Optimal deviations from an asset allocation

Gratcheva, Ekaterina M.; Falk, James E.

doi:10.1016/s0305-0548(02)00097-7

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…It is observed that the tangency portfolio provides an optimum solution to the asset allocation problem most investors are faced with, on how much to allocate to each asset or market in order to maximize their wealth at the possible minimum risk (Zhu and Zhou, 2009;Gratcheva and Falk, 2003). This is because this portfolio offers the best blend of risky securities that gives the best risk-expected return trade-off on a portfolio (Xie, 2009;Jorion 1992).…”

Section: Resultsmentioning

confidence: 99%

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Asset Allocation Brewed Accross African Stock Markets

Mensah¹

2016

Proceedings of the 5th Economic &Amp; Finance Conference, Miami

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Section: Resultsmentioning

confidence: 99%

“…Hence, this study employs a static asset allocation. The basic allocation problem is to decide which asset classes to include in a portfolio and in what proportions (Gratcheva and Falk, 2003). This is because the asset allocation decision has a cumulative influence on the portfolios overall performance than any other decision.…”

Section: Introductionmentioning

confidence: 99%

Asset Allocation Brewed Accross African Stock Markets

Mensah¹

2016

Proceedings of the 5th Economic &Amp; Finance Conference, Miami

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“…Another commonly adopted measure of effectiveness for transit resource allocation is in regard to monetary units. The performance measures frequently used in the literature are maximization of revenue, return, or profits; benefit-to-cost ratio; internal rate of return; payoff period; and cost-effectiveness (17)(18)(19)(20)(21)(22)(23). NPC is a widely understood and used measure of effectiveness in transportation decision making.…”

Section: Quantitative Measures For Fleet Managementmentioning

confidence: 99%

Multiobjective Optimization Model for Transit Fleet Resource Allocation

Mishra

Sharma

Mathew

et al. 2013

Transportation Research Record: Journal of the Transportation R

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State and local transit agencies require government support to preserve their aging transit fleets. With the passage of time, transit fleets get older and require maintenance costs to keep them operational. To provide services at a desired level, transit agencies must maintain a minimum fleet size. Two imperative considerations from the transit planning viewpoint are (a) the remaining life of the total fleet and (b) the cost required to maintain the fleet size. While the former is a quality measure indicating the health of the fleet, the latter is an economic measure requiring minimum expenditure levels. Ideally, agencies would like to maximize the total remaining life of the fleet and minimize the total cost required to maintain the fleet size. In this paper, a multiobjective optimization (MO) model is proposed to incorporate simultaneously the two objectives when subjected to budget and various operational constraints. The MO problem is solved with a classical weight sum approach by using the branch and bound algorithm, which has proved to be better than other solution methodologies. The MO results in Pareto-optimal solutions with possible trade-offs between the two objectives. The model is applied to a large-scale transit fleet system in the state of Michigan. The case study results demonstrate that the proposed model is compact, efficient, robust, and suitable for long-range planning with multiple solutions to choose from a Pareto-optimal frontier. The correlation between decision variables and objective functions has been investigated in-depth, providing important insights. The proposed model can act as a tool for resource allocation for state and local agency transit fleets.

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“…74 71 See in particular Black (1986), whose contribution on noise initiated a major stream of research. 72 See Farrell, Jr. (1997) and Gratcheva and Falk (2003), but also Lettau and Uhlig (1999) and Cesari and Cremonini (2003). 73 See Campbell (2000) 74 See also Stracca (2004) and van der Saar (2004).…”

Section: Limitations Of the Mptmentioning

confidence: 99%

Portfolio Management with Heuristic Optimization

2005

Advances in Computational Management Science

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All Rights Reserved © 2005 SpringerNo part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. PrefaceManaging financial portfolios is primarily concerned with finding a combination of assets that serves an investor's needs and demands the best. This includes a wide range of aspects such as the analysis of the investor's attitude towards risk, expected return and consumption; estimations of future payoffs of the financial securities and the risk associated with it have to be made; assessing the relationships between securities; determining fair prices for these securities -and finding an optimal combination of financial securities. Many of these tasks are interrelated: what is an optimal combination depends on the investor's preferences as well as on the properties of the assets, which, in return, will affect what is considered a fair price and vice versa.The usual (theoretical) frameworks for portfolio management and portfolio optimization assume markets to be frictionless. Though it drives the models away from reality, this assumption has long been considered the only way to make these models approachable. However, with the advent of a new type of optimization and search techniques, heuristic optimization, more complex scenarios and settings can be investigated and many of these simplifying assumptions are no longer necessary.This book is merely concerned with problems in portfolio management when there are market frictions and when there are no ready-made solutions available. For this purpose, the first two chapters present the foundations for portfolio management and new optimization techiques. In the subsequent chapters, financial models will be enhanced by problems and aspects faced in real-life such as transaction costs, indivisible assets, limits on the number of assets, alternative risk measures and descriptions of the returns' distributions, and so on. For each of these enhanced problems, a detailed presentation of the model will be followed by a description of how it can be approached with heuristic optimization. Next, the suggested approaches will be applied to empirical studies and the conclusions for financial theory will be discussed. vi Preface Non-technical SummaryThe theoretical foundation to portfolio management as we know it today was laid by Harry M. Markowitz by stating a parametric optimization model. The gist of this model is to split the portfolio selection process into two steps where first the set of optimal portfolios is determined and then the investor chooses from this set that portfolio that suits her best. Markowitz's approach therefore includes (i) measuring the expected return and risk of the available assets (independently of the inves...

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Optimal deviations from an asset allocation

Cited by 8 publications

References 15 publications

Asset Allocation Brewed Accross African Stock Markets

Asset Allocation Brewed Accross African Stock Markets

Multiobjective Optimization Model for Transit Fleet Resource Allocation

Portfolio Management with Heuristic Optimization

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