On long-only information-based portfolio diversification framework

: Calculating the amount of information about a random function contained in another random function has important uses in communication theory. An expression for the mutual information for continuous time random processes has been given by Gelfand and Yaglom, Chiang, and Perez by generalizing Shannon's result in a natural way. Under a condition of absolute continuity of measures the continuous time expression has the same form as Shannon's result. For two Gaussian processes Gelfand and Yaglom express the mutual information in terms of a mean square estimation error. We generalize this result to diffusion processes and express the solution in a different form which is more naturally related to a corresponding filtering problem. We also use these results to calculate some information rates.

In spite of theoretical benefits, Markowitz mean-variance (MV) optimized portfolios often fail to meet practical investment goals of marketability, usability, and performance, prompting many investors to seek simpler alternatives. Financial experts Richard and Robert Michaud demonstrate that the limitations of MV optimization are not the result of conceptual flaws in Markowitz theory but unrealistic representation of investment information. What is missing is a realistic treatment of estimation error in the optimization and rebalancing process. The text provides a non-technical review of classical Markowitz optimization and traditional objections. The authors demonstrate that in practice the single most important limitation of MV optimization is oversensitivity to estimation error. Portfolio optimization requires a modern statistical perspective. Efficient Asset Management, Second Edition uses Monte Carlo resampling to address information uncertainty and define Resampled Efficiency(TM) (RE) technology. RE optimized portfolios represent a new definition of portfolio optimality that is more investment intuitive, robust, and provably investment effective. RE rebalancing provides the first rigorous portfolio trading, monitoring, and asset importance rules, avoiding widespread ad hoc methods in current practice. The Second Edition resolves several open issues and misunderstandings that have emerged since the original edition. The new edition includes new proofs of effectiveness, substantial revisions of statistical estimation, extensive discussion of long-short optimization, and new tools for dealing with estimation error in applications and enhancing computational efficiency. RE optimization is shown to be a Bayesian-based generalization and enhancement of Markowitz’s solution. RE technology corrects many current practices that may adversely impact the investment value of trillions of dollars under current asset management. RE optimization technology may also be useful in other financial optimizations and more generally in multivariate estimation contexts of information uncertainty with Bayesian linear constraints. Michaud and Michaud’s new book includes numerous additional proposals to enhance investment value including Stein and Bayesian methods for improved input estimation, the use of portfolio priors, and an economic perspective for asset-liability optimization. Applications include investment policy, asset allocation, and equity portfolio optimization. A final chapter includes practical advice for avoiding simple portfolio design errors. A simple global asset allocation problem illustrates portfolio optimization techniques. The presentation is intuitive, rigorous and informed with institutional management experience to appeal to investment management executives, consultants, fund trustees, brokers, academics, and anyone seeking to stay abreast of the future of investment technology. With its important implications for investment practice, Efficient Asset Management’s highly intuitive yet rigorous approach to defining optimal portfolios will appeal to investment management executives, consultants, brokers, and anyone seeking to stay abreast of current investment technology. Through practical examples and illustrations, Michaud and Michaud update the practice of optimization for modern investment management.

Despite the fact any investor prefers lower risk and higher return, investors may have different preferences about what would be an acceptable risk or a minimal return. For instance, some investors prefer to have a lower bound risk rather than gaining a higher return. In portfolio theory, it is commonly assumed the existence of one risk free asset that offers a positive return. This theoretical risk free asset combined with a risky portfolio creates a new portfolio that presents a linear relation between risk and return as the risk free asset weight (w f ) changes. Hence, any level of risk or of return is easy to achieve separately, just by changing w f . However, in a world without any risk free assets, the combination between assets creates nonlinear portfolios. Achieving a specific level of risk or return is not a trivial task. In this paper, we assume a risky world rather than the existence of a risk free asset, in order to model an automated asset management system. Furthermore, some automated asset managers give very different results when evolving in different contexts: hence, a very profitable manager can have very bad results in other market situations. This paper presents a multiagent architecture, aiming to tackle these problems. The architecture, named COAST (COmpetitive Agent SocieTy), is based on competitive agents that act autonomously on behalf of an investor in financial asset management. It allows the simultaneous and competitive use of several asset analysis techniques currently applied in the finance field. Some dedicated agents, called advisors, apply a particular technique to a single asset. The results provided by these advisors are then submitted to and analyzed by a special agent called coach, who evaluates its advisors' performance and defines an expectation about the future price of one specific asset. Within COAST, several coaches negotiate to define the best money allocation among different assets, by using a negotiation protocol defined in this paper. We also propose an investor description model that is able to represent different investors' preferences with defined acceptable limits of risk and/or return. The COAST architecture was designed to operate adequately with any possible investor's preference. It was implemented using a financial market simulator called AgEx and tested using real data from the Nasdaq stock exchange. The test results show that the architecture performed well when compared to an adjusted market index.

Markowitz’s portfolio theory is one of the most important theoretical developments in finance. Its elegance and theoretical appeal are illustrated by the fact that it is taught in virtually every undergraduate and MBA level course on portfolio management. Yet the Markowitz mean-variance (MV) optimization is not widely used by the investment community. In this book, Richard Michaud, a leading researcher in this area, explains why this is so. Based on his diagnosis of the problem, he advocates a “statistical view” of the MV optimization that leads to new procedures that can reduce or eliminate many practical deficiencies of the MV optimization. Using a simple global asset allocation example throughout the book, he illustrates the problems associated with a naïve implementation of the MV optimization procedure. He then describes how the new procedures can be applied, and shows how they improve the MV optimization results. The bottom line: the “statistical view” and the new procedures offer the potential of substantial improvements in the investment performance of the MV optimization. I agree with this assessment. I find this book a must-read for industry professionals and researchers who are interested in this area.

ABSTRACTWe consider an asset allocation problem for a multi-asset fund where multiple decentralized managers implement investment strategies in separate asset classes that include alternative investments. In this setting it is common practice to use traditional asset allocation methodology (e.g. mean-variance optimization) to obtain optimal target weights and to compel investment managers to stay close to their targets. However, the sophistication and high level of specialization involved in alternative investments often result in deviations from target weights. Misallocations may require ad hoc investment adjustments to maintain feasibility of the fund with respect to overall risk and return profiles. We develop an asset allocation model that provides a range of portfolio allocations (i.e. bounds) for individual managers. These bounds represent the maximum flexibility managers have in their investments without affecting the overall portfolio characteristics. Based on out-of-sample testing, our model is more robust than traditional asset allocation methodology with respect to misallocations.

Following the recent financial crisis, the need for diversification cannot be over emphasized. This notwithstanding, some concerns have been raised regarding the efficacy of the conventional asset allocation methods. These traditional strategies encompass equal weighting, minimum variance, and mean- variance optimization based on modern portfolio theory (MPT). MPT remains the most widely used method despite its inherent estimation error in the determination of expected returns and correlations. Portfolio construction using MPT in real estate presents similar challenges where naive diversification is predominantly used to diversify portfolios. Thus, these conventional strategies have been questioned especially in the wake of the financial crisis due to poor performance of portfolios.This paper introduces an alternative strategy of asset allocation called 'risk parity' to real estate portfolios by using the framework from mainstream finance. In contrast with the traditional strategies, the aim of risk parity is equal risk allocation across asset classes in a portfolio. To achieve the broad aim, the paper relies heavily on a systematic analysis of listed as well as direct real estate to ascertain whether risk parity provides a better alternative to the traditional allocation strategies employed in the real estate market. The paper argues that compared to the traditional allocation strategies, a risk parity based portfolio is likely to produce superior risk-reward tradeoff. Utilizing risk parity strategy in constructing a portfolio does not require the creation of expected return assumptions as only the asset class covariances are needed. These covariances can be more accurately estimated from historical data than expected returns. In lower risk portfolios, leverage can be employed in order to increase return expectation while derivatives can be used as a way to attain desired market exposures more safely and inexpensively.

Asset allocation represents a fundamental strategic decision for every institutional investor. Although many asset allocation approaches have been recommended and implemented in various forms, each has its own strengths and weaknesses. A careful review of current asset allocation frameworks motivated the authors to design a hybrid approach that addresses many of these perceived individual shortcomings. The authors’ guiding principle was to use several familiar elements to create a flexible process that incorporates lessquantifiable investment ideas around a rigorous foundation. The result, which they call flexible indeterminate factor-based asset allocation (FIFAA), is an adaptive four-step asset allocation synthesis that remains quantitatively and theoretically well grounded. To achieve this goal, FIFAA overlays informed judgment about investment opportunities onto an objectively derived set of core factor exposures. <b>TOPICS:</b>Portfolio management/multi-asset allocation, factor-based models, portfolio theory

In this article, the authors revisit a neglected paper written by Rubinstein in which he advocated a generalized logarithmic utility function. The profession has rather ignored his model in favor of the mean–variance approach, for reasons that are well known. The authors note that generalized logarithmic utility allows us to identify absolute risk aversion more directly and intuitively than the approaches adopted in mean–variance portfolio construction. They demonstrate that, for top-level asset allocation in which the conditions required by mean–variance are satisfied and mean–variance gives a good approximation to expected utility, generalized logarithmic utility gives essentially the same answer. In situations not suited to mean–variance, such as the inclusion of derivatives in portfolios or, more generally, when higher moments in return and utility probability distributions are unavoidable, Rubinstein’s model appears superior and seems particularly well suited to multi-asset investment. The authors’ simplified Bayesian model shows out-of-sample advantages for broad US asset allocation for the 2000 through mid-2020 period. <b>TOPICS:</b>Statistical methods, portfolio theory, portfolio construction <b>Key Findings</b> ▪ Maximizing the expected Rubinstein utility increases expected surplus growth for conservative investors and for those who do not rebalance frequently. Markowitz’s risk aversion parameter is replaced without requiring subjective judgement of aversion to return variance. ▪ Bayesian prediction can incorporate both scenario priors and observed return data in a simple grid form. This increases model flexibility and decreases sensitivity to errors in estimates of return probability distribution parameters. ▪ We demonstrate out-of-sample advantages for Rubinstein utility with simplified Bayesian prediction in broad US stock, bond, and cash allocation for the most recent decades. This is based on a prior inferred from earlier history, including the 1929 crash.

This paper presents a detailed analysis of portfolio construction strategies aimed at maximizing risk-adjusted returns for investors, utilizing Python-based methodologies. By leveraging historical data on stock returns and risks, a meticulous selection process identified 5 stocks with superior risk-return profiles, outperforming the dataset's average measures with higher returns and lower risks. The selected stocks from the defense industry formed the foundation of an optimal portfolio designed to minimize sector-specific risks while maximizing growth potential. Through rigorous portfolio optimization techniques, including Markowitz's mean-variance optimization, an optimal portfolio allocation was identified with a return of 62.40% and volatility of 27.74%. Insights from the study provide valuable perspectives for investors seeking realistic and sustainable wealth accumulation strategies. The research also highlights future avenues for exploration, such as integrating alternative asset classes, incorporating behavioral finance insights, and leveraging advancements in risk management technologies. This study contributes to advancing portfolio theory and offers practical guidance for investment decision-making.This study demonstrates the effectiveness of Python in financial analysis and portfolio optimization while furthering the theory of portfolios and providing valuable guidance for making investment decisions. Keywords: Returns, Risk, Volatility, Optimal Portfolio Construction, Defense Sector, Markowitz Portfolio Theory,

Stock market portfolio optimization is a very important aspect for stock market trading. Various techniques have been proposed in the literature ranging from technical indicator-based methods to statistical methods and machine learning for portfolio optimization. However, there has been lack of effort to find the impact of machine learning along with technical indicators to build optimal stock portfolios for trading. From this line of research, this paper focusses on the use of deep learning-based LSTM (Long Short Term Memory) models along with Ichimoku cloud indicators to build optimal portfolios, which can be used in a practical stock market environment. This model is developed with combining the above prediction models such as LSTM and Ichimoku Cloud Indicators with Modern Portfolio Theory and Sharpe Ratio to optimize the model by reducing the risk to reward ratio. The model is tested with real life data of London Stock Exchange and Bombay Stock Exchange. By doing extensive experimental analysis, it was found that, the model combining LSTM network and Ichimoku Indicator, paired with Modern Portfolio Theory and Sharpe Ratio giving consistent performance in the market despite volatility.

This paper focused on Portfolio Analysis that set-up among 15 selected stocks traded in Kuala Lumpur Stock Exchange (KLSE). Markowitz model (1959) is the main idea which used to build up the optimal portfolio in order to achieve the objective of maximize the return and minimize the risk. There are few scenarios are considered in constructing the optimal portfolio, such as risk-free, taxes, transaction cost and benchmark portfolio. The results suggest that almost all of the funds should place into T -bill and little bit in other stocks when constructing the Portfolio with risk-free asset. However, when the desired target of investment in higher, the investors are suggested to lower the portion of investment into risk-free asset. While transaction cost is considered, solution recommend that AMMB, NESTLE and CASRLBERG should be invest. AMMB and NESTLE show as the potential stocks for investor in Portfolio with taxes. Even though, there is no feasible solution when benchmarking KLSE in Matching Portfolio. Result in hedging case also implies that AMMB has the potential to be invests and only several stocks, such as AFFIN, BAT, TANJONG, PUBLIC and CARLSBERG would consider in the investment.

The quest for the optimal balance between risk and return remains a pivotal area of research within modern portfolio theory. As two traditional approaches, the Markowitz model and the single-index model embody distinct philosophies: precise calculation versus simplified approximation. The Markowitz model delves deeply into the covariance relationships between assets, laying the foundation for modern portfolio theory; the single-index model enhances practical applicability by streamlining the estimation process of the covariance matrix. This study constructs the efficient frontier, global minimum variance portfolio, and optimal risky portfolio under both the Markowitz model and single index model using data from 21 selected sample stocks. Empirical testing and comparisons are conducted across various scenarios using metrics such as the Sharpe ratio and Sortino ratio. Empirical results indicate that the single-index model demonstrates superior risk-adjusted returns and robustness across most market conditions. Whilst the Markowitz model theoretically achieves greater diversification, cumulative estimation errors may lead to comparatively weaker performance in empirical outcomes. These findings not only assist investors in model selection across dynamic market environments but also inform future research on expanding constraints and incorporating factor models.

This study includes alternative portfolio construction approaches consistent with the Modern Portfolio Theory (MPT) and Postmodern Portfolio Theory (PMPT). We propose a weighting strategy based on Sharpe and Sortino optimization, and unlike MPT, we create PMPT portfolios using downside metrics, such as downside risk, downside beta, and downside capital asset pricing model (D-CAPM). Portfolios consist of stocks in the Borsa Istanbul Participation 30 Index (XK030), with the stocks in the portfolio having been revised according to screening periods. In addition, we created an equally weighted portfolio and used XK030 as a benchmark for comparative analysis. The sample period covers 527 trading days between May 6, 2022, and June 28, 2024. The results show that the Sharpe portfolio consistently follows the benchmark index throughout the observation period. Sortino outperforms both the benchmark and conventional market index in some specific periods when the market has an upward trend, especially. This study provides evidence that the MPT and PMPT approaches and measures can be used in asset allocation and portfolio management. Investors can manage their assets and balance portfolio weights by implementing the models in different market conditions.

Inflation and portfolio selection

Macro-Based Parametric Asset Allocation