Downloads & Free Reading Options - Results

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

The main goal of investors is to minimize risk at any point of a given returns or/and maximize returns at any given risk. Asset allocation involves allotting investments among different assets. Optimal asset allocation minimizes risk of portfolio to the barest level and maximizes returns better. The aim of this paper is to investigate the two asset allocations; Black Litterman model (BLM) and Mean Variance model (MVM) and examine the model that minimizes risk better and maximizes return optimally. The data used are monthly data of groundnut oil, palm oil and palm kernel. The study shows that the BLM minimizes risk of portfolio better and maximizes return optimally than MVM. 

Piracy on the high seas is a problem of world-wide concern. In response to this threat, the US Navy has developed a visualization tool known as the Pirate Attack Risk Surface (PARS) that integrates intelligence data, commercial shipping routes, and meteorological and oceanographic (METOC) information to predict regions where pirates may be present and where they may strike next. This paper proposes an algorithmic augmentation or add-on to PARS that allocates interdiction and surveillance assets so as to minimize the likelihood of a successful pirate attack over a fixed planning horizon. This augmentation, viewed as a tool for human planners, can be mapped closely to the decision support layer of the Battlespace on Demand (BonD) framework [32]. Our solution approach decomposes this NPhard optimization problem into two sequential phases. In Phase I, we solve the problem of allocating only the interdiction assets, such that regions with high cumulative probability of attack over the planning horizon are maximally covered.

Piracy on the high seas is a problem of world-wide concern. In response to this threat, the US Navy has developed a visualization tool known as the Pirate Attack Risk Surface (PARS) that integrates intelligence data, commercial shipping routes, and meteorological and oceanographic (METOC) information to predict regions where pirates may be present and where they may strike next. This paper proposes an algorithmic augmentation or add-on to PARS that allocates interdiction and surveillance assets so as to minimize the likelihood of a successful pirate attack over a fixed planning horizon. This augmentation, viewed as a tool for human planners, can be mapped closely to the decision support layer of the Battlespace on Demand (BonD) framework [32]. Our solution approach decomposes this NPhard optimization problem into two sequential phases. In Phase I, we solve the problem of allocating only the interdiction assets, such that regions with high cumulative probability of attack over the planning horizon are maximally covered.

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;

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.

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.

SR_031 Ｓｐｏｒｔ３０００: https://sport3000.bandcamp.com https://soundcloud.com/sport3000 https://twitter.com/sportplusrec

SR_031 Ｓｐｏｒｔ３０００: https://sport3000.bandcamp.com https://soundcloud.com/sport3000 https://twitter.com/sportplusrec

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

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

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, 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;

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. 

Office of the State Treasurer Asset Allocation Compliance Report, August 31, 2023; investments; securities; deposits;

Office of the State Treasurer Asset Allocation Compliance Report, August 31, 2023; 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, November 30, 2024; asset allocation graph; investments; securities; deposits;

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, 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

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.

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.

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.

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.

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.

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.

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.

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.

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 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;

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.

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

In an effort to preserve the Army s unmatched capabilities in aerial reconnaissance and surveillance (R&S), the Integrated Capabilities Development Team (ICDT) administered a large-scale study during fiscal years 2012 and 2013 to determine in which R&S platforms and sensors the Army should invest. This report describes the Joint Platform Allocation Tool (JPAT), a mixed integer linear program developed as part of this effort. JPAT determines an optimal R&S investment portfolio by evaluating cost, performance, and production timelines of existing and planned assets, as well as these assets ability to perform against a 12-year prioritized mission demand signal. JPAT has informed critical resourcing decisions concerning the Army s long-term investment strategy.

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

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

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

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.

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.