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Journal articles on the topic "Portfolio management":
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AAS, TOR HELGE, KARL JOACHIM BREUNIG, and KATJA MARIA HYDLE. "EXPLORING NEW SERVICE PORTFOLIO MANAGEMENT." International Journal of Innovation Management 21, no. 06 (July 27, 2017): 1750044. http://dx.doi.org/10.1142/s136391961750044x.
Most research on the management of innovation portfolios has focused on new product portfolios, whereas the management of new service portfolios has not been researched correspondingly. This paper addresses this literature gap by exploring portfolio management of New Service Development (NSD) activities empirically. The paper applies a qualitative research design, where data was collected in 52 in-depth interviews with managers and employees involved with NSD. The study finds that the portfolio management activities and processes were carried out in parallel with the NSD process, and that the most important stakeholders in the NSD portfolio management organization were top managers not involved in the daily NSD operations. Findings reveal that the firms used a great variety of criteria when making portfolio decisions. However, contrary to prescriptions based on new product development research, the decision process exposed for NSD was to a limited degree assisted by explicit portfolio management tools. We explicate our findings in five propositions.
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Nisani, Doron. "Portfolio selection using the Riskiness Index." Studies in Economics and Finance 35, no. 2 (June 4, 2018): 330–39. http://dx.doi.org/10.1108/sef-03-2017-0058.
PurposeThe purpose of this paper is to increase the accuracy of the efficient portfolios frontier and the capital market line using the Riskiness Index.Design/methodology/approachThis paper will develop the mean-riskiness model for portfolio selection using the Riskiness Index.FindingsThis paper’s main result is establishing a mean-riskiness efficient set of portfolios. In addition, the paper presents two applications for the mean-riskiness portfolio management method: one that is based on the multi-normal distribution (which is identical to the MV model optimal portfolio) and one that is based on the multi-normal inverse Gaussian distribution (which increases the portfolio’s accuracy, as it includes the a-symmetry and tail-heaviness features in addition to the scale and diversification features of the MV model).Research limitations/implicationsThe Riskiness Index is not a coherent measurement of financial risk, and the mean-riskiness model application is based on a high-order approximation to the portfolio’s rate of return distribution.Originality/valueThe mean-riskiness model increases portfolio management accuracy using the Riskiness Index. As the approximation order increases, the portfolio’s accuracy increases as well. This result can lead to a more efficient asset allocation in the capital markets.
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Yang, Hyunjun, Hyeonjun Park, and Kyungjae Lee. "A Selective Portfolio Management Algorithm with Off-Policy Reinforcement Learning Using Dirichlet Distribution." Axioms 11, no. 12 (November 23, 2022): 664. http://dx.doi.org/10.3390/axioms11120664.
Existing methods in portfolio management deterministically produce an optimal portfolio. However, according to modern portfolio theory, there exists a trade-off between a portfolio’s expected returns and risks. Therefore, the optimal portfolio does not exist definitively, but several exist, and using only one deterministic portfolio is disadvantageous for risk management. We proposed Dirichlet Distribution Trader (DDT), an algorithm that calculates multiple optimal portfolios by taking Dirichlet Distribution as a policy. The DDT algorithm makes several optimal portfolios according to risk levels. In addition, by obtaining the pi value from the distribution and applying importance sampling to off-policy learning, the sample is used efficiently. Furthermore, the architecture of our model is scalable because the feed-forward of information between portfolio stocks occurs independently. This means that even if untrained stocks are added to the portfolio, the optimal weight can be adjusted. We also conducted three experiments. In the scalability experiment, it was shown that the DDT extended model, which is trained with only three stocks, had little difference in performance from the DDT model that learned all the stocks in the portfolio. In an experiment comparing the off-policy algorithm and the on-policy algorithm, it was shown that the off-policy algorithm had good performance regardless of the stock price trend. In an experiment comparing investment results according to risk level, it was shown that a higher return or a better Sharpe ratio could be obtained through risk control.
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Attar, Arbaz, Pranay Mule, Piyush Kulkarni, Shubham Narale, and Prof Ms Jaitee Bankar. "Investment Portfolio Management System: A Survey." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 2966–68. http://dx.doi.org/10.22214/ijraset.2023.52241.
Abstract: An investment portfolio management system is a highly sophisticated software application meticulously crafted to assist investors in the management of their investment portfolios. This innovative system provides investors with a centralized platform that empowers them to track their investments meticulously, closely monitor their performance, and judiciously make informed investment decisions. The system encompasses several advanced features such as portfolio analysis, risk management tools, asset allocation strategies, and performance reporting, that provide investors with a comprehensive overview of their portfolio's performance. Additionally, this cutting-edge platform offers investors the opportunity to diversify their portfolio by investing across multiple asset classes such as stocks, bonds, and mutual funds. This paper delves into the various techniques and methods employed to identify the optimal strategy to maximize gains from the investment. The fusion of algorithms and investments has revolutionized the investment landscape, enabling investors to obtain insightful data and make data-driven decisions. Several research studies have been conducted in the investment field, bolstered by machine learning models and algorithms, resulting in exceptional gains for investors.
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Zhang, Shicheng. "Portfolio Management for Multi-industry." Highlights in Business, Economics and Management 5 (February 16, 2023): 214–21. http://dx.doi.org/10.54097/hbem.v5i.5078.
In the financial field, portfolio management is an important measure in the direction of investment or hedging. This paper mainly focuses on the optimization for the portfolio composed of assets from five industries, which is education, banking, automobile manufacturing, parts industry and e-commerce, and considers the allocation of assets to optimize the returns. In this paper, five representative assets from these five industries are selected. The Markowitz efficient frontier is plotted by Monte-Carlo method, using the return data of assets. Then the portfolio is optimized by mean-variance analysis and the maximum Sharpe ratio portfolio as well as minimum variance portfolio can be calculated. Finally, this paper analyzes the performance of the two portfolios, considering the influence of individual assets on the portfolio weight, and uses the Fama-French three factor model to analyze the performance of the portfolio. The results show that PTAIY and PSO from parts manufacturing and education occupy a large proportion in the maximum Sharpe ratio portfolio as well as the minimum variance portfolio. The findings could help investors interested in these five areas.
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Fiala, Petr. "New trends in project portfolio management." Trendy v podnikání 10, no. 3 (2021): 4–11. http://dx.doi.org/10.24132/jbt.2020.10.3.4_11.
The use of project portfolio management is increasingly becoming a tool for promoting the strategy of the organization. Using sophisticated quantitative tools becomes a significant competitive advantage for project portfolio management. Project portfolio management is a dynamic multi-criteria decision-making problem under risk. The paper presents new approaches for analyzing the problem. A dynamic version of the Analytic Network Process (ANP) captures the network, multicriteria and dynamic structure of the problem. Multicriteria decision trees analyze risk of project portfolios. Possible projects are characterized by sets of inputs and outputs, where inputs are resources for project realization and outputs measure multiple criteria of goals of the organization. The Data Envelopment Analysis (DEA) is an appropriate approach to select efficient project portfolios.
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Kiyko, S., L. Deineha, M. Basanets, D. Kamienskyi, and A. Didenko. "PORTFOLIO MANAGEMENT OF ENERGY SAVING PROJECTS BASED ON THE MARKOVITS THEORY." Integrated Technologies and Energy Saving, no. 3 (November 9, 2021): 79–91. http://dx.doi.org/10.20998/2078-5364.2021.3.08.
The goal of the work was to identify research and compare methods of portfolio management of energy saving projects and to develop software for optimizing portfolio investments using several methods. The key elements and strategies of creating an effective investment portfolio are considered: diversification, rebalancing, active portfolio management, passive portfolio management.
Given the basic principles of investment theory, the task of portfolio investment is to form an investment portfolio with known shares of certain assets to maximize returns and minimize risk. To solve this problem, the method of Harry Markowitz, known as modern portfolio theory, was chosen. This is the theory of financial investment, in which statistical methods are used to make the most profitable risk distribution of the securities portfolio and income valuation, its components are asset valuation, investment decisions, portfolio optimization, evaluation of results. From a mathematical point of view, the problem of forming an optimal portfolio is the problem of optimizing a quadratic function (finding the minimum) with linear constraints on the arguments of the function.
Methods of optimization of portfolios of energy saving projects taking into account the specifics of the subject area are analyzed. According to the results of the analysis, the methods of finding the maximum Sharpe’s ratio and the minimum volatility from randomly generated portfolios were chosen.
A software application has been developed that allows you to download data, generate random portfolios and optimize them with selected methods. A graphical display of portfolio optimization results has also been implemented. The program was tested on data on shares of energy saving companies. The graphs built by the program allow the operator to better assess the created portfolio of the energy saving project.
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Levchenko, Valentyna, and Myroslav Ostapenko. "Formation of the optimal portfolio of insurer’s services of the voluntary types of insurance." Insurance Markets and Companies 7, no. 1 (November 18, 2016): 45–51. http://dx.doi.org/10.21511/imc.7(1).2016.05.
The article studies the possibility of using optimization modelling to form the optimal structure of insurance services’ portfolio of insurance companies. Based on the data of net insurance payments and profitability of the voluntary types of insurance in 2005-2015, the authors conducted their analysis according to the possibility to be included in the general insurance portfolio of the insurance company. The optimization model is based on the approach developed by G. Markowitz. The formation of insurance services portfolio is conducted by solving the optimization problem to maximize the portfolios’ profitability or to minimize the portfolio’s risks. The obtained results can be used in making strategic decisions by the management regarding the development of insurance companies. Keywords: insurance company, insurance service, insurance portfolio, portfolio optimization
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Micán, Camilo, Gabriela Fernandes, and Madalena Araújo. "Disclosing the Tacit Links between Risk and Success in Organizational Development Project Portfolios." Sustainability 14, no. 9 (April 26, 2022): 5235. http://dx.doi.org/10.3390/su14095235.
Project portfolios aim to impact organizational strategic goals, influencing both the organization’s business model and its processes. Nonetheless, the actual impact is dependent on the portfolio’s success, which is affected by the materialization of risk factors. This study aims to examine the tacit conceptualization of project portfolio risk as a risk measure explicitly based on project portfolio success itself. In order to focus on the portfolios of organizational development projects, Social Representation Theory was adopted to analyze empirical evidence from twenty-eight semi-structured interviews conducted with project portfolio practitioners. Findings showed that strategic fit, future preparedness, and stakeholder satisfaction were dimensions of success within which project portfolio risk could be conceptualized. Additionally, results evidenced that risk factors influenced project portfolio success through systematic and non-systematic impacts on project portfolio outputs, and also had direct impacts on project portfolio outcomes. This paper provides empirical evidence to back up the conceptualization of project portfolio risk explicitly oriented to portfolio success as a multidimensional risk measure. It represents a new avenue for conducting portfolio risk analysis for both practitioners and academics, orienting the decision-making process based on the portfolio success rather than only on the success of each project.
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Elton, Edwin J., and Martin J. Gruber. "Optimum Centralized Portfolio Construction with Decentralized Portfolio Management." Journal of Financial and Quantitative Analysis 39, no. 3 (September 2004): 481–94. http://dx.doi.org/10.1017/s0022109000003999.
AbstractMany financial institutions employ outside portfolio managers to manage part or all of their investable assets. It is well recognized that outside portfolio managers are unwilling to share security information with each other or with the centralized decision maker and this in general will lead to sub-optimal portfolios. In this paper, we derive an implementable set of rules under which a central decision maker can make optimal decisions without requiring decentralized decision makers to reveal estimates of security returns. Furthermore, we derive conditions under which these rules hold and when they do not hold.
Dissertations / Theses on the topic "Portfolio management":
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Tolonen, A. (Arto). "Product portfolio management over horizontal and vertical portfolios." Doctoral thesis, Oulun yliopisto, 2016. http://urn.fi/urn:isbn:9789526212678.
Abstract The main objective of this study is to clarify the current challenges and preconditions relating to product portfolio management (PPM) and widen the PPM framework over horizontal and vertical portfolios, including a related governance model, strategic performance management and the PPM process. This study analyses comprehensively the current PPM literature and the relevant practices of 10 case companies representing business areas such as hardware (HW), software (SW) and Services. This study approaches PPM from a more comprehensive viewpoint as all product life cycle phases and product structure levels are not covered well in this context by the earlier literature. The principal results of this study involve revealing the need for a new PPM governance model including strategic targets, KPIs and the PPM process according to vertical and horizontal portfolios. The created PPM framework clarifies the strategic role of PPM in cross-functional analysis and decision making for commercial and technical portfolios. The role and the impact of strategic PPM have been further enhanced by positioning the PPM process on the level of other business processes. The created PPM framework enhances the collaboration between business and engineering teams. The managerial implications include the potential preconditions of clarifying the dynamic and active role of PPM at the level of other business processes. The findings can aid business managers in understanding PPM as an entity that has a role in managing the entire product portfolio and its renewal based on strategic performance measures over horizontal and vertical portfolios according to cross-functional governance bodies. This highlights the criticality of managing all items both in commercial and technical portfolios. The role of other business processes should be highly operational by executing product development, marketing and sales, delivery and care activities according to PPM decisions. The primary role of PPM should be active management of the entire product portfolio over product life cycle phases and product structure levels, instead of merely focusing on new product development, to ensure product portfolio renewal Tiivistelmä Tämä tutkimus selventää tuoteportfolion hallintaan liittyviä edellytyksiä ja haasteita, sekä laajentaa tuoteportfolion hallintamallia, suorituskyvyn johtamista ja prosessia horisontaalisesti ja vertikaalisesti. Tuoteportfolion hallintaa on lähestytty kattavasti analysoimalla nykyistä kirjallisuutta, sekä kymmenen kohdeyrityksen käytänteitä nykytila-analyysin keinoin. Kohdeyritykset edustavat useita liiketoiminta- ja tuotealueita kattaen laitteiston, ohjelmiston ja palvelut. Tämä tutkimus lähestyy tuoteportfolion hallintaa laajemmalta katsantokannalta kuin nykyinen kirjallisuus joka ei kata kaikkia tuotteen elinkaaren vaiheita ja tuoterakennetasoja. Tämän väitöstutkimuksen tärkeimmät tulokset liittyvät uuden tuoteportfolion hallintamallin tarpeellisuuden esille tuomiseen, sisältäen tuoteportfolion strategiset tavoitteet, suorituskykymittarit ja hallintaprosessin perustuen vertikaalisiin ja horisontaalisiin tuoteportfolioihin. Luotu viitekehys selkeyttää tuoteportfolion hallinnan strategista roolia organisaatiorajat ja liiketoimintaprosessit ylittävässä analyysissa ja päätöksenteossa liittyen kaupallisiin ja teknisiin tuoteportfolioihin. Strategisen tuoteportfolion hallinnan roolia ja merkitystä on erityisesti korostettu nostamalla tuoteportfolion hallintaprosessi muiden liiketoimintaprosessien tasolle. Tässä tutkimuksessa luotu tuoteportfolion hallinnan viitekehys vahvistaa yhteistyötä liiketoiminnanjohto- ja insinööritiimien välillä kaikilla organisaatiotasoilla. Työn kontribuutiot yritysjohdolle korostavat tuoteportfolion hallintaprosessin keskitettyä, dynaamista ja aktiivista roolia johtaa yrityksen kaupallisia ja teknisiä nimikkeitä horisontaalisesti ja vertikaalisesti kokonaisuutena perustuen strategisiin suorituskykymittareihin. Tuoteportfolion hallinta yli horisontaalisten ja vertikaalisten portfolioiden mahdollistaa tuoteportfolion uudistumisen yli kaikkien elinkaarivaiheiden ja tuoterakennetasojen. Muiden liiketoimintaprosessien roolin tulisi olla selkeästi operatiivinen toteuttaen tuotekehitykseen, markkinointiin, myyntiin, tilaamiseen, hankintaan, toimittamiseen ja huoltoon liittyviä tehtäviä perustuen strategisiin tuoteportfolion hallinnan tavoitteisiin ja suorituskykymittareihin
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Pachtová, Iva. "Portfolio management v projektovém řízení." Master's thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-2098.
Hlavním cílem této práce je poskytnout přehledné a ucelené informace o aplikaci portfolia managementu v projektovém řízení, zprostředkovat zkušenosti a doporučení ze zahraničních aplikací a také seznámit potencionální zájemce s návody, jak v případě zájmu postupovat při aplikaci v praxi. Práce vychází z obecného pohledu klasické teorie portfolia, na tuto část navazuje teoreticky zaměřený úsek věnující se teorii portfolio managementu. Poslední část je věnována aplikaci portfolia managementu a konkrétní ukázce implementace z praxe.
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Phillips, Brandis. "Information systems portfolio management the impact of portfolio management practices /." Diss., Connect to online resource - MSU authorized users, 2008.
Thesis (Ph. D.)--Michigan State University. Dept. of Accounting and Information Systems, 2008. Title from PDF t.p. (viewed on July 2, 2009) Includes bibliographical references (p. 98-102). Also issued in print.
Alimohammadi, Reza. "Portfolio strategic control and portfolio management performance." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/102162/4/Reza_Alimohammadi_Thesis.pdf.
This thesis presents the development of a new control mechanism for managing portfolio of projects in today’s rapidly changing environment and fierce global competitions. “Portfolio Strategic Control” combines elements of portfolio management and functions of strategic management to control portfolios in a strategic manner and improve portfolio’s performance. This feedforward approach can be applied in parallel with traditional feedback control system to prepare portfolio for future environments by aligning its objectives with organisational strategy, managing resources, risks, and opportunities in an integrated fashion, and adding elements of flexibility and learning to portfolio.
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Wong, Kwok Chuen. "Topics in portfolio management." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/56044.
In this thesis, two topics in portfolio management have been studied: utility-risk portfolio selection and a paradox in time consistency in mean-variance problem. The first topic is a comprehensive study on utility maximization subject to deviation risk constraints. Under the complete Black-Scholes framework, by using the martingale approach and mean-field heuristic, a static problem including a variational inequality and some constraints on nonlinear moments, called Nonlinear Moment Problem, has been obtained to completely characterize the optimal terminal payoff. By solving the Nonlinear Moment Problem, the various well-posed mean-risk problems already known in the literature have been revisited, and also the existence of the optimal solutions for both utility-downside-risk and utility-strictly-convex-risk problems has been established under the assumption that the underlying utility satisfies the Inada Condition. To the best of our knowledge, the positive answers to the latter two problems have long been absent in the literature. In particular, the existence of an optimal solution for utility-semivariance problem, an example of the utility-downside-risk problem, is in substantial contrast to the nonexistence of an optimal solution for the mean-semivariance problem. This existence result allows us to utilize semivariance as a risk measure in portfolio management. Furthermore, it has been shown that the continuity of the optimal terminal wealth in pricing kernel, thus the solutions in the binomial tree models converge to the solution in the continuous-time Black-Scholes model. The convergence can be applied to provide a numerical method to compute the optimal solution for utility-deviation-risk problem by using the optimal portfolios in the binomial tree models, which are easily computed; such numerical algorithm for optimal solution to utility-risk problem has been absent in the literature. In the second part of this thesis, a paradox in time consistency in mean-variance has been established. People often change their preference over time, so the maximizer for current preference may not be optimal in the future. We call this phenomenon time inconsistency or dynamic inconsistency. To manage the issues of time inconsistency, a game-theoretic approach is widely utilized to provide a time-consistent equilibrium solution for dynamic optimization problem. It has been established that, if investors with mean-variance preference adopt the equilibrium solutions, an investor facing short-selling prohibition can acquire a greater objective value than his counterpart without the prohibition in a buoyant market. It has been further shown that the pure strategy of solely investing in bond can sometimes simultaneously dominate both constrained and unconstrained equilibrium strategies. With numerical experiments, the constrained investor can dominate the unconstrained one for more than 90% of the time horizon. The source of paradox is rooted from the nature of game-theoretic approach on time consistency, which purposely seeks for an equilibrium solution but not the ultimate maximizer. Our obtained results actually advocate that, to properly implement the concept of time consistency in various financial problems, all economic aspects should be critically taken into account at a time.
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Du, Plooy A. P. "Coal contract portfolio management." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/6404.
En el capítulo I, se examina el rendimiento de los activos financieros que representan las "mejores ideas" de los gestores de los fondos de inversión. Las inversiones para las que un gestor activo augura un buen rendimiento obtienen mejor retorno de mercado, asi como el resto de inversiones en sus carteras. En el capítulo II, se muestra explicitamente que los gestores que concentran sus carteras en un número reducido de activos, superan reiteradamente sus benchmarks y otros fondos más diversificados. Esta diferencia de rendimiento se puede explicar gracias a las diferencias en la exposición a factores de riesgo valorados por el mercado y al mayor talento de los gestores que se centran en invertir en activos de alta incertidumbre. En el capítulo III, se estudia la información contenida en las transacciones de activos y se muestra que las decisiones recientes de los gestores predicen el rendimiento futuro de las inversiones. Mientras que las compras llevadas a cabo por gestores con una habilidad superior se asocian a un rendimiento futuro anormalmente positivo, los gestores poco hábiles cometen errores de forma sistemática en la selección y en las transacciones de activos. In Chapter I, we examine the performance of stocks that represent mutual fund managers' "best ideas". The stock that active managers display the most conviction towards ex-ante, significantly outperforms the market, as well as the other stocks in those managers' portfolios. In Chapter II, I explicitly show that managers, who concentrate their portfolios into a small number of stocks, consistently beat their benchmarks and their more diversified peers. This performance gap can be explained by differing portfolio exposures towards priced risk factors as well as stronger abilities of concentrated managers when investing in stocks with high uncertainty of information. In Chapter III, I study the information content of portfolio rebalances by mutual fund managers and show that their recent trading decisions predict future stock returns. While purchases by skilled managers are associated with positive future abnormal performance, unskilled managers systematically commit errors in the selection and trading of stocks.
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Kouch, Richard Banking & Finance Australian School of Business UNSW. "Efficient estimation in portfolio management." Awarded by:University of New South Wales. School of Banking and Finance, 2006. http://handle.unsw.edu.au/1959.4/26943.
This thesis investigates whether estimating the inputs of the Markowitz (1952) Mean- Variance framework using various econometric techniques leads to improved optimal portfolio allocations at the country, sector and stock levels over a number of time periods. We build upon previous work by using various combinations of conventional and Bayesian expected returns and covariance matrix estimators in a Mean-Variance framework that incorporates a benchmark reference, an allowable deviation range from the benchmark weights and short-selling constraints so as to achieve meaningful and realistic outcomes. We found that models based on the classical maximum likelihood method performed just as well as the more sophisticated Bayesian return estimators in the study. We also found that the covariance matrix estimators analysed created covariance matrices that were similar to one another and, as a result, did not seem to have a large effect on the overall portfolio allocation. A sensitivity analysis on the level of risk aversion confirmed that the simulation results were robust for the different levels of risk aversion.
Xidonas, Panos, George Mavrotas, Theodore Krintas, John Psarras, and Constantin Zopounidis. Multicriteria Portfolio Management. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3670-6.
Catherine, Shenoy, and Kent C. Mccarthy, eds. Applied Portfolio Management. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119196747.
Leibowitz, Martin L., Simon Emrich, and Anthony Bova. Modern Portfolio Management. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2008. http://dx.doi.org/10.1002/9781118267189.
Book chapters on the topic "Portfolio management":
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Sekhar, G. V. Satya. "Portfolio Management." In The Management of Mutual Funds, 133–48. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34000-5_6.
Doumpos, Michael, and Constantin Zopounidis. "Portfolio Management." In Multicriteria Analysis in Finance, 61–81. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05864-1_5.
Van der Auwera, Eline, Wim Schoutens, Marco Petracco Giudici, and Lucia Alessi. "Portfolio Management." In SpringerBriefs in Finance, 97–103. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51093-0_6.
Hacιsalihzade, Selim S. "Portfolio Management." In Control Engineering and Finance, 215–40. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64492-9_8.
Cetindamar, Dilek, Rob Phaal, and David Probert. "Portfolio Management." In Technology Management, 173–82. London: Macmillan Education UK, 2010. http://dx.doi.org/10.1007/978-1-349-92389-2_9.
Stephan, Ulrich. "Portfolio-Management." In Informationseffizienz von Aktienindexoptionen, 77–125. Wiesbaden: Deutscher Universitätsverlag, 1998. http://dx.doi.org/10.1007/978-3-322-99717-3_2.
Pustišek, Andrej, and Michael Karasz. "Portfolio Management." In Natural Gas: A Commercial Perspective, 199–205. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53249-3_9.
Conference papers on the topic "Portfolio management":
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Maknickienė, Nijolė, Raimonda Martinkutė-Kaulienė, and Lina Rapkevičiūtė. "FAMILIARITY BIAS INVESTIGATIO IN PORTFOLIO CREATION." In 12th International Scientific Conference „Business and Management 2022“. Vilnius Gediminas Technical University, 2022. http://dx.doi.org/10.3846/bm.2022.775.
The prevailing opinion exists that investors include to their portfolio what they know or what is located around them. Investment decision, which is impacted by familiarity bias, avoid including international companies to portfolio which might lead to lower performance compared to portfolio which has both, local and international, stocks in a portfolio. The aim of this study is to analyse the impact of familiarity bias on investment decision, to form port-folios from the stocks listed on the Nasdaq Baltic stock exchange and compare their performance to global portfolios, which are formed from the stocks listed on the New York Stock Exchange. Investment portfolios were built using mean variance (MV) and Black–Litterman (BL) models. The analysis revealed that the returns of the portfolios built on the Nasdaq Baltic exchange are higher than the returns of the global portfolios. Additionally, the volatility of returns is lower for Nasdaq Baltic portfolios. When selected markets have different growth rates, investment decisions based on familiarity bias can achieve better results.
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Maryan, Jacek P. "From Portfolio Management to Portfolio Optimization Application Portfolio Management in the SOA Era." In 2009 IEEE Congress on Services (SERVICES). IEEE, 2009. http://dx.doi.org/10.1109/services-i.2009.112.
Rai, Veerendra K., B. Vijayasaradhi, K. Subramanian, and S. Umamaheswari. "ODC portfolio management." In 2010 4th Annual IEEE Systems Conference. IEEE, 2010. http://dx.doi.org/10.1109/systems.2010.5482474.
This document aims to explain the portfolio management contract. Portfolio Management Contract is constitutive of a mouth certain value of wealth and portfolio called is integrally managed. By the contract, the aim is that financier wealth value direct to market expectation investment, mainly in commerce. The contract usually forms through the transport of Securities and Exchange Commission Notices. Portfolio Management Companies, whose major business line is established and management and as be found incorporated company securities and exchange commission, stockbrokers and banks, which are nonaccedding deposits, constitute the part of the contract. Counterparty is individual or corporate financier. According to general principles of Obligations Law, the contract, which does not have any mandatory condition, depends on requirement of written form with regard to notice of this/the subject.
Remuneration is the essential component for Portfolio Management Contract, which has the characteristics of the anonymous contract. In this case, it has to be agreed on getting charge for servitude given by Portfolio Management Companies, stockbrokers and banks, which are nonaccedding deposits. The contract is aimed to commit the obligation with caution rather than extrapolating to a specific condition. In suitable conditions of primarily provisions of the contract of not against of this subject’s issue notices and in case of gaps, provisions of contract of mandate will be applied to the contract by comparison.
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Menke, M. "Product portfolio management in the context of enterprise portfolio management." In Proceedings of PICMET 2006-Technology Management for the Global Future. IEEE, 2006. http://dx.doi.org/10.1109/picmet.2006.296863.
Maknickienė, Nijolė, and Darius Sabaliauskas. "Investment portfolio analysis by using neural networks." In Contemporary Issues in Business, Management and Economics Engineering. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/cibmee.2019.028.
Purpose – the purpose of the article is to compare the formation of portfolios and to make predictions about how it will change. Research methodology – for analysis, optimization and predictions use the neural network models that are created using a neural recurrent long short-term memory cell architecture network and Markowitz’s modern portfolio theory Findings – this article compares the portfolios of IT field with different instruments and level of optimization. Research limitations – the main limit of the article is that only historical data is used. The real-time investment would check the performance of the portfolio creation methodology under uncertain conditions. Practical implications – the results of the article give opportunities for investors and speculators in the finance market by using neural networks for forming investment portfolios, as well as analysing and predicting their changes. Originality/Value – the growing high-tech use in financial markets changes our habits and our understanding of the surrounding world. The financial sphere has also had several changes, and it has undergone major changes that will change the approach to producing financial forecasts and analysis. Including Artificial Intelligence in these processes brings new innovative opportunities.
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Horlach, Bettina, Ingrid Schirmer, Tilo Böhmann, and Paul Drews. "Agile portfolio management patterns." In XP '18 Companion: 19th International Conference on Agile Software Development. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3234152.3234179.
Wojszczyk, B., and F. Katiraei. "Sustainable energy portfolio management." In Energy Society General Meeting. IEEE, 2008. http://dx.doi.org/10.1109/pes.2008.4596947.
Cunningham, Ward. "The WyCash portfolio management system." In Addendum to the proceedings. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/157709.157715.
Mitchell, Kenneth N. Coastal Navigation Portfolio Management. Fort Belvoir, VA: Defense Technical Information Center, February 2015. http://dx.doi.org/10.21236/ada622104.
Erdogan, E., D. Goldfarb, and G. Iyengar. Robust Active Portfolio Management. Fort Belvoir, VA: Defense Technical Information Center, November 2006. http://dx.doi.org/10.21236/ada478060.
Seo, Young-Woo, Joseph Giampapa, and Katia Sycara. Text Classification for Intelligent Portfolio Management. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada595830.
Freyman, Christina, Tulay Muezzinoglu, John Byrnes, Nikhil Kalathil, Daniel Querejazu, David Hart, and Alfred Sarkissian. Machine Learning for Solar Technology Portfolio Management. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1542861.
Seo, Young-Woo, Joseph Giampapa, and Katia Sycara. Financial News Analysis for Intelligent Portfolio Management. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada599073.
Dow, James, and Gary Gorton. Noise Trading, Delegated Portfolio Management, and Economic Welfare. Cambridge, MA: National Bureau of Economic Research, September 1994. http://dx.doi.org/10.3386/w4858.
Pope, Donald. Strategies and the management of a portfolio of business units. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.566.
de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Madrid: Banco de España, September 2023. http://dx.doi.org/10.53479/33560.
The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold) investment strategy in the construction of a duration management model for high-grade bond portfolios, specifically focusing on US treasury bonds. Additionally, a two-step procedure is proposed, together with a simple ensemble averaging aimed at minimising the potential overfitting of traditional machine learning algorithms. A method to select thresholds that translate probabilities into signals based on conditional probability distributions is also introduced.
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Glinsky, M. E. Diversification and strategic management of LLNL`s R&D portfolio. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/32373.
None, None. Building Technologies Program Multi-Year Program Plan Program Portfolio Management 2008. Office of Scientific and Technical Information (OSTI), January 2008. http://dx.doi.org/10.2172/1217878.
