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In this episode Scott Becker discusses the importance of learning to live with your asset allocation.

In this episode Scott Becker discusses the importance of learning to live with your asset allocation.

In this episode Scott Becker discusses the importance of learning to live with your asset allocation.

The primary objective of this thesis is to analyze and recommend improvements to the Maritime Patrol Aircraft (MPA) detection and allocation models utilized by the ASW Systems Evaluation Tool (ASSET), version 1.0. ASSET is a generic high-level ASW modeling tool, designed to aid CNO (OP-71) in the development and refinement of ASW top-level warfare requirements and the ASW master Plan. ASSET's strengths lie in its C3I modeling of submarine, MPA, and overhead surveillance in large scale ASW campaigns. To reduce the processing time required by ASSET, the current version of the MPA detection model contains simplifications which can limit its ability to effectively simulate some MPA tactical ASW scenarios. This thesis proposes two new MPA detection models which utilize the coverage area of a user-defined sonobuoy pattern and address the limitations of the current ASSET model. Also proposed is an MPA allocation scheme which should provide a higher cumulative detection probability.

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

Includes index

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

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

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;

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, July 31, 2024; asset allocation graph; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, July 31, 2024; asset allocation graph; investments; securities; deposits;

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.

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. 

CFA L3

My work consists of a comparative study of the performances of the multivariate regime switching model against the single regime model in terms of portfolio returns in the context of dynamic asset allocation. The study was conducted through the practical application, both in-sample and out of-sample, of the two models under various portfolio optimization approaches. In the first part of the asset allocation exercise I constructed for any asset pricing model, both in-sample and out-of-sample, two dynamic recursive efficient portfolios that maximize the Sharpe among portfolios on the efficient frontier (one with opened budget constraint that permits between 0% and 100% in the riskless asset, one whose weights must sum to 1); in addition short selling, thus negative asset class weights, is not allowed. The other three dynamic recursive portfolios that I constructed have been chosen as those that maximize the investor utility function with three different risk aversion coefficient subject to non-negative weights and opened upper budget constraint. The second part of the asset allocation exercise focuses only on the out-of-sample period. Here the Copula-Opinion Pooling approach is applied to implement in the asset pricing model views on the asset returns produced by both the single regime model and the regime switching model. The purpose of this section is to investigate and make a comparison of the behavior of the regime switching model and the single state model in the COP framework in terms of both expected and realized portfolio returns and Sharpe ratio in the context of mean-variance and conditional value-at-risk (CVaR) portfolio optimization. Therefore, in addition to the five recursive optimal portfolios chosen with the same portfolio selection process as in the first part, here using conditional value-at-risk as the risk exposure constraint, I derived the dynamic optimal weights of other five different portfolios equally distributed, in terms of CVaR, along the time dependent efficient frontier for different values of the confidence in the views. The over-performance can be achieved by the more efficient and desirable risk-reward combinations on the state-dependent frontier that can be obtained only by systematically altering portfolio allocations in response to changes in the investment opportunities as the economy switches back and forth among different states. An investor who ignores regimes sits on the unconditional frontier, thus an investor can do better by holding a higher Sharpe ratio portfolio when the low volatility regime prevails. Conversely, when the bad regime occurs, the investor who ignores regimes holds too high a risky asset weight. She would have been better off shifting into the risk-free asset when the bear regime hits. As a consequence, the presence of two regimes and two frontiers means that the regime switching investment opportunity set dominates the investment opportunity set offered by one frontier.

Office of the State Treasurer Asset Allocation Compliance Report, May 30, 2025; asset allocation graph; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, December 31, 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.

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. 

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  http://www.ijtsrd.com/economics/finance/2278/asset-allocation-as-determinant-of-bank-profitability-in-nigeria/gloria-obiageli-izundu

304 p

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.

It is well known that quantile regression model minimizes the portfolio extreme risk, whenever the attention is placed on the estimation of the response variable left quantiles. We show that, by considering the entire conditional distribution of the dependent variable, it is possible to optimize different risk and performance indicators. In particular, we introduce a risk-adjusted profitability measure, useful in evaluating financial portfolios under a pessimistic perspective, since the reward contribution is net of the most favorable outcomes. Moreover, as we consider large portfolios, we also cope with the dimensionality issue by introducing an l1-norm penalty on the assets weights.

It is well known that quantile regression model minimizes the portfolio extreme risk, whenever the attention is placed on the estimation of the response variable left quantiles. We show that, by considering the entire conditional distribution of the dependent variable, it is possible to optimize different risk and performance indicators. In particular, we introduce a risk-adjusted profitability measure, useful in evaluating financial portfolios under a pessimistic perspective, since the reward contribution is net of the most favorable outcomes. Moreover, as we consider large portfolios, we also cope with the dimensionality issue by introducing an l1-norm penalty on the assets weights.

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 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.

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.

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.

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.

SECRET? Appro''d For Release 2005/03/24: CIA-RDP8 01720R001300080022-2 MEMORANDUM FOR THE RECORD SUBJECT: USIB Committee on Intelligence Resources and Asset Allocation -- 2 1. The Chairman opened the meeting by stating that the independently prepared inputs from each of the principals had been received and circulated among the group. As expected, there was a fairly high level of duplication in the require- ments submitted, but there was a sufficient degree of diver- sions to make the inputs worthwhile. He stated that he felt a consolidated listing of the requirements should be prepared for dissemination to Dr. Kissinger, Secretary Rogers, Secretary Laird, and Admiral Moorer for their decision as to whether the Committee was on the right track. 2. The State Department representative indicated that, as expected, the agencies' inputs reflected the parochial views of each agency, i.e., DIA reacted as a collector/consumer; CIA as a collector/consumer; State as a consumer; and NSA as a collector. None of the inputs addressed squarely the problem of reducing U.S. presence and its effects on collection capa- bilities. All requested the same level of requirements, when in actuality there could be a diminution of. requirements as U.S. forces withdraw. The Chairman allowed that there probably would be no diminishing of intelligence of interest, as our masters will still want the same coverage. 3. The CIA representative agreed that there still will be a high level of requirements required, but felt that the mix of requirements will change and that it will be necessary to refine the priorities involved. He felt that it will be necessary to further refine our collection priorities -- a sharper focus will be needed or significant short falls will occur. In his view, intelligence forces will have to be the last to go from the theater. 4. The DIA representative agreed that refinements of priorities are necessary, but that it was difficult to define where the cuts should come. He noted that the reduced U.S. presence will reduce organic collection capability as far as the U.S. military was concerned. The Chairman allowed that this will lead to increased dependence on NSA and CIA for collection of the priority intelligence and that a clear definition of what they are responsible for must be made. Approved For Release 2005/03/24: CIA-RDP80R01720R001300080022-2 S CP TI 25X1 25X1, 25X1 Vietnamese Affairs Staff Related Activities Center Approd For Release 2005/03/24: CIA-RDP801720R001300080022-2 6. With the concurrence of the Committee, the Chairman proposed that the set of general requirements would be pre- pared for forwarding to the senior officials concerned with a covering memorandum from the DCI bringing them up to date as to the status of the Committee's deliberations. A draft of the memorandum will be circulated among the principals for their concurence and then will be forwarded to the appropriate officials for their approval. Upon receipt of this approval, the Committee will proceed to the next phases of the exercise -- the refinement of the requirements, the assignment of priorities, the tasking of appropriate collection assets, and the recommen- dation for policy approval regarding personnel and facility re- quirements to enable the meeting of the anticipated goals. Approved For Release 2005/03/24: CIA-RDP80RO172OR001300080022-2 25X1 25X1

The "Art of Investing" discussion group meets every Monday at Chautauqua Institution during the nine week summer season. Dennis Galucki leads a group discussion as part of the "Buffalo-Chautauqua Idea" sponsored by C-SAAHN, The Center for the Study of Art, Architecture, History & Nature. The group explores the various investment disciplines used by attendees. Attempting to invest in a sustainable world through individual companies is the central theme of the discussions. Four key topics of discussion for individual investors are: 1. "Know thyself" and what risk/reward you want. 2 Develop a strategy, philosophy, asset allocation and disciplines to get what you want. 3 See yourself as a lifelong learner and student of the market. 4 Decide how to measure success and/or failure often - even daily. The 2nd week discussion focused on the discipline of asset allocation.

The "Art of Investing" discussion group meets every Monday at Chautauqua Institution during the nine week summer season. Dennis Galucki leads a group discussion as part of the "Buffalo-Chautauqua Idea" sponsored by C-SAAHN, The Center for the Study of Art, Architecture, History & Nature. The group explores the various investment disciplines used by attendees. Attempting to invest in a sustainable world through individual companies is the central theme of the discussions. Four key topics of discussion for individual investors are: 1. "Know thyself" and what risk/reward you want. 2 Develop a strategy, philosophy, asset allocation and disciplines to get what you want. 3 See yourself as a lifelong learner and student of the market. 4 Decide how to measure success and/or failure often - even daily. The 2nd week discussion focused on the discipline of asset allocation.

The "Art of Investing" discussion group meets every Monday at Chautauqua Institution during the nine week summer season. Dennis Galucki leads a group discussion as part of the "Buffalo-Chautauqua Idea" sponsored by C-SAAHN, The Center for the Study of Art, Architecture, History & Nature. The group explores the various investment disciplines used by attendees. Attempting to invest in a sustainable world through individual companies is the central theme of the discussions. Four key topics of discussion for individual investors are: 1. "Know thyself" and what risk/reward you want. 2 Develop a strategy, philosophy, asset allocation and disciplines to get what you want. 3 See yourself as a lifelong learner and student of the market. 4 Decide how to measure success and/or failure often - even daily. The 2nd week discussion focused on the discipline of asset allocation.

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, March 31, 2025; asset allocation graph; investments; securities; deposits;

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, February 28, 2023; asset allocation graph; investments; securities; deposits;

Markowitz (1952, 1959) laid down the ground-breaking work on the mean-variance analysis. Under his framework, the theoretical optimal allocation vector can be very different from the estimated one for large portfolios due to the intrinsic difficulty of estimating a vast covariance matrix and return vector. This can result in adverse performance in portfolio selected based on empirical data due to the accumulation of estimation errors. We address this problem by introducing the gross-exposure constrained mean-variance portfolio selection. We show that with gross-exposure constraint the theoretical optimal portfolios have similar performance to the empirically selected ones based on estimated covariance matrices and there is no error accumulation effect from estimation of vast covariance matrices. This gives theoretical justification to the empirical results in Jagannathan and Ma (2003). We also show that the no-short-sale portfolio is not diversified enough and can be improved by allowing some short positions. As the constraint on short sales relaxes, the number of selected assets varies from a small number to the total number of stocks, when tracking portfolios or selecting assets. This achieves the optimal sparse portfolio selection, which has close performance to the theoretical optimal one. Among 1000 stocks, for example, we are able to identify all optimal subsets of portfolios of different sizes, their associated allocation vectors, and their estimated risks. The utility of our new approach is illustrated by simulation and empirical studies on the 100 Fama-French industrial portfolios and the 400 stocks randomly selected from Russell 3000.

Markowitz (1952, 1959) laid down the ground-breaking work on the mean-variance analysis. Under his framework, the theoretical optimal allocation vector can be very different from the estimated one for large portfolios due to the intrinsic difficulty of estimating a vast covariance matrix and return vector. This can result in adverse performance in portfolio selected based on empirical data due to the accumulation of estimation errors. We address this problem by introducing the gross-exposure constrained mean-variance portfolio selection. We show that with gross-exposure constraint the theoretical optimal portfolios have similar performance to the empirically selected ones based on estimated covariance matrices and there is no error accumulation effect from estimation of vast covariance matrices. This gives theoretical justification to the empirical results in Jagannathan and Ma (2003). We also show that the no-short-sale portfolio is not diversified enough and can be improved by allowing some short positions. As the constraint on short sales relaxes, the number of selected assets varies from a small number to the total number of stocks, when tracking portfolios or selecting assets. This achieves the optimal sparse portfolio selection, which has close performance to the theoretical optimal one. Among 1000 stocks, for example, we are able to identify all optimal subsets of portfolios of different sizes, their associated allocation vectors, and their estimated risks. The utility of our new approach is illustrated by simulation and empirical studies on the 100 Fama-French industrial portfolios and the 400 stocks randomly selected from Russell 3000.

Includes bibliographical references (pages 358-359) and index

Office of the State Treasurer Asset Allocation Compliance Report, May 31, 2022; asset allocation graph; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, November 30, 2022; asset allocation graph; investments; securities; deposits;