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Piracy in the Gulf of Aden and the waters around Somalia has increased in recent years, with international naval assets allocated to patrol at-risk areas. This paper compares measures of area coverage in situations where there is a uniform piracy risk and where some areas are more vulnerable than others. Simulated annealing was used to allocate the patrolling naval assets. The novel problem of positioning a coalition of ships whilst incorporating the transit of a non-coalition ship that may respond to a piracy incident but can not be prepositioned is addressed.

Office of the State Treasurer Asset Allocation Compliance Report, February 28, 2023; asset allocation graph; investments; securities; deposits;

In this episode Scott Becker discusses personal asset allocation for Founders & CEOs of private equity backed companies.

Office of the State Treasurer Asset Allocation Compliance Report, July 31, 2020; asset allocation graph; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, February 28, 2025; asset allocation graph; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, February 28, 2025; asset allocation graph; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, September 30, 2024; asset allocation graph; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, September 30, 2024; asset allocation graph; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, September 30, 2024; asset allocation graph; investments; securities; deposits;

The USAF maintains a network of sensors for a variety of purposes including detecting threats, collecting intelligence, and monitoring space. Because of its nature, the network must be able to complete all its varying missions with consistently high probability. Thus, there is a need to assign the sensors to tasks and functions so as to maximize the network's capability to meet its objectives. While it is possible to determine the best allocation based on a total enumeration of potential sensor assignments, this is intractable for large problems.

The USAF maintains a network of sensors for a variety of purposes including detecting threats, collecting intelligence, and monitoring space. Because of its nature, the network must be able to complete all its varying missions with consistently high probability. Thus, there is a need to assign the sensors to tasks and functions so as to maximize the network's capability to meet its objectives. While it is possible to determine the best allocation based on a total enumeration of potential sensor assignments, this is intractable for large problems.

The USAF maintains a network of sensors for a variety of purposes including detecting threats, collecting intelligence, and monitoring space. Because of its nature, the network must be able to complete all its varying missions with consistently high probability. Thus, there is a need to assign the sensors to tasks and functions so as to maximize the network's capability to meet its objectives. While it is possible to determine the best allocation based on a total enumeration of potential sensor assignments, this is intractable for large problems.

This paper examines the optimal annuitization, investment and consumption strategies of a utility-maximizing retiree facing a stochastic time of death under a variety of institutional restrictions. We focus on the impact of aging on the optimal purchase of life annuities which form the basis of most Defined Benefit pension plans. Due to adverse selection, acquiring a lifetime payout annuity is an irreversible transaction that creates an incentive to delay. Under the institutional all-or-nothing arrangement where annuitization must take place at one distinct point in time (i.e. retirement), we derive the optimal age at which to annuitize and develop a metric to capture the loss from annuitizing prematurely. In contrast, under an open-market structure where individuals can annuitize any fraction of their wealth at anytime, we locate a general optimal annuity purchasing policy. In this case, we find that an individual will initially annuitize a lump sum and then buy annuities to keep wealth to one side of a separating ray in wealth-annuity space. We believe our paper is the first to integrate life annuity products into the portfolio choice literature while taking into account realistic institutional restrictions which are unique to the market for mortality-contingent claims.

When the available statistical information is imperfect, it is dangerous to follow standard optimisation procedures to construct an optimal portfolio, which usually leads to a strong concentration of the weights on very few assets. We propose a new way, based on generalised entropies, to ensure a minimal degree of diversification.

As fears over inflation and interest rates start to moderate, the case for taking on more risk may be rising. Christian Mueller-Glissmann, who heads asset allocation research in Goldman Sachs Research, and Alexandra Wilson-Elizondo, co-chief investment officer of the multi-asset solutions business in Goldman Sachs Asset Management, explain their outlooks for asset classes and portfolio strategies. 

As fears over inflation and interest rates start to moderate, the case for taking on more risk may be rising. Christian Mueller-Glissmann, who heads asset allocation research in Goldman Sachs Research, and Alexandra Wilson-Elizondo, co-chief investment officer of the multi-asset solutions business in Goldman Sachs Asset Management, explain their outlooks for asset classes and portfolio strategies. 

Approved For Relse 2005/03/24: CIA-R 000080030-3 SUBJECT: USIB Committee on Intelligence Resources and Asset Allocation -- 1 25X1 1. The Chairman opened the meeting with an explanation of the purpose for which the Committee had been convened -- a re- view of resources and asset allocation over the next five years in Indochina during the era of reducing U.S. presence. The en- suing discussion covered such subjects as Vietnamization, the problem of National versus Tactical/Military requirements, and the level of enemy activity, all of which contribute directly or indirectly to the problem at hand. All agreed that enemy capabilities and intentions coupled with our Allies' capabilities were of considerable importance in attempting to determine what the intelligence requirements of the future will be. The question was raised as to whether we will really need as much intelligence in the future as U.S. forces are withdrawn from the field. 2. The State Department representative stated that the Committee should develop a framework of intelligence require- ments which then could be reviewed by the Intelligence Community and the field. While the feeling was expressed that the status quo would have to be maintained regarding intelligence collection to support U.S. forces, it was necessary that specific require- ments be developed in place of the shot gun approach of the past. The consensus of the group was that it must be decided what it is we want -- what will the Intelligence Community be asked to col- lect in the future and at what level and what will be the time- liness required. 3. The Chairman asked that each principal draft his own interpretation of what will be needed in the future and that the submissions be received by 1200 on Wednesday. These sub- missions will be circulated among the group for review and comment. It is envisaged that the inputs will provide the material to develop a consolidated prospectus of what is needed and will be expected of the Intelligence Community. Mr. Tweedy asked that the principals keep in mind the various priorities involved regarding intelligence requirements, i.e., those re- quired by U.S. officials, both in Washington and in the field, to meet their day-to-day responsibilities, those necessary to Approved For Release 2005/03/24: CIA-R 01300080030-3 S`04~`f 25X1 Approved For Release 2005/03/24: CIA- assist our Allies and protect our forces during withdrawal, and those which will allow our Allies to protect themselves now and in the future. 25X1 25X1 Vietnamese Affairs Staff Related Activities Center Approved For Release 2005/03/24: CI - 720R001300080030-3 E WH 25X1

Perma.cc archive of http://www.ubs.com/global/en/assetmanagement/insights/thematic-viewpoints/sustainable-impact-investing/articles/asset-allocation-for-an-esg-world.html created on 2022-09-11 10:05:37.443287+00:00.

Thesis advisor, J.N. Eagle

Market volatility, inflation and positive correlations across assets have put a question mark on the diversification benefits of multi-asset portfolios. In the latest episode of Exchanges at Goldman Sachs, Goldman Sachs Research's Christian Mueller-Glissmann, who heads asset allocation research, breaks down what investors should keep in mind when building portfolios in 2023. 

Every rational economic decision maker would prefer to avoid a loss, to have benefits be greater than costs, to reduce risk, and to have investments gain value. Loss aversion refers to the tendency to loathe realizing a loss to the extent that you avoid it even when it is the better choice. How can it be rational for a loss to be the better choice? Say you buy stock for $100 per share. Six months later, the stock price has fallen to $63 per share. You decide not to sell the stock to avoid realizing the loss. If there is another stock with better earnings potential, however, your decision creates an opportunity cost. You pass up the better chance to increase value in the hopes that your original value will be regained. Your opportunity cost likely will be greater than the benefit of holding your stock, but you will do anything to avoid that loss. Loss aversion is an instance where a rational aversion leads you to underestimate a real cost, leading you to choose the lesser alternative. Aim of this paper is to identify the various factors which are affecting to the investment decision and behavioural finance Gobinda Dhamala, Khushboo Sharma, Kunal Jaiswal, Dimple Patel, Pooja Singh and Ritu Sinha 2020. A research study on investors behaviour regarding choice of asset allocation of teaching staff. International Journal on Integrated Education. 3, 3 (Mar. 2020), 126-135. DOI:https://doi.org/10.31149/ijie.v3i3.298 Pdf Url : https://journals.researchparks.org/index.php/IJIE/article/view/298/291 Paper Url : https://journals.researchparks.org/index.php/IJIE/article/view/298

This research involves creating an efficient portfolio construction that aims to guide the retail investors about the significance of the data-driven decision-making using the analytical tool Python, especially for financial securities investments with a focus on mutual funds. A dataset comprising necessary information on nearly 625 mutual fund schemes from the dataset obtained from Kaggle has been utilized for analysis and study. The study focuses on applying the modern portfolio theory for portfolio construction proposed by Markowitz, which is a very popular financial theory, in real-world investment strategy in the case of constructing a mutual fund portfolio with an adjusted risk-return tradeoff. The methodology relies on the construction of a portfolio with modern portfolio theory concepts and predicting the possible outcomes of the portfolio with the Monte Carlo simulation technique by running the codes in Python and constructing two distinct portfolios: one with diversified and lower risk, comprising 15 mutual fund schemes for conservative investors, and another with minimum compromised risk, comprising 5 mutual fund schemes, which achieves a higher return than the previous one. The parameters taken for the choice of selecting the schemes from the data set are based on the renowned ones such as the Sharpe Ratio and Sortino Ratio. The findings reveal that the 15 schemes portfolio returns are in the range of 10% and 12% with risk levels between 1.5% and 2.5%, and the 5 schemes portfolio returns are in the range of 15% and 17% with risk levels between 4% and 4.5%. The optimum weights to be invested in each scheme to achieve maximum return at the lowest possible risk are also mentioned in proportion for both the portfolios. The findings can be interpreted in a way that the construction of a portfolio with rational decisions backed by data is more appropriate in the modern world with the availability of analytical tools such as Python for forecasting and predicting the potential return and constructing the portfolio based on that by minimizing risks with the traditional theories, which can be efficiently and easily used with technology.

In this episode I speak with Jim Masturzo, Head of Asset Allocation at Research Affiliates. In his role, Jim oversees the research and publication of the firm's capital market assumptions as well as the implementation of those views into a suite of tactical portfolios. We begin our conversation discussing the foundational assumptions behind the capital market assumptions. Like most firms, Research Affiliates takes a long-term view on return and risk. In line with the firm's guiding philosophy, they also introduce long-term mean reversionary effects. Not surprisingly, these assumptions have been relatively bearish on U.S. equity returns for a large part of the last decade, and we discuss how to view the dispersion between these model forecasts and realized results. We then shift our conversation to the application of tactical views. With capital market assumptions serving as the strategic backbone, Jim and his team develop a number of regime-based model portfolios that can be blended to express different tactical views. But the team does not take a purely quantitative approach. Jim proactively acknowledges and seeks out model blindness. Rather than try to force idiosyncratic fixes into the models that might bias results, however, he and his team adopt qualitative trades to adapt the portfolios. From strategic to tactical and quantitative to qualitative, this is a wide ranging conversation all about asset allocation. I hope you enjoy.

In this episode I speak with Jim Masturzo, Head of Asset Allocation at Research Affiliates. In his role, Jim oversees the research and publication of the firm's capital market assumptions as well as the implementation of those views into a suite of tactical portfolios. We begin our conversation discussing the foundational assumptions behind the capital market assumptions. Like most firms, Research Affiliates takes a long-term view on return and risk. In line with the firm's guiding philosophy, they also introduce long-term mean reversionary effects. Not surprisingly, these assumptions have been relatively bearish on U.S. equity returns for a large part of the last decade, and we discuss how to view the dispersion between these model forecasts and realized results. We then shift our conversation to the application of tactical views. With capital market assumptions serving as the strategic backbone, Jim and his team develop a number of regime-based model portfolios that can be blended to express different tactical views. But the team does not take a purely quantitative approach. Jim proactively acknowledges and seeks out model blindness. Rather than try to force idiosyncratic fixes into the models that might bias results, however, he and his team adopt qualitative trades to adapt the portfolios. From strategic to tactical and quantitative to qualitative, this is a wide ranging conversation all about asset allocation. I hope you enjoy.

In this episode I speak with Jim Masturzo, Head of Asset Allocation at Research Affiliates. In his role, Jim oversees the research and publication of the firm's capital market assumptions as well as the implementation of those views into a suite of tactical portfolios. We begin our conversation discussing the foundational assumptions behind the capital market assumptions. Like most firms, Research Affiliates takes a long-term view on return and risk. In line with the firm's guiding philosophy, they also introduce long-term mean reversionary effects. Not surprisingly, these assumptions have been relatively bearish on U.S. equity returns for a large part of the last decade, and we discuss how to view the dispersion between these model forecasts and realized results. We then shift our conversation to the application of tactical views. With capital market assumptions serving as the strategic backbone, Jim and his team develop a number of regime-based model portfolios that can be blended to express different tactical views. But the team does not take a purely quantitative approach. Jim proactively acknowledges and seeks out model blindness. Rather than try to force idiosyncratic fixes into the models that might bias results, however, he and his team adopt qualitative trades to adapt the portfolios. From strategic to tactical and quantitative to qualitative, this is a wide ranging conversation all about asset allocation. I hope you enjoy.

In this episode I speak with Jim Masturzo, Head of Asset Allocation at Research Affiliates. In his role, Jim oversees the research and publication of the firm's capital market assumptions as well as the implementation of those views into a suite of tactical portfolios. We begin our conversation discussing the foundational assumptions behind the capital market assumptions. Like most firms, Research Affiliates takes a long-term view on return and risk. In line with the firm's guiding philosophy, they also introduce long-term mean reversionary effects. Not surprisingly, these assumptions have been relatively bearish on U.S. equity returns for a large part of the last decade, and we discuss how to view the dispersion between these model forecasts and realized results. We then shift our conversation to the application of tactical views. With capital market assumptions serving as the strategic backbone, Jim and his team develop a number of regime-based model portfolios that can be blended to express different tactical views. But the team does not take a purely quantitative approach. Jim proactively acknowledges and seeks out model blindness. Rather than try to force idiosyncratic fixes into the models that might bias results, however, he and his team adopt qualitative trades to adapt the portfolios. From strategic to tactical and quantitative to qualitative, this is a wide ranging conversation all about asset allocation. I hope you enjoy.

In this episode I speak with Jim Masturzo, Head of Asset Allocation at Research Affiliates. In his role, Jim oversees the research and publication of the firm's capital market assumptions as well as the implementation of those views into a suite of tactical portfolios. We begin our conversation discussing the foundational assumptions behind the capital market assumptions. Like most firms, Research Affiliates takes a long-term view on return and risk. In line with the firm's guiding philosophy, they also introduce long-term mean reversionary effects. Not surprisingly, these assumptions have been relatively bearish on U.S. equity returns for a large part of the last decade, and we discuss how to view the dispersion between these model forecasts and realized results. We then shift our conversation to the application of tactical views. With capital market assumptions serving as the strategic backbone, Jim and his team develop a number of regime-based model portfolios that can be blended to express different tactical views. But the team does not take a purely quantitative approach. Jim proactively acknowledges and seeks out model blindness. Rather than try to force idiosyncratic fixes into the models that might bias results, however, he and his team adopt qualitative trades to adapt the portfolios. From strategic to tactical and quantitative to qualitative, this is a wide ranging conversation all about asset allocation. I hope you enjoy.

In this episode I speak with Jim Masturzo, Head of Asset Allocation at Research Affiliates. In his role, Jim oversees the research and publication of the firm's capital market assumptions as well as the implementation of those views into a suite of tactical portfolios. We begin our conversation discussing the foundational assumptions behind the capital market assumptions. Like most firms, Research Affiliates takes a long-term view on return and risk. In line with the firm's guiding philosophy, they also introduce long-term mean reversionary effects. Not surprisingly, these assumptions have been relatively bearish on U.S. equity returns for a large part of the last decade, and we discuss how to view the dispersion between these model forecasts and realized results. We then shift our conversation to the application of tactical views. With capital market assumptions serving as the strategic backbone, Jim and his team develop a number of regime-based model portfolios that can be blended to express different tactical views. But the team does not take a purely quantitative approach. Jim proactively acknowledges and seeks out model blindness. Rather than try to force idiosyncratic fixes into the models that might bias results, however, he and his team adopt qualitative trades to adapt the portfolios. From strategic to tactical and quantitative to qualitative, this is a wide ranging conversation all about asset allocation. I hope you enjoy.

In this episode I speak with Jim Masturzo, Head of Asset Allocation at Research Affiliates. In his role, Jim oversees the research and publication of the firm's capital market assumptions as well as the implementation of those views into a suite of tactical portfolios. We begin our conversation discussing the foundational assumptions behind the capital market assumptions. Like most firms, Research Affiliates takes a long-term view on return and risk. In line with the firm's guiding philosophy, they also introduce long-term mean reversionary effects. Not surprisingly, these assumptions have been relatively bearish on U.S. equity returns for a large part of the last decade, and we discuss how to view the dispersion between these model forecasts and realized results. We then shift our conversation to the application of tactical views. With capital market assumptions serving as the strategic backbone, Jim and his team develop a number of regime-based model portfolios that can be blended to express different tactical views. But the team does not take a purely quantitative approach. Jim proactively acknowledges and seeks out model blindness. Rather than try to force idiosyncratic fixes into the models that might bias results, however, he and his team adopt qualitative trades to adapt the portfolios. From strategic to tactical and quantitative to qualitative, this is a wide ranging conversation all about asset allocation. I hope you enjoy.

In this episode I speak with Jim Masturzo, Head of Asset Allocation at Research Affiliates. In his role, Jim oversees the research and publication of the firm's capital market assumptions as well as the implementation of those views into a suite of tactical portfolios. We begin our conversation discussing the foundational assumptions behind the capital market assumptions. Like most firms, Research Affiliates takes a long-term view on return and risk. In line with the firm's guiding philosophy, they also introduce long-term mean reversionary effects. Not surprisingly, these assumptions have been relatively bearish on U.S. equity returns for a large part of the last decade, and we discuss how to view the dispersion between these model forecasts and realized results. We then shift our conversation to the application of tactical views. With capital market assumptions serving as the strategic backbone, Jim and his team develop a number of regime-based model portfolios that can be blended to express different tactical views. But the team does not take a purely quantitative approach. Jim proactively acknowledges and seeks out model blindness. Rather than try to force idiosyncratic fixes into the models that might bias results, however, he and his team adopt qualitative trades to adapt the portfolios. From strategic to tactical and quantitative to qualitative, this is a wide ranging conversation all about asset allocation. I hope you enjoy.

This item represents a case in PACER, the U.S. Government's website for federal case data. If you wish to see the entire case, please consult PACER directly.

Office of the State Treasurer Asset Allocation Compliance Report, August 30, 2024; asset allocation graph; investments; securities; deposits;

In the business of banking, banks do not only invest the received deposits to meet future obligations but also to make a profit. The study investigates the effect of asset allocation on profitability Deposit Money Banks in Nigeria. The study adopted a panel regression model involving five selected commercial banks. The data were obtained from the financial statement and annual reports of the selected banks spanning 2011 to 2015. The explanatory variables of the study are liquid assets, equities, loans, securities. The panel Ordinary Least Square regression was employed for data analyses. The findings have shown that aggregate asset allocation variable significantly explains 54% of changes in bank profits. Further results shows that liquid assets, investments equities have positive but insignificant effect on profitability of Deposit Money Banks in Nigeria; loan extension of the banks have insignificant negative effect on profitability of Deposit Money Banks in Nigeria; whereas investments in security have significant positive effect on the profitability of Deposit Money Banks in Nigeria. The study thus concluded that asset allocation is an important financial management tool to enhancing bank profitability, and that investments in securities are veritable channel for banks to improve profitability in Nigeria. Gloria Obiageli Izundu | Clem Nwakoby | Cyprian Okey Okoro | Udoka Bernard Alajekwu*"Asset Allocation as Determinant of Bank Profitability in Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2278.pdf Article URL: http://www.ijtsrd.com/economics/finance/2278/asset-allocation-as-determinant-of-bank-profitability-in-nigeria/gloria-obiageli-izundu

In the business of banking, banks do not only invest the received deposits to meet future obligations but also to make a profit. The study investigates the effect of asset allocation on profitability Deposit Money Banks in Nigeria. The study adopted a panel regression model involving five selected commercial banks. The data were obtained from the financial statement and annual reports of the selected banks spanning 2011 to 2015. The explanatory variables of the study are liquid assets, equities, loans, securities. The panel Ordinary Least Square regression was employed for data analyses. The findings have shown that aggregate asset allocation variable significantly explains 54% of changes in bank profits. Further results shows that liquid assets, investments equities have positive but insignificant effect on profitability of Deposit Money Banks in Nigeria; loan extension of the banks have insignificant negative effect on profitability of Deposit Money Banks in Nigeria; whereas investments in security have significant positive effect on the profitability of Deposit Money Banks in Nigeria. The study thus concluded that asset allocation is an important financial management tool to enhancing bank profitability, and that investments in securities are veritable channel for banks to improve profitability in Nigeria. Gloria Obiageli Izundu | Clem Nwakoby | Cyprian Okey Okoro | Udoka Bernard Alajekwu*"Asset Allocation as Determinant of Bank Profitability in Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2278.pdf Article URL: http://www.ijtsrd.com/economics/finance/2278/asset-allocation-as-determinant-of-bank-profitability-in-nigeria/gloria-obiageli-izundu

Every rational economic decision maker would prefer to avoid a loss, to have benefits be greater than costs, to reduce risk, and to have investments gain value. Loss aversion refers to the tendency to loathe realizing a loss to the extent that you avoid it even when it is the better choice. How can it be rational for a loss to be the better choice? Say you buy stock for $100 per share. Six months later, the stock price has fallen to $63 per share. You decide not to sell the stock to avoid realizing the loss. If there is another stock with better earnings potential, however, your decision creates an opportunity cost. You pass up the better chance to increase value in the hopes that your original value will be regained. Your opportunity cost likely will be greater than the benefit of holding your stock, but you will do anything to avoid that loss. Loss aversion is an instance where a rational aversion leads you to underestimate a real cost, leading you to choose the lesser alternative. Aim of this paper is to identify the various factors which are affecting to the investment decision and behavioural finance Gobinda Dhamala, Khushboo Sharma, Dimple Patel and Kunal Jaiswal 2020. A research study on investors behaviour regarding choice of asset allocation of teaching staff. International Journal on Integrated Education. 3, 2 (Feb. 2020), 68-77. DOI:https://doi.org/10.31149/ijie.v3i2.16 Pdf Url : https://journals.researchparks.org/index.php/IJIE/article/view/16/16 Paper Url : https://journals.researchparks.org/index.php/IJIE/article/view/16

Office of the State Treasurer Asset Allocation Compliance Report, November 30, 2024; asset allocation graph; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, March 31, 2025; asset allocation graph; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, March 31, 2025; asset allocation graph; investments; securities; deposits;

Assume (1) asset returns follow a stochastic multi-factor process with time-varying conditional expectations; (2) investments are linear functions of factors. This paper calculates asymptotic joint moments of the logarithm of investor's wealth and the factors. These formulas enable fast computation of a wide range of investment criteria. The results are illustrated by a numerical example that shows that the optimal portfolio rules are sensitive to the specification of the investment criterion.

Motivated by the Navy's emphasis on networked planning capabilities in maritime operations centers (MOC), we have developed an agent-based multi-level resource allocation model that takes high level commands from the human planners and then dynamically allocates the lower-level assets and processes tasks to accomplish the mission objectives. The agent-based model supports a controllable, multi-player, real time collaborative planning environment in a Windows environment. The architecture allows for adding constraints on and manipulations of organizational structures, such as authority, information, communication, resource ownership, task assignment, as well as mission and environmental structures. The planning problem is formulated as a multi-level optimization problem of minimizing the overall difference between the human specified performance measures and expected performance measures which are evaluated based on how well the assigned resources match the required resources, subject to a number of real-world planning constraints on assets. We applied a Dynamic List Planning algorithm (DLP) to solve the intractable multi-level resource allocation problem. The near-optimal DLP method can generate high-quality solutions in seconds compared to days taken by the branch-and-bound-based search methods.

Motivated by the Navy's emphasis on networked planning capabilities in maritime operations centers (MOC), we have developed an agent-based multi-level resource allocation model that takes high level commands from the human planners and then dynamically allocates the lower-level assets and processes tasks to accomplish the mission objectives. The agent-based model supports a controllable, multi-player, real time collaborative planning environment in a Windows environment. The architecture allows for adding constraints on and manipulations of organizational structures, such as authority, information, communication, resource ownership, task assignment, as well as mission and environmental structures. The planning problem is formulated as a multi-level optimization problem of minimizing the overall difference between the human specified performance measures and expected performance measures which are evaluated based on how well the assigned resources match the required resources, subject to a number of real-world planning constraints on assets. We applied a Dynamic List Planning algorithm (DLP) to solve the intractable multi-level resource allocation problem. The near-optimal DLP method can generate high-quality solutions in seconds compared to days taken by the branch-and-bound-based search methods.

Phil Bloyd, President & Founder Revolutionary Financial Group 843-941-7791 http://www.RevolutionaryFinancial.com [email protected] 25 Clarks Summit Dr, Suite F-102, Bluffton, SC Lowcountry Money Talk Hosted by Jessa Jeremiah January-February 2022 WHHI Television: Your Hyperlocal South Carolina Television Station Serving the Lowcountry! Hilton Head Island, SC - Bluffton, SC - Beaufort, SC - Pooler, GA Beaufort County - Jasper County - Hampton County WHHI-TV. Local News. Local Sports. Local Television. Live Local. Spectrum Channel 3 & 1230 - Hargray Channel 8 & 418 843-785-4545 http://www.WHHITV.com http://www.Facebook.com/WHHITV http://www.Twitter.com/WHHITV [email protected] 32 Office Park Rd, Hilton Head Island, SC 29928

Macro-based asset allocation is based on the identification of turning points in macro-financial cycles. This paper suggests that macro-based asset allocation informed by trends in continuous business and financial cycle indicators could be a promising alternative for medium- and long-term investment strategies. Despite changes during the last three decades, the most promising specifications of this approach did roughly anticipate turning points in asset price cycles, implying favourable returns and low portfolio volatility.

Macro-based asset allocation is based on the identification of turning points in macro-financial cycles. This paper suggests that macro-based asset allocation informed by trends in continuous business and financial cycle indicators could be a promising alternative for medium- and long-term investment strategies. Despite changes during the last three decades, the most promising specifications of this approach did roughly anticipate turning points in asset price cycles, implying favourable returns and low portfolio volatility.

This item represents a case in PACER, the U.S. Government's website for federal case data. If you wish to see the entire case, please consult PACER directly.

This item represents a case in PACER, the U.S. Government's website for federal case data. If you wish to see the entire case, please consult PACER directly.

This item represents an oral argument audio file as scraped from a U.S. Government website by Free Law Project.