finally, we improved and extended the existing model by building the Measure-VaR multi-stage portfolio model and solve it by using the PSO algorithm, the theoretical results is good.
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DeMiguel, Victor, Lorenzo Garlappi, and Raman Uppal. "Optimal versus Naive Diversification: How Inefficient Is the 1/N Portfolio Strategy?" In Heuristics, 644–64. Oxford University Press, 2011. http://dx.doi.org/10.1093/acprof:oso/9780199744282.003.0034.

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Singh, Sarthak, Vedank Goyal, Sarthak Goel, and H. C. Taneja. "Deep Reinforcement Learning Models for Automated Stock Trading." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220738.

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This paper proposes an automated trading strategy using reinforcement learning. The stock market has become one of the largest financial institutions. These institutions embrace machine learning solutions based on artificial intelligence for market monitoring, credit quality, fraud detection, and many other areas. We desire to provide an efficient and effective solution that would overcome the manual trading drawbacks by building a Trading Bot. In this paper, we will propose a stock trading strategy that uses reinforcement learning algorithms to maximize the profit. The strategy employs three actor critic models: Advanced Actor Critic(A2C), Twin Delayed DDPG (TD3) and Soft Actor Critic (SAC). Our strategy picks the most optimal model based on the current market situation. The performance of our trading bot is evaluated and compared with Markowitz portfolio theory.
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Panja, Soma, and Dilip Roy. "Risk-Based Selection of Portfolio." In Handbook of Research on Strategic Business Infrastructure Development and Contemporary Issues in Finance, 222–37. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5154-8.ch016.

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This chapter examines the closeness between the optimum portfolio and portfolio selected by an investor who follows a heuristic approach. There may be basically two ways of arriving at an optimum portfolio – one by minimizing the risk and the other by maximizing the return. In this chapter, the authors propose to strike a balance between these two. The optimum portfolio has been obtained through a mathematical programming framework so as to minimize the portfolio risk subject to return constraint expressed in terms of coefficient of optimism (a), where a varies between 0 to 1. Simultaneously, the authors propose to develop four heuristic portfolios for the optimistic and pessimistic investors, risk planners, and random selectors. Given the optimum portfolio and a heuristic portfolio, City Block Distance has been calculated to measure the departure of the heuristic solution from the optimum solution. Based on daily security wise data of ten companies listed in Nifty for the years 2004 to 2008, the authors have obtained that when the value of a lies between 0 to 0.5, the pessimistic investor's decision is mostly closest to the optimum solution, and when the value of a is greater than 0.5, the optimistic investor's decision is mostly near to the optimum decision. Near the point a = 0.5, the random selectors and risk planners' solutions come closer to the optimum decision. This study may help the investors to take heuristic investment decision and, based on his/her value system, reach near to the optimum solution.
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Lestari, S., and D. Rahadian. "Optimal stock portfolio establishment using PEG and Tobin’s Q ratio with active and passive strategy approach in JKLQ45 index 2013–2018 period." In Synergizing Management, Technology and Innovation in Generating Sustainable and Competitive Business Growth, 130–34. Routledge, 2021. http://dx.doi.org/10.1201/9781003138914-23.

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Syrris, Vassilis. "Information Technology Portfolio Management." In Strategic Information Technology and Portfolio Management, 118–49. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-687-7.ch007.

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This study involves the exploration of Information Technology Portfolio Management (ITPM) in conjecture with Modern Portfolio Theory (MPT). The ITPM constitutes an indispensable part of the Strategic Information System which is an integrated multi-functional structure having its origin in works of Anthony (1965) and Gibson & Nolan (1974). ITPM attempts to resolve the project selection problem concerning the majority of project-centric organizations. Roughly speaking, projects must compete for the scarce resources which are only available for the fittest ones. Although a large proportion of portfolio selection decisions are taken on a qualitative basis, quantitative approaches to selection such as MPT or its variants are beginning to gain wide application. Yet, the standard MPT has proved incapable of representing the complexities of real-world situations. This issue generates the present work which focuses on the project portfolio optimization problem through a meta-heuristic approach, that is, application of algorithmic methods able to find near-optimal solutions in a reasonable amount of computation time. The ultimate goal is the enhancement of IT portfolio management which has become a significant factor in the planning and operation of information-based organizations, thus, offering a competitive advantage.
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MacLeod, Matthew R., Mark Rempel, and Michael L. Roi. "Decision Support for Optimal Use of Joint Training Funds in the Canadian Armed Forces." In Analytics, Operations, and Strategic Decision Making in the Public Sector, 255–76. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7591-7.ch012.

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Joint exercises are vital to the Canadian Armed Forces (CAF) meeting its readiness targets. However, CAF resources are often insufficient to participate in all candidate joint exercises. Many organizations face resource challenges. In the context of preparing the CAF for its mandated missions and operational tasks, this chapter addresses the following research question: How can the CAF get the most value out of its joint training resources? Using strategic analysis and operations research, the authors designed a value model to gauge a joint exercise's value and an optimization model to support decision makers when selecting a joint exercise portfolio. This chapter describes these models, presents an example of their application, and discusses future improvements.
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Conference papers on the topic "Optimal portfolio strategy"

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Shi-Qi Ye and Yong Peng. "The optimal strategy of portfolio selection with transaction costs." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527544.

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Huang, Xiaoxia, and Yuanqiong You. "Optimal Mixed Project and Security Portfolio Selection under Reinvestment Strategy." In 2017 International Conference on Industrial Engineering, Management Science and Application (ICIMSA). IEEE, 2017. http://dx.doi.org/10.1109/icimsa.2017.7985599.

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Gao, Jianwei. "Optimal Investment Strategy for Merton's Portfolio Optimization Problem under a CEV Model." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5304889.

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Yanqing Wang. "Intelligent Method for Solving Optimal Strategy of Dynamic Portfolio Selection with Credibility Criterion." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1712983.

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Li, Jincheng, Xinyi Liu, Yicheng Jiang, Haiwei Xie, Chuan He, Kai Wang, Hailong Jiang, et al. "Optimal Investment Strategy for Thermal Generation Companies Based on Portfolio Theory under RPS." In 2020 International Conference on Smart Grids and Energy Systems (SGES). IEEE, 2020. http://dx.doi.org/10.1109/sges51519.2020.00178.

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Wang, G., X. Ge, K. Li, P. Tao, P. Ren, and F. Wang. "OPTIMAL PORTFOLIO AND BIDDING STRATEGY FOR DER AGGREGATORS PARTICIPATING IN VARIOUS ANCILLARY SERVICES TRANSACTIONS." In The 10th Renewable Power Generation Conference (RPG 2021). Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/icp.2021.2301.

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Yeter, Baran, Yordan Garbatov, and Carlos Guedes Soares. "Optimal Management of Offshore Wind Assets at Different Stages of Life Extension Accounting for Uncertainty Propagation." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-78185.

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Abstract The objective of the present study is to develop an optimal life extension management strategy for ageing offshore wind farms. The optimal management strategy is attained by minimising the overall risk of a group of different assets based on the principles of the modern portfolio theory. The statistical measures regarding the risk and expected return are obtained through a probabilistic techno-economic assessment, which considers the mean wind speed, capacity factor, degradation severity, initial crack size and feed-in tariff as stochastic variables. The expected return accounts for the time-weighted incremental free cash flows. The risk and return of the offshore wind farm are investigated under optimistic, moderate, and pessimistic scenarios. Finally, the optimal allocation (portfolio) of offshore wind assets attained based on the mean-variance optimisation is presented for the different stages of the life extension of the offshore wind farms accounting for the uncertainty propagation during the life extension. The uncertainty propagation is reflected in the correlation and variability between the offshore wind turbines and their operation. The results indicate that a significant risk reduction can be achieved by employing an offshore wind asset allocation according to the risk-adjusted portfolio. Also, the benefit of using a risk-adjusted allocation increases as the life extension moves on. The outcome of the present study can be useful for the feature engineering part of the deep neural network training for the classification of ageing offshore wind turbines.
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Lee, Jinho, Raehyun Kim, Seok-Won Yi, and Jaewoo Kang. "MAPS: Multi-Agent reinforcement learning-based Portfolio management System." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/623.

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Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposing an optimal model or network architecture by maximizing return. However, these models often fail to consider and adapt to the continuously changing market conditions. In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS). MAPS is a cooperative system in which each agent is an independent "investor" creating its own portfolio. In the training procedure, each agent is guided to act as diversely as possible while maximizing its own return with a carefully designed loss function. As a result, MAPS as a system ends up with a diversified portfolio. Experiment results with 12 years of US market data show that MAPS outperforms most of the baselines in terms of Sharpe ratio. Furthermore, our results show that adding more agents to our system would allow us to get a higher Sharpe ratio by lowering risk with a more diversified portfolio.
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Feng, Yingchun, Jie Fan, Yu Jiang, Xuesong Li, Tianyu Li, Ciwei Gao, and Tao Chen. "Optimal Trading Strategy of Inter-and Intra-provincial Medium-and Long-term Power Exchange Considering Renewable Portfolio Standard." In 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2020. http://dx.doi.org/10.1109/appeec48164.2020.9220555.

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Wang, Fei, Ge Wang, Xinxin Ge, and Fei Li. "Optimal Portfolio Strategy of DERs for Offering the Flexible Ramping Ancillary Services under High Penetration Distributed PV Scenario." In 2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia). IEEE, 2022. http://dx.doi.org/10.1109/icpsasia55496.2022.9949666.

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