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Markowitz optimization plays a crucial role in modern portfolio theory. However, it is well known that Markowitz optimization is highly affected by estimation errors in the mean vector and covariance matrix, resulting in extreme and/or unrealistic portfolio weights, lack of diversification, and poor out-of-sample performance. In response to these challenges, Michaud and Michaud (1998) proposed a heuristic portfolio resampling approach aimed to deliver more diversified and better out-of-sample portfolio performance. This approach has received both criticism and praise among academics and practitioners, with the main critique being the unclear economic benefits of applying this method to empirical data. To contribute to this ongoing debate, our study assess the performance of the Michaud and Michaud (1998) portfolio resampling approach using data from the Brazilian stock market. Additionally, we evaluate whether a resampling approach based on factor structure can yield superior out-of-sample performance. Our findings indicate no evidence of superiority over Markowitz optimization, reinforcing criticisms of the portfolio resampling approach. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: During the last five years, the average rate of return on the assets for Civil Servants Pension Fund was only about 15%, and, during that period, about 75% of the financial resources required to fulfill the obligations of the Fund were provided by government grants. In this study, for the goal of asset-liability management of the Fund, determining the optimal investment portfolio of the Fund in major stock market groups has been studied and the proposed solutions for asset management in the form of modifying the fund’s investment share in various stock market groups and with the aim of financial stability of the fund have been presented. METHODS: Research is quantitative in terms of the type of data and descriptive and applied in terms of purposes and results. Due to the use of the past performance of the stocks in the Fund portfolio and the study of the history of information, it is an event research. Quantitative research data for the period of 79 months (1584 working days) from March 2017 to October 2023 was collected from the data bank of the Iran Financial Information Processing Center of the Tehran Stock Exchange Technology Management Company, and to perform model calculations, the stock exchange assets of the Fund were separated into 18 stock groups, including pharmaceuticals, cement, lime, plaster, rubber and plastic, sugar, tile and ceramic, metal ore extraction, petroleum products, leather products, insurance, computer, non-metallic mineral, food except sugar, basic metals, intermediary financial institutions, investments, chemical, transportation, warehousing and communication, multi-disciplinary industries have been carried out. Data collection and data sorting were done using Microsoft Excel software, and objective functions were solved based on Markowitz patterns and value at risk using MATLAB software. FINDINGS: In the current investment portfolio of the Fund, the major share (about 95%) of the portfolio is dedicated to only three groups including “multi-industry”, “transportation, warehousing and communication” and “chemical” and based on the efficiency frontier curve drawn based on historical data, the Fund’s current portfolio is not on the mentioned curve, and the need to optimize the current portfolio was determined. The objective functions were solved based on the Markowitz and value at risk models to find the optimal portfolio of the Fund, and in three cases of the current investment portfolio, the optimal portfolio of the value at risk model model and the optimal portfolio of the Markowitz, the average daily rate of return is equal to 0.232, 0.235 and 0.230 percent respectively; the risk of the portfolio was 1.62, 1.31 and 1.21 percent respectively; and the return-to-risk ratio was equal to 0.143, 0.1793 and 0.1908 respectively. Therefore, it can be concluded that among the three studied investment portfolios, the portfolio from Makowitz’s model is the best investment portfolio due to the highest return-to-risk ratio. CONCLUSION: In order to optimize the current investment portfolio of Civil Servants Pension Fund while maintaining the absolute amount of investment, the share of investment in the “multi-industry”, “transportation, warehousing and communication” groups should be reduced and the share of groups such as “sugar”, “cement, lime and plaster”, “tiles and ceramics”, “pharmaceutical” and “investments” should be increased. [ABSTRACT FROM AUTHOR]

Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to approximations, as fractional stocks are typically not tradeable. While these approximations are effective for large investment budgets, they deteriorate as budgets decrease. To alleviate this issue, a discrete Markowitz portfolio theory (DMPT) with finite budgets and integer stock weights can be formulated, but results in a non-polynomial (NP)-hard problem. Recent progress in quantum processing units (QPUs), including quantum annealers, makes solving DMPT problems feasible. Our study explores portfolio optimization on quantum annealers, establishing a mapping between continuous and discrete Markowitz portfolio theories. We find that correctly normalized discrete portfolios converge to continuous solutions as budgets increase. Our DMPT implementation provides efficient frontier solutions, outperforming traditional rounding methods, even for moderate budgets. Responding to the demand for environmentally and socially responsible investments, we enhance our discrete portfolio optimization with ESG (environmental, social, governance) ratings for EURO STOXX 50 index stocks. We introduce a utility function incorporating ESG ratings to balance risk, return and ESG friendliness, and discuss implications for ESG-aware investors. [ABSTRACT FROM AUTHOR]

In the context of global climate change and the water-carbon peak target, improving water security in arid regions is a persistent challenge in global water resources management. Water diversion projects can serve as an important measure to effectively alleviate the uneven distribution of water resources, achieve rational allocation and efficient utilization of water resources. However, how to achieve the maximization of comprehensive benefits during the process of water allocation is also an urgent problem that needs to be solved. This study focuses on the Middle Route Project of the South to North Water Diversion Project in China, selecting four important municipalities and provinces during 2015 to 2021, namely Beijing, Tianjin, Hebei Province, and Henan Province, based on the actual benefits of the water receiving areas of the middle line project. Nine representative indicators related to social, economic, and ecological benefits were selected to evaluate the optimal combination of water resource allocation in the water receiving areas along the central line, in order to achieve the maximum comprehensive benefits and solve the problems of high water safety guarantee requirements and difficult balanced water distribution in urban agglomerations in the water receiving areas. Through the calculation of the Markovsky theoretical model, the results show that when 79.9% of the water conveyance is used to generate social benefits, 15.8% of the water conveyance is used to generate ecological benefits, and 4.5% of the water conveyance is used to generate economic benefits, the project achieves the maximum comprehensive benefits. This computational model method can be used to provide technical support and scientific reference for the optimal allocation of water resources in cross regional water transfer projects. [ABSTRACT FROM AUTHOR]

A portfolio is a combination of assets with a desired rate of return as well as risks that can be minimized by spreading risks across different assets. The LQ45 index contains 45 companies whose shares have a market capitalization and a high level of liquidity. The population used in this study is 7 banking stocks that are on the LQ45 index. This study aims to develop an optimal portfolio using the Markowitz Method and the Single Index Model, then compare the results of the two. The combination of stocks produced using the Markowitz method consists of 2 BMRI and BBNI stocks with a portfolio return rate of 0.0247 and a risk of 0.0033. While the combination of stocks produced using the Single Index Model consists of 5 stocks, namely BMRI, BRIS, BBRI, BBNI, and BBCA stocks with a portfolio return rate of 0.019 and a portfolio risk of 0.0018. So a good model to use is the Single Index Model because the risk is smaller than using the Markowitz method

Portfolio allocation is an important tool for portfolio managers and investors interested in diversification as well as improvements in out-of-sample portfolio performance. Recently, new portfolio allocation strategies based on unsupervised machine learning have been proposed in the literature, with hierarchical risk parity being one of the most popular. This article uses assets from the Brazilian financial market to perform an extensive out-of-sample comparison of hierarchical risk parity against widelyknown, traditional portfolio allocation techniques. The results suggest that, in general, hierarchical risk parity does not report the best performance but, in some performance measures, performs equally well to other approaches. Overall, hierarchical risk parity outperforms the market index. [ABSTRACT FROM AUTHOR]

Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to approximations, as fractional stocks are typically not tradeable. While these approximations are effective for large investment budgets, they deteriorate as budgets decrease. To alleviate this issue, a discrete Markowitz portfolio theory (DMPT) with finite budgets and integer stock weights can be formulated, but results in a non-polynomial (NP)-hard problem. Recent progress in quantum processing units (QPUs), including quantum annealers, makes solving DMPT problems feasible. Our study explores portfolio optimization on quantum annealers, establishing a mapping between continuous and discrete Markowitz portfolio theories. We find that correctly normalized discrete portfolios converge to continuous solutions as budgets increase. Our DMPT implementation provides efficient frontier solutions, outperforming traditional rounding methods, even for moderate budgets. Responding to the demand for environmentally and socially responsible investments, we enhance our discrete portfolio optimization with ESG (environmental, social, governance) ratings for EURO STOXX 50 index stocks. We introduce a utility function incorporating ESG ratings to balance risk, return and ESG friendliness, and discuss implications for ESG-aware investors.

Copyright of Revista de Economia e Sociologia Rural is the property of Sociedade Brasileira de Economia e Sociologia Rural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID 19 pandemic in early March 2020 starting from Wuhan had a very large impact on the global economy and the Indonesian economy, the COVID 19 pandemic forced the government to establish a PPKM policy (Implementation of Restrictions on Community Activities) to suppress the spread of Covid 19 in Indonesia. Indonesia's State Gross Domestic Product fell in the second quarter 2021 amounting to -5.32% and also having an impact on the stock market in Indonesia at January 02 2020 the JCI was recorded at 6283 fell to 3937 in March 24 2020 (minus 36.77%) in March 2020. The results of this study using 22 of the stocks representing 11 sectors in the stock market in Indonesia obtained a maximum Sharpe ratio calculation of 31.57% with a weekly yield of 1.48% and a standard deviation of 4.39% and a maximum yield with a Sharpe ratio of 31.29% and a yield weekly yield of 1.37% and a standard deviation of 4.08%, the result of the minimum standard deviation with a Sharpe ratio of 5.92% with a weekly yield of 0.24% and a standard deviation of 2.51% and the results of Portfolio in the efficient frontier namely portfolio 6 with a Sharpe ratio of 30.94% and weekly yield of 1.3% and a standard deviation of 3.9%. For investors who want to optimize their portfolio, they can choose stocks with optimal Sharpe ratios that already pay attention to risk-adjusted return. [ABSTRACT FROM AUTHOR]

In the context of global climate change and the water-carbon peak target, improving water security in arid regions is a persistent challenge in global water resources management. Water diversion projects can serve as an important measure to effectively alleviate the uneven distribution of water resources, achieve rational allocation and efficient utilization of water resources. However, how to achieve the maximization of comprehensive benefits during the process of water allocation is also an urgent problem that needs to be solved. This study focuses on the Middle Route Project of the South to North Water Diversion Project in China, selecting four important municipalities and provinces during 2015 to 2021, namely Beijing, Tianjin, Hebei Province, and Henan Province, based on the actual benefits of the water receiving areas of the middle line project. Nine representative indicators related to social, economic, and ecological benefits were selected to evaluate the optimal combination of water resource allocation in the water receiving areas along the central line, in order to achieve the maximum comprehensive benefits and solve the problems of high water safety guarantee requirements and difficult balanced water distribution in urban agglomerations in the water receiving areas. Through the calculation of the Markovsky theoretical model, the results show that when 79.9% of the water conveyance is used to generate social benefits, 15.8% of the water conveyance is used to generate ecological benefits, and 4.5% of the water conveyance is used to generate economic benefits, the project achieves the maximum comprehensive benefits. This computational model method can be used to provide technical support and scientific reference for the optimal allocation of water resources in cross regional water transfer projects.

In today's globalized world, investing is a key financial management strategy related to the hope of obtaining future economic benefits. Access to more and more information has created new opportunities for wealth creation, including investments in stocks, cryptocurrencies, gold, real estate and many other assets. The work emphasizes the importance of investing and diversifying the investment portfolio, especially in periods of inflation, using the example of a portfolio of listed companies compared to a currency portfolio. The study covers the period from December 2019 to December 2020, and the analysis aims to estimate the rate of return on investment based on the Markowitz and CAPM theories. The presented analysis method is applicable both in the academic environment and in real economic scenarios, encouraging further research on investments. [ABSTRACT FROM AUTHOR]

The Sharpe ratio is a measure based on the theory of mean variance, it is the measure of the performance of a portfolio when the risk can be measured through the standard deviation. This paper suggests a Sharpe-ratio portfolio solution using a second order cone programming (SOCP). We use the penalty-regularized method to represent the nonlinear portfolio problem. We present a computationally tractable way to determining the Sharpe-ratio portfolio. A Markov chain structure is employed to represent the underlying asset price process. In order to determine the optimal portfolio in Markov chains, a new hybrid optimization programming method for SOCP is proposed. The suggested method's efficiency and efficacy are demonstrated using a numerical example. [ABSTRACT FROM AUTHOR]

The objective of investors to invest their money was to maximize return, although they were subject to constraints, primarily risk, so this study aims to provide an alternative to the formation of portfolios and corporate bonds to obtain optimal returns with acceptable risk. The alternative model is to combine Graham's stock selection model with the formation of an optimal portfolio model, namely the Markowitz model. The results of the selection using the Graham model of defensive investors and aggressive investors are 9 and 13 stocks, respectively, of the 45 stocks listed on the LQ45 index. From the selected stocks, the optimal portfolio is formed using the Markowitz model. The results show that the Markowitz model optimizing portfolio provides better performance (reward to variability ratio) than the LQ45 portfolio index with a yield difference of 13.68 – 20.24% per year. Furthermore, the selection of corporate bonds for the optimal portfolio resulted in 8 corporate bonds with a portion of each bond, and this portfolio can generate rates of 8.88% per annum. JEL: G10, G11, G12

This paper expands the model for higher-order moments (evolving into an MVSK model with skewness and kurtosis analysis) and compares it to the classic quadratic objective function of Markowitz. Along with the MVSK analysis, we add an information entropy variable to the model taking into account the asset's informational efficiency and diversity, and trying to encompass the high uncertainty intrinsic to the market's returns and increase the model's validity. We analyze the practical effectiveness and the complexity of creating such a multi-objective portfolio model to see if we can provide more information to the investor with the new framework.

This research is a quantitative descriptive study which aims to determine the optimal portfolio composition of stocks that are consistently listed on the Jakarta Islamic Index (JII) from the 2018 – November 2020 period. By using the Markowitz analysis method and the Single Index Model, and then looking at the portfolio performance of both methods. The results of this study are: (1) in the Markowitz method, 2 stocks that form the optimal portfolio are obtained, namely: ANTM (51%), and BRPT (49%). With a level of expected return portfolio of 0,0083 or 0,83%, and a portfolio risk level of 0,0659 or 6,59%. (2) in the Single Index Model method, 4 stocks that form the optimal portfolio are obtained, namely: BRPT (35,75%), ANTM (35,27%), INCO (25,07%), and WIKA (3,9%). With a level of expected return portfolio of 0,0075 or 0,75%, and a portfolio risk level of 0,0110 or 1,10%. (3) performance Markowitz portfolio has a value sharpe index of 0,1116, treynor index of 0,0066, and jensen index of 0,0052. (4) the performance of Single Index Model portfolio has a value sharpe index of 0,5919, treynor index of 0,0042, and jensen index of 0,0035.

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Two of the most common tools used in finance teaching in a modern classroom are spreadsheets and the web browser. Spreadsheets are perhaps unmatched for building simple models and explaining concepts, while the browser provides access to online calculators and data from websites. In this paper we show how to combine the power of tools of spreadsheets with ubiquity of the internet browser to build web based applications for teaching portfolio analysis and option pricing. While spreadsheets can easily demonstrate the efficient frontier with two securities, it is hard to go beyond two securities and show how the efficient frontier with many securities rules out a large fraction of feasible portfolios. Similarly, while spreadsheets can perform the basic Black Scholes option pricing calculations, they are ill-suited for building high quality interactive visualizations of option values and Greeks. By exploiting the power of R programming language and packages, we show how to build web applications for teaching portfolio analysis with arbitrary number of securities and visualizing option Greeks. Students do not need to learn any programming as they are just using a browser. For instructors, the learning curve is short with even a rudimentary exposure to programming in any language. [ABSTRACT FROM AUTHOR]

In this paper, we show that quantitative portfolio construction techniques can deal with 'reasonable' constraints and deliver most of the expected (ie before constraints) value-added. To do so, we use the case of a hypothetical environmental, social and corporate governance (ESG) investor who chooses to not invest in companies typically excluded for ESG reasons (eg weapons manufacturing, nuclear energy generation, tobacco or high carbon intensity) back in the late 1980s. While satisfying ESG objectives, we capture the market's exposure using several portfolio constructions such as pro-rata cap-weighting and minimum tracking. We also capture other 'styles' that could mimic exposures some asset owners want to be exposed to, such as value or momentum. Our results are helpful to deliver an appropriate, risk-controlled investment experience to asset owners who must integrate externally defined constraints in their long-only equity investments. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have gained high interest from media, regulatory authorities, and both retailer and institutional investors especially in the COVID-19 pandemic followed by a tremendous academic interest. Classification of cryptos as an asset is one important issue while another crucial topic is the significantly high return and volatility fluctuations which does not make life easier for portfolio investors. High energy consumption to produce cryptos and carbon emission issues are also not developing the favor of cryptocurrencies. Moreover, in financial literature recent studies show that prices of renewable energy stocks have long-term cointegrating relationship with technology companies. In this context first we employ an asymmetric VAR-GARCH model to study spillover effects between major crypto-currencies, clean energy, and technology indices. Using daily data of the two major cryptocurrencies for the period of 1/1/2016 and 30/09/2021, we relate risk and return of different mean-variance portfolio strategies to Bitcoin (BTC), Etherium (ETH), S&P Global Clean Energy Index (SPGCE) and MSCI World Information Technology Index (MSCIWIT). Secondly, we apply the Markowitz mean-variance framework to assess risk-return benefits of cryptocurrency-portfolios. Our main goal is to offer optimal portfolio allocation approaches including cyptocurrencies with traditional financial assets. We will combine cryptocurrencies, clean energy, and technology indices to maximize return and Sharpe ratio. Furthermore, we will use our asymmetric VAR-GARCH models results to understand and cross check the Markowitz portfolio allocation results in details.

The aim of this research is to improve investment processes in the capital market, so that the decisions taken are the best, at the best time and at the best price in line with the risk factor. In this paper we present a simplified model of Harry Markowitz, using Microsoft Excel. By gathering public data on the shares of selected companies as being in the current economic trend, over a period of 6 months, we are able to make a prediction of the amount of these shares in a profitable portfolio using the Markowitz model. For the portfolio manager, it is sufficient for the client investor of the respective investment fund to formulate concretely its requirements regarding the minimum accepted return level and the model allows the identification of such a portfolio structure that determines the obtaining of this level of the indicator in minimum risk conditions.

The formation of the optimum portfolio based on risk and return is one of the most important decisions of investors in financial markets, for which there are various methods. Markowitz’s Mean-Variance method was the first method introduced in this area; but because of the normality assumption for the return distribution function, it only considered specific characteristics (expected return and variance) of the return distribution function. Another method introduced years later was the Stochastic Dominance method which considers all of the return distribution function instead of specific characteristics such as variance. The present research investigates the “Stochastic Dominance method” in portfolio optimization and compares the performance of this method with “Markowitz Portfolio Optimization method” using performance evaluation criteria in the Tehran Stock Exchange. The performance evaluation criteria used in this article are: Sharpe, Treynor, Sortino and Omega. The results of this research indicate the advantage of Second Order Stochastic Dominance method on the Markowitz method in out-of-sample and in-sample approaches. Moreover the Second Order Stochastic Dominance method has a higher cumulative return than the Markowitz method.

The Sharpe ratio is a measure based on the theory of mean variance, it is the measure of the performance of a portfolio when the risk can be measured through the standard deviation. This paper suggests a Sharpe-ratio portfolio solution using a second order cone programming (SOCP). We use the penalty-regularized method to represent the nonlinear portfolio problem. We present a computationally tractable way to determining the Sharpe-ratio portfolio. A Markov chain structure is employed to represent the underlying asset price process. In order to determine the optimal portfolio in Markov chains, a new hybrid optimization programming method for SOCP is proposed. The suggested method’s efficiency and efficacy are demonstrated using a numerical example.

• We propose an integrated Support Vector Machine (SVM) model to select the assets before applying the portfolio optimization. • We consider different financial indicators to feed the SVM model. • We consider portfolio allocation with the aim of maximizing the Sharpe's ratio. • We validate the selected investment portfolio using performance indicators such as CAGR (Compound Annual Growth Rate), Sharpe, Calmar, Treynor and Sortino's ratios and Jensen's Alpha. • We show that the optimized portfolio using pre-selected assets by SVM outperforms the expected return and efficiency compared to different tested portfolios. This study aims to analyze the performance of an investment portfolio using the Markowitz model, which maximizes the Sharpe ratio from a set of assets preselected through the Support Vector Machine (SVM) model using fundamental indicators in the Brazilian stock market. With an accuracy of 61% for the SVM model, the results indicate that preselecting assets based on fundamental indicators and subsequently optimizing them by maximizing the Sharpe ratio showed a superior return and faster recovery after drawdown periods compared to the benchmark or SVM (1/n) strategy. These results suggest the relevance of including the SVM in the optimization portfolio process. [ABSTRACT FROM AUTHOR]

Purpose – This study investigates the role of cryptocurrencies in enhancing the performance of portfolios constructed from traditional asset classes. Using a long sample period covering not only the large value increases but also the dramatic declines during the beginning of 2018, the purpose of this paper is to provide a more complete analysis of the dynamic nature of cryptocurrencies as individual investment opportunities, and as components of optimal portfolios. Design/methodology/approach – The mean-variance optimization technique of Merton (1990) is applied to develop the risk and return characteristics of the efficient portfolios, along with the optimal weights of the asset class components in the portfolios. Findings – The authors provide evidence that as a single investment, the best cryptocurrency is Ripple, followed by Bitcoin and Litecoin. Furthermore, cryptocurrencies have a useful role in the optimal portfolio construction and in investments, in addition to their original purposes for which they were created. Bitcoin is the best cryptocurrency enhancing the characteristics of the optimal portfolio. Ripple and Litecoin follow in terms of their usefulness in an optimal portfolio as single cryptocurrencies. Including all these cryptocurrencies in a portfolio generates the best (most optimal) results. Contributions of the cryptocurrencies to the optimal portfolio evolve over time. Therefore, the results and conclusions of this study have no guarantee for continuation in an exact manner in the future. However, the increasing popularity and the unique characteristics of cryptocurrencies will assist their future presence in investment portfolios. Originality/value – This is one of the first studies that examine the role of popular cryptocurrencies in enhancing a portfolio composed of traditional asset classes. The sample period is the largest that has been used in this strand of the literature, and allows to compare optimal portfolios in early/recent subsamples, and during the pre-/post-cryptocurrency crisis periods.

Many research about portfolio optimization in Indonesia still uses the ‘original’ mean-variance model as proposed by Markowitz more than 60 years ago. This article reviews the development and modification of the Markowitz’s mean-variance model, especially that dealing with real stock-market features, which could help the investor to create their own portfolio. There were several real-stock market features that implemented in the modification of mean-variance portfolios optimization models, such as the minimum transaction lots, the transaction cost, the cardinality constraint, the weight constraint, and the sectoral constraint. To implement these features, several heuristic methods were used to obtain the optimal portfolio weight, such as genetic algorithm, Tabu search, bee colony algorithm, particle swarm algorithm, and simulated annealing. These methods become alternative to the mathematical programming method.

There are many non-probability factors affecting financial markets and the return on risk assets is fuzzy and uncertain. The authors propose new risk measurement methods to describe or measure the real investment risks. Currently many scholars are studying fuzzy asset portfolios. Based on previous research and in view of the threshold value constraint and entropy constraint of transaction costs and transaction volume, the multiple-period mean value -mean absolute deviation investment portfolio optimization model was proposed on a trial basis. This model focuses on a dynamic optimization problem with path dependence; solving using the discrete approximate iteration method certifies the algorithm is convergent. Upon the empirical research on 30 weighted stocks selected from Shanghai Stock Exchange and Shenzhen Stock Exchange, a multi-period investment portfolio optimum strategy was designed. Through the empirical research, it can be found that the multi-period investments dynamic optimization model has linear convergence and is more effective. This is of great value for investors to develop a multi-stage fuzzy portfolio investment strategy.

Portfolio selection problems have been thoroughly studied under the risk-and-return paradigm introduced by Markowitz. However, the usefulness of this approach has been hindered by some practical considerations that have resulted in poorly diversified portfolios, or, solutions that are extremely sensitive to parameter estimation errors. In this work, we use sampling methods to cope with this issue and compare the merits of two approaches: a sample average approximation approach and a performance-based regularization (PBR) method that appeared recently in the literature. We extend PBR by incorporating three different risk metrics—integrated chance-constraints, quantile deviation, and absolute semi-deviation—and deriving the corresponding regularization formulas. Additionally, a numerical comparison using index-based portfolios is presented using historic data that includes the subprime crisis. [ABSTRACT FROM AUTHOR]

Copyright of Revista Pretexto is the property of Revista Pretexto and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We develop a general framework to apply the Kelly criterion to the stock market data, and consequently, to portfolio optimization. Under few conditions, using Monte Carlo simulations with different scenarios we prove that the Kelly criterion beats any other approach in many aspects. In particular, it maximizes the expected growth rate and the median of the terminal wealth. We also show that, under a normal distribution of returns, the Kelly criterion has the best performance in the long run. Next, we optimize a portfolio with the Kelly criterion with no leverage and no short selling conditions and show that this portfolio lays in the mean-variance efficient frontier and has higher expected return and higher variance, although it is less diversified, respect to the tangent portfolio optimized under the Markowitz approach. Finally, we implement a dynamic strategy applied on the European stock market data and compare the results between the tangent and the optimal Kelly portfolios. In a dynamic setting, the rolling Kelly portfolio outperforms competitors particularly in the case of rebalanced portfolios optimized with a 2-years window width.

This paper presents an innovative new approach to investment portfolio design, which applies a discrete, state-based methodology to defining market states and making asset allocation decisions with respect to both current and future state membership. State membership is based on attributes taken from traditional finance and portfolio theory namely expected growth, and covariance. The transitional dynamics of the derived states are modeled as a Markovian process. Asset weighting and portfolio allocation decisions are made through an optimization-based approach coupled with heuristics that account for the probability of state membership and the quality of the state in terms of information provided.

Some empirical studies suggest that the computation of certain graph structures from a (large) historical correlation matrix can be helpful in portfolio selection. In particular, a repeated finding is that information about the portfolio weights in the minimum variance portfolio (MVP) from classical Markowitz theory can be inferred from measurements of centrality in such graph structures. The present article compares the two concepts from a purely algebraic perspective. It is demonstrated that this heuristic relationship between graph centrality and the MVP does not originate from a structural similarity between the two portfolio selection mechanisms, but instead is due to specific features of observed correlation matrices. This means that empirically found relations between both concepts depend critically on the underlying historical data. Repeated empirical evidence for a strong relationship is hence shown to constitute a stylized fact of financial return time series.

This paper formulates a relaxed risk parity optimization model to control the balance of risk parity violation against the total portfolio performance. Risk parity has been criticized as being overly conservative and it is improved by re-introducing the asset expected returns into the model and permitting the portfolio to violate the risk parity condition. This paper proposes the incorporation of an explicit target return goal with an intuitive target return approach into a second-order-cone model of a risk parity optimization. When the target return is greater than risk parity return, a violation to risk parity allocations occurs that is controlled using a computational construct to obtain near-risk parity portfolios to retain as much risk parity-like traits as possible. This model is used to demonstrate empirically that higher returns can be achieved than risk parity without the risk contributions deviating dramatically from the risk parity allocations. Furthermore, this study reveals that the relaxed risk parity model exhibits advantageous traits of robustness to expected returns, which should not deter the use of expected returns in risk parity model. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Administraçãao da UNIMEP is the property of Revista de Administracao da UNIMEP and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this research is to improve investment processes in the capital market, so that the decisions taken are the best, at the best time and at the best price in line with the risk factor. In this paper we present a simplified model of Harry Markowitz, using Microsoft Excel. By gathering public data on the shares of selected companies as being in the current economic trend, over a period of 6 months, we are able to make a prediction of the amount of these shares in a profitable portfolio using the Markowitz model. For the portfolio manager, it is sufficient for the client investor of the respective investment fund to formulate concretely its requirements regarding the minimum accepted return level and the model allows the identification of such a portfolio structure that determines the obtaining of this level of the indicator in minimum risk conditions. [ABSTRACT FROM AUTHOR]

This article tells us about selection of portfolio from National Stock Exchange. Investors are more concerned about their objective minimizing risk and maximizing returns. They considering their tradeoff b/w risk and return from the portfolio, a financial economist, Harry Markowitz, proposed the So-called optimal portfolio theory in 1952. The aim of this article is to provide a practical study of Markowitz model on the Indian Stock Market (NSE) from 1st June 2009 to 30th June 2019.The Markowitz model has been widely used by investors, its application on National Stock Exchange is limited. From the data input which are monthly adjusted closing & daily adjusted closing prices. As a result, investors can select the optimal portfolio that maximizes portfolio return with respect to risk. It is vividly manifest that the investor tends to be risk averters, the attitude towards risk and return tends to play a vital role for the selection of the portfolio. Thus, there is the need to comprehend the investor's attitude towards different portfolio choices. Diversification always reduces the non-systematic risk, it is also given by Morgan Kelly in 1994 gives the benefits of diversification (all the eggs in one basket: portfolio diversification of US households). In other words, diversification allows an opportunity for investments to grow with minimum volatility, security behave differently in different market conditions. [ABSTRACT FROM AUTHOR]

A comparative analysis of the portfolio optimization processes is presented using the Kelly criterion under an uncoupled nonlinear function, that is, when the profitability function for a multi-asset portfolio is defined as a nonlinear function of the fraction of total capital. that is assigned in each investment. The comparison elements are the levels of profitability and risk in the two portfolios (a portfolio obtained by applying the Markowitz model versus a portfolio applying the Kelly criterion) in a defined time horizon. [ABSTRACT FROM AUTHOR]

This paper presents an innovative new approach to investment portfolio design, which applies a discrete, state-based methodology to defining market states and making asset allocation decisions with respect to both current and future state membership. State membership is based on attributes taken from traditional finance and portfolio theory namely expected growth, and covariance. The transitional dynamics of the derived states are modeled as a Markovian process. Asset weighting and portfolio allocation decisions are made through an optimization-based approach coupled with heuristics that account for the probability of state membership and the quality of the state in terms of information provided. [ABSTRACT FROM AUTHOR]