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Journal articles on the topic 'Portfolio'
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1
Micán, Camilo, Gabriela Fernandes, and Madalena Araújo. "Disclosing the Tacit Links between Risk and Success in Organizational Development Project Portfolios." Sustainability 14, no. 9 (April 26, 2022): 5235. http://dx.doi.org/10.3390/su14095235.
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Project portfolios aim to impact organizational strategic goals, influencing both the organization’s business model and its processes. Nonetheless, the actual impact is dependent on the portfolio’s success, which is affected by the materialization of risk factors. This study aims to examine the tacit conceptualization of project portfolio risk as a risk measure explicitly based on project portfolio success itself. In order to focus on the portfolios of organizational development projects, Social Representation Theory was adopted to analyze empirical evidence from twenty-eight semi-structured interviews conducted with project portfolio practitioners. Findings showed that strategic fit, future preparedness, and stakeholder satisfaction were dimensions of success within which project portfolio risk could be conceptualized. Additionally, results evidenced that risk factors influenced project portfolio success through systematic and non-systematic impacts on project portfolio outputs, and also had direct impacts on project portfolio outcomes. This paper provides empirical evidence to back up the conceptualization of project portfolio risk explicitly oriented to portfolio success as a multidimensional risk measure. It represents a new avenue for conducting portfolio risk analysis for both practitioners and academics, orienting the decision-making process based on the portfolio success rather than only on the success of each project.
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Nisani, Doron. "Portfolio selection using the Riskiness Index." Studies in Economics and Finance 35, no. 2 (June 4, 2018): 330–39. http://dx.doi.org/10.1108/sef-03-2017-0058.
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PurposeThe purpose of this paper is to increase the accuracy of the efficient portfolios frontier and the capital market line using the Riskiness Index.Design/methodology/approachThis paper will develop the mean-riskiness model for portfolio selection using the Riskiness Index.FindingsThis paper’s main result is establishing a mean-riskiness efficient set of portfolios. In addition, the paper presents two applications for the mean-riskiness portfolio management method: one that is based on the multi-normal distribution (which is identical to the MV model optimal portfolio) and one that is based on the multi-normal inverse Gaussian distribution (which increases the portfolio’s accuracy, as it includes the a-symmetry and tail-heaviness features in addition to the scale and diversification features of the MV model).Research limitations/implicationsThe Riskiness Index is not a coherent measurement of financial risk, and the mean-riskiness model application is based on a high-order approximation to the portfolio’s rate of return distribution.Originality/valueThe mean-riskiness model increases portfolio management accuracy using the Riskiness Index. As the approximation order increases, the portfolio’s accuracy increases as well. This result can lead to a more efficient asset allocation in the capital markets.
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Wu, Liyun, Muneeb Ahmad, Salman Ali Qureshi, Kashif Raza, and Yousaf Ali Khan. "An analysis of machine learning risk factors and risk parity portfolio optimization." PLOS ONE 17, no. 9 (September 26, 2022): e0272521. http://dx.doi.org/10.1371/journal.pone.0272521.
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Many academics and experts focus on portfolio optimization and risk budgeting as a topic of study. Streamlining a portfolio using machine learning methods and elements is examined, as well as a strategy for portfolio expansion that relies on the decay of a portfolio’s risk into risk factor commitments. There is a more vulnerable relationship between commonly used trademarked portfolios and neural organizations based on variables than famous dimensionality decrease strategies, as we have found. Machine learning methods also generate covariance and portfolio weight structures that are more difficult to assess. The least change portfolios outperform simpler benchmarks in minimizing risk. During periods of high instability, risk-adjusted returns are present, and these effects are amplified for investors with greater sensitivity to chance changes in returns R.
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Yan, Kuan. "Approaching Portfolio Optimization through Empirical Examination." BCP Business & Management 21 (July 20, 2022): 63–66. http://dx.doi.org/10.54691/bcpbm.v21i.1177.
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In this project, the study focuses on the portfolio profitability, one of the most vital quantitative-finance measurements. Out of all possible portfolios being considered, portfolio optimization is the process of selecting the best portfolio, according to some objective. It is a quantitative principle based on statistics, research methods, and advanced mathematical calculation. In this model, financial risk is calculated to usually be minimum while factors such as expected return are maximized. These factors may include physical aspects, "tangible" indicators, and financial metrics. Based on this assumption, an investor is seeking to maximize the portfolio's expected return despite a certain level of risk. Obtaining a higher expected return with these portfolios, called efficient portfolios, usually will let investors to take on more risk, so investors are sometimes forced to choose between risk and return. An asset's weight is a measure of its concentration within a particular class. In order to optimize portfolios, investors assign 'optimization weights' to each asset class and each asset within the class. By weighing the mean and variance of the whole portfolio, investors can approach the best plan with the most profitability.
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Tamara, Dewi, and Grigory Ryabtsev. "VALUE-AT-RISK (VAR) APPLICATION AT HYPOTHETICAL PORTFOLIOS IN JAKARTA ISLAMIC INDEX." Journal of Applied Finance & Accounting 3, no. 2 (June 30, 2011): 153–80. http://dx.doi.org/10.21512/jafa.v3i2.168.
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The paper is an exploratory study to apply the method of historical simulation based on the concept of Value at Risk on hypothetical portfolios on Jakarta Islamic Index (JII). Value at Risk is a tool to measure a portfolio’s exposure to market risk. We construct four portfolios based on the frequencies of the companies in Jakarta Islamic Index on the period of 1 January 2008 to 2 August 2010. The portfolio A has 12 companies, Portfolio B has 9 companies, portfolio C has 6 companies and portfolio D has 4 companies. We put the initial investment equivalent to USD 100 and use the rate of 1 USD=Rp 9500. The result of historical simulation applied in the four portfolios shows significant increasing risk on the year 2008 compared to 2009 and 2010. The bigger number of the member in one portfolio also affects the VaR compared to smaller member. The level of confidence 99% also shows bigger loss compared to 95%. The historical simulation shows the simplest method to estimate the event of increasing risk in Jakarta Islamic Index during the Global Crisis 2008.
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Romano, Tom. "Portfolio on Portfolios." English Education 29, no. 3 (October 1, 1997): 158–72. http://dx.doi.org/10.58680/ee19973711.
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Offers a discussion, in the form of a portfolio, of how the author helps student teachers reflect on their teaching through learning portfolios including artifacts on the culture of teaching, pedagogical insights, big risks and monumental leaps, and failures. Notes that these portfolios often surprise and instruct the author about students’ subjective teaching experience. Full article available in print version only.
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Levchenko, Valentyna, and Myroslav Ostapenko. "Formation of the optimal portfolio of insurer’s services of the voluntary types of insurance." Insurance Markets and Companies 7, no. 1 (November 18, 2016): 45–51. http://dx.doi.org/10.21511/imc.7(1).2016.05.
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The article studies the possibility of using optimization modelling to form the optimal structure of insurance services’ portfolio of insurance companies. Based on the data of net insurance payments and profitability of the voluntary types of insurance in 2005-2015, the authors conducted their analysis according to the possibility to be included in the general insurance portfolio of the insurance company. The optimization model is based on the approach developed by G. Markowitz. The formation of insurance services portfolio is conducted by solving the optimization problem to maximize the portfolios’ profitability or to minimize the portfolio’s risks. The obtained results can be used in making strategic decisions by the management regarding the development of insurance companies. Keywords: insurance company, insurance service, insurance portfolio, portfolio optimization
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Berouaga, Younes, Cherif El Msiyah, and Jaouad Madkour. "Portfolio Optimization Using Minimum Spanning Tree Model in the Moroccan Stock Exchange Market." International Journal of Financial Studies 11, no. 2 (March 23, 2023): 53. http://dx.doi.org/10.3390/ijfs11020053.
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Portfolio optimization is a pertinent topic of significant importance in the financial literature. During the portfolio construction, an investor confronts two important steps: portfolio selection and portfolio allocation. This article seeks to investigate portfolio optimization based on the Minimum Spanning Tree (MST) method applied on the Moroccan All Shares Index (MASI) historical stock log returns covering the period from 2 January 2013 to 27 October 2022 allowing us to build two portfolios: MST-Portfolio and MST-Portfolio 2. Portfolio selection was carried out for MST-Portfolio and MST-Portfolio 2, respectively, based on 63 stocks or using the Degree Centrality (DC) measure and portfolio allocation for both portfolios was carried through the use of the Inverse Degree Centrality Portfolio (IDCP). The obtained portfolios were compared with the Minimum Variance Portfolio (MV Portfolio) and Equal Weighting Portfolio (EW Portfolio) using centrality measures, diversification, and backtesting. According to the used indicators analysis, MST-Portfolio and MST-Portfolio 2 are the most well-performed and robust portfolios showing a good performance during the studied period, even during the COVID-19 crisis, and ensuring a good level of diversification. The findings demonstrate that both suggested methods can enhance portfolio performance, evidence that can help investors or active managers when optimizing their portfolios.
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Faisal Hasan Shoman, Hasanain, and Mustafa Muneer Isma'eel. "Hedging an Efficient Portfolio against Expected Inflation Risk: An Applied Research in the Iraq Stock Exchange." Journal of Economics and Administrative Sciences 30, no. 140 (April 30, 2024): 104–35. http://dx.doi.org/10.33095/6dt08n85.
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This research aims to hedge the efficient portfolio of the investor against the expected inflation risk and to evaluate the extent of improvement in the quality of its performance. It has applied to an intentional sample of companies whose shares traded on the Iraq Stock Exchange, consisting of (37) companies, with (120) monthly observations for each company from 2012 to 2021. The simple ranking model of Elton et al. (1978) has been used to build the nominally efficient portfolio and the inflation-adjusted model of Chen and Moore (1985) to hedge a portfolio against expected inflation risk. The Sharpe, Treynor, Jensen, and M2 models have been used to evaluate the performance of portfolios. The research has reached several results. The most important of which is the presence of a big difference in the components of the efficient hedged portfolio compared to the nominal unhedged portfolio, in addition to the presence of a big difference in the amounts of investment weights between the two portfolios. The results of the analysis have also shown a significant improvement in the quality of the performance of the efficient portfolio that has hedged against the expected inflation risk compared to the unhedged nominal portfolio. The originality of the research and its scientific value lie in the fact that it is the first to adopt the inflation-adjusted model in hedging the efficient portfolios of investors against the inflation risk. In addition, it is the first knowledge contribution with empirical evidence about the efficient portfolio's hedging against that risk on the Iraq Stock Exchange. Paper type: Research Paper
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Tarczyński, Waldemar. "Different Variants of Fundamental Portfolio." Folia Oeconomica Stetinensia 14, no. 1 (June 1, 2014): 47–62. http://dx.doi.org/10.2478/foli-2014-0104.
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Abstract The paper proposes the fundamental portfolio of securities. This portfolio is an alternative for the classic Markowitz model, which combines fundamental analysis with portfolio analysis. The method’s main idea is based on the use of the TMAI1 synthetic measure and, in limiting conditions, the use of risk and the portfolio’s rate of return in the objective function. Different variants of fundamental portfolio have been considered under an empirical study. The effectiveness of the proposed solutions has been related to the classic portfolio constructed with the help of the Markowitz model and the WIG20 market index’s rate of return. All portfolios were constructed with data on rates of return for 2005. Their effectiveness in 2006- 2013 was then evaluated. The studied period comprises the end of the bull market, the 2007-2009 crisis, the 2010 bull market and the 2011 crisis. This allows for the evaluation of the solutions’ flexibility in various extreme situations. For the construction of the fundamental portfolio’s objective function and the TMAI, the study made use of financial and economic data on selected indicators retrieved from Notoria Serwis for 2005.
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Yang, Hyunjun, Hyeonjun Park, and Kyungjae Lee. "A Selective Portfolio Management Algorithm with Off-Policy Reinforcement Learning Using Dirichlet Distribution." Axioms 11, no. 12 (November 23, 2022): 664. http://dx.doi.org/10.3390/axioms11120664.
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Existing methods in portfolio management deterministically produce an optimal portfolio. However, according to modern portfolio theory, there exists a trade-off between a portfolio’s expected returns and risks. Therefore, the optimal portfolio does not exist definitively, but several exist, and using only one deterministic portfolio is disadvantageous for risk management. We proposed Dirichlet Distribution Trader (DDT), an algorithm that calculates multiple optimal portfolios by taking Dirichlet Distribution as a policy. The DDT algorithm makes several optimal portfolios according to risk levels. In addition, by obtaining the pi value from the distribution and applying importance sampling to off-policy learning, the sample is used efficiently. Furthermore, the architecture of our model is scalable because the feed-forward of information between portfolio stocks occurs independently. This means that even if untrained stocks are added to the portfolio, the optimal weight can be adjusted. We also conducted three experiments. In the scalability experiment, it was shown that the DDT extended model, which is trained with only three stocks, had little difference in performance from the DDT model that learned all the stocks in the portfolio. In an experiment comparing the off-policy algorithm and the on-policy algorithm, it was shown that the off-policy algorithm had good performance regardless of the stock price trend. In an experiment comparing investment results according to risk level, it was shown that a higher return or a better Sharpe ratio could be obtained through risk control.
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Jayeola, Dare, Zulhaimy Ismail, and Suliadi Firdaus Sufahani. "Effects of diversification of assets in optimizing risk of portfolio." Malaysian Journal of Fundamental and Applied Sciences 13, no. 4 (December 26, 2017): 584–87. http://dx.doi.org/10.11113/mjfas.v0n0.567.
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Diversification is a strategic option that investors use to optimize their portfolio. Diversification is investing in many assets for the purpose of minimizing risk or maximizing return of portfolio. It is an opportunity by which investors improve from his micro-firm into macro-firm. The investors’ aim is to make an optimal choice that leads to minimization of risk and maximization of return, but the platform that achieves these objectives is not at the finger tips. The purpose of this study is to propose procedures for constructing optimal portfolio for rational investors. Also, the study demonstrates the benefits of diversification of each asset in portfolio. The assets allocations divulge by Black Litterman model are used to estimate risk of both portfolios and assets. We explore DataStream (Yahoo finance) of Gold, Oil, Silver and Platinum. It is observed that diversifying in Gold minimizes higher risk and achieve more benefits than other assets in the portfolio, which made portfolio1 to be optimal portfolio. In view of these facts, it means diversifying in gold acts as hedge/safe haven for investors during economic recession.
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Famara Badji, Cherif, Cristiane Benetti, and Renato Guimaraes. "Diversification Benefits of European REIT, Equities and Bonds." New Challenges in Accounting and Finance 6 (November 2021): 31–49. http://dx.doi.org/10.32038/ncaf.2021.06.03.
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This study aims to demonstrate the benefits related to the inclusion of European REIT in a portfolio of stocks and bonds traded on European markets. Major studies of the portfolio’s diversification those considered REIT was carried out on the American real estate market. Therefore, this research project aims to extend the work to the European scale by forming a mixed pan‐European REIT portfolio. The database is composed of monthly dividend‐adjusted closing prices, over the past 11 years, from different stock indexes collected during eleven years of different European market indexes. Four hypothetic portfolios were constructed to test our hypothesis. Descriptive statistics and correlations were presented. Therefore, the results have shown that European REIT has reduced the risk of a mixed portfolio even if the risk reduction is very limited. The same is true for the portfolio’s return, which was slightly improved. The study showed that European REIT remains a factor of diversification that should not be overlooked in building an asset portfolio. These findings contribute to the existing portfolio theory arguing that using REITs in portfolio diversification helps investors dilute their risk and improve their returns. Our results also have practical implications. They can be useful to investors and financial analysts in their investment decisions by shedding light on this type of asset.
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Giemza, Dawid. "Ranking of optimal stock portfolios determined on the basis of expected utility maximization criterion." Journal of Economics and Management 43 (2021): 154–78. http://dx.doi.org/10.22367/jem.2021.43.08.
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Aim/purpose – The aim of the paper is to rank the optimal portfolios of shares of com- panies listed on the Warsaw Stock Exchange, taking into account the investor’s propen- sity to risk. Design/methodology/approach – Investment portfolios consisting of varied number of companies selected from WIG 20 index were built. Next, the weights of equity holdings of these companies in the entire portfolio were determined, maximizing portfolio’s expected (square) utility function, and then the obtained structures were compared between investors with various levels of risk propensity. Using Hellwig’s taxonomic development measure, a ranking of optimum stock portfolios depending on the inves- tor’s risk propensity was prepared. The research analyzed quotations from 248 trading sessions. Findings – The findings indicated that whilst there are differences in the weight struc- tures of equity holdings in the entire portfolio between the investor characterized by aversion to risk at the level of γ = 10 and the investor characterized by aversion to risk at the level of γ = 100, the rankings of the constructed optimum portfolios demonstrate strong similarity. The study validated, in conformity with the literature, that with the increase in the number of equity holdings in the portfolio, the portfolio risk initially decreases and then becomes stable at a certain level. Research implications/limitations – The study used data from the past as for which there is no guarantee that they will be adequate for the future. There is sensitivity to the selection of the period from which the historic data come. When changing the period of the analyzed historic data by a small time unit it may prove that the portfolio composi- tion will become totally different. Originality/value/contribution – The paper compares the composition of optimum stock portfolios depending on the investor’s propensity to risk. Their ranking was cre- ated using the taxonomic method for this purpose. Taking advantage of this method also additional variables can be taken into account, which describe and differentiate the port- folio and they can be assigned relevant significance depending on the investor’s prefer- ences. Keywords: optimal portfolio, expected rate of return on the portfolio, portfolio standard deviation, expected utility theory, multidimensional comparative analysis. JEL Classification: G10, G11.
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Chandavar, Vanita, Komal Gadade, and Sagar Patil. "Risk-return Analysis and Portfolio Construction of S&P BSE-30 Listed Companies." MUDRA: Journal of Finance and Accounting 9, no. 2 (2022): 39–59. http://dx.doi.org/10.17492/jpi.mudra.v9i2.922203.
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Portfolio construction is the process of choosing securities with the lowest possible risk in order to get the highest returns. A risk and return analysis are the trade-off between aids in portfolio creation. The study aims to formulate portfolio based on assessment of volatility. Exploratory research is being undertaken. For the analysis, S&P BSE listed 30 companies are considered. The data during 2017-2022 is collected from secondary sources. Using the Beta, a volatility measure, all 30 companies are divided into three portfolios. Hypothesis was tested. And Sharpe’s, Treynor’s and Jensen’s Performance Measure were calculated and Portfolio’s were ranked. It is reported that, P3 has outperformed during the years. It is concluded that when volatility < 0.5, there is no significant impact of volatility on portfolio performance whereas the portfolio with volatility more than one has reported significant impact of volatility on its performance.
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Yu-Hsiang (John) Huang, Yu-Ju (Tony) Tu, Troy J. Strader, Michael J. Shaw, and Ramanath (Ram) Subramanyam. "Selecting the Most Desirable IT Portfolio Under Various Risk Tolerance Levels." Information Resources Management Journal 32, no. 4 (October 2019): 1–19. http://dx.doi.org/10.4018/irmj.2019100101.
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To better assist decision-makers (e.g., enterprise executives) in selecting the most desirable IT portfolio, this study proposes a new IT Portfolio Efficient Frontier model that incorporates the decision-maker's risk tolerance levels. The proposed model, built on portfolio optimization along with experimental design and simulation data, considers three IT portfolio scenarios: even distribution-based IT portfolios, uneven distribution-based IT portfolios, and dominant IT portfolios. Our findings show that the IT portfolio efficient frontiers derived from both an even distribution-based IT portfolio and an uneven distribution-based IT portfolio have a relatively positive relationship between IT portfolio risk and return. Our findings also indicate that if IT investments are part of a dominant IT portfolio, an inflection point of the IT portfolio efficient frontier appears under the decision-maker's medium risk tolerance level, and the most desirable IT portfolio is generated when a decision maker's risk tolerance level is medium or higher.
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Matar, Ali. "Does Portfolio’s Beta in Financial Market Affected by Diversification? Evidence from Amman Stock Exchange." International Journal of Business and Management 11, no. 11 (October 26, 2016): 101. http://dx.doi.org/10.5539/ijbm.v11n11p101.
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This study’s goal is to examine the effect of diversification on the portfolio’s beta for stocks of companies listed on the Amman Stock exchange (ASE) return over the 2005-2014 period. Moreover, it will show if the investors can reduce beta in their portfolios by diversification. Monthly data, Capital Assets Pricing Model (CAPM) and portfolio selection model were applied to measure the risk and required rate of return and compare it with the realized rate of return. The results suggest evidence that diversification can only affect unsystematic risk leaving systematic risk unaffected. The regression analysis indicates the existence of a significant relationship between the individual stock <em>β</em> and the portfolio <em>β</em>. The results didn’t approve any relationship between the portfolio size and portfolio <em>β</em>, and the portfolio <em>β</em> is affected only by the individual stock <em>β</em> value.
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Cui, Han, Yu Ping Tong, and Yue Ming Hou. "The Application of E-Portfolios in Designing Alternative Assessment System for Foreign Language Education." Advanced Materials Research 591-593 (November 2012): 2341–44. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2341.
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Effective foreign language education assessment is an issue worthy of exploring. Hence, the purpose of this paper is to facilitate quality language education via the application of e-portfolios as an alternative language assessment tool. After the reserch on e-portfolios and portforlio assessment, several strategies for designing e-portfolio language assessment system are put forward, with the expectation that language learners will become life-long, innovative language users.
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Širůček, Martin, and Lukáš Křen. "Application of Markowitz Portfolio Theory by Building Optimal Portfolio on the US Stock Market." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 4 (2015): 1375–86. http://dx.doi.org/10.11118/actaun201563041375.
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This paper is focused on building investment portfolios by using the Markowitz Portfolio Theory (MPT). Derivation based on the Capital Asset Pricing Model (CAPM) is used to calculate the weights of individual securities in portfolios. The calculated portfolios include a portfolio copying the benchmark made using the CAPM model, portfolio with low and high beta coefficients, and a random portfolio. Only stocks were selected for the examined sample from all the asset classes. Stocks in each portfolio are put together according to predefined criteria. All stocks were selected from Dow Jones Industrial Average (DJIA) index which serves as a benchmark, too. Portfolios were compared based on their risk and return profiles. The results of this work will provide general recommendations on the optimal approach to choose securities for an investor’s portfolio.
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Kaczmarek, Krzysztof, Ludmila Dymova, and Pavel Sevastjanov. "A Simple View on the Interval and Fuzzy Portfolio Selection Problems." Entropy 22, no. 9 (August 25, 2020): 932. http://dx.doi.org/10.3390/e22090932.
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In this paper, first we show that the variance used in the Markowitz’s mean-variance model for the portfolio selection with its numerous modifications often does not properly present the risk of portfolio. Therefore, we propose another treating of portfolio risk as the measure of possibility to earn unacceptable low profits of portfolio and a simple mathematical formalization of this measure. In a similar way, we treat the criterion of portfolio’s return maximization as the measure of possibility to get a maximal profit. As the result, we formulate the portfolio selection problem as a bicriteria optimization task. Then, we study the properties of the developed approach using critical examples of portfolios with interval and fuzzy valued returns. The α-cuts representation of fuzzy returns was used. To validate the proposed method, we compare the results we got using it with those obtained with the use of fuzzy versions of seven widely reputed methods for portfolio selection. As in our approach we deal with the bicriteria task, the three most popular methods for local criteria aggregation are compared using the known example of fuzzy portfolio consist of five assets. It is shown that the results we got using our approach to the interval and fuzzy portfolio selection reflect better the essence of this task than those obtained by widely reputed traditional methods for portfolio selection in the fuzzy setting.
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SARAL, KUNIKA. "Analyzing the Relationship between Real Estate Investments and Portfolio Diversification." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 5, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32966.
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Real estate has long been considered an attractive investment option for individuals and institutions seeking to build wealth and diversify their portfolios. Unlike traditional investment vehicles such as stocks and bonds, real estate offers unique characteristics that can potentially enhance returns and mitigate risk. This analysis aims to explore the role of real estate investments in portfolio diversification and assess their potential impact on overall portfolio performance. Portfolio diversification is a fundamental principle in investment management, as it helps to spread risk across different asset classes and mitigate the impact of market fluctuations on a portfolio's overall value. By including assets with low or negative correlations, investors can reduce the volatility of their portfolios and potentially achieve higher risk-adjusted returns. Real estate investments, including direct property ownership, real estate investment trusts (REITs), and other real estate-related securities, have traditionally exhibited low correlations with other asset classes, such as equities and bonds. This low correlation can be attributed to the unique characteristics of real estate, including its tangible nature, the presence of rental income streams, and the potential for capital appreciation. Furthermore, real estate investments can provide a hedge against inflation, as property values and rental rates tend to increase during periods of rising prices. This feature makes real estate an attractive diversification option, particularly for investors seeking to protect their portfolios from the eroding effects of inflation. This analysis will delve into the historical performance of real estate investments, examine their risk and return characteristics, and evaluate their potential contribution to portfolio diversification. By examining empirical data and leveraging portfolio optimization techniques, we aim to provide insights into the optimal allocation of real estate investments within a diversified portfolio.
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Mulyono, Gharyni Nurkhair, Deni Saepudin, and Aniq Atiqi Rohmawati. "Portfolio Optimization Based on Return Prediction and Semi Absolute Deviation (SAD)." International Journal on Information and Communication Technology (IJoICT) 9, no. 1 (June 18, 2023): 14–26. http://dx.doi.org/10.21108/ijoict.v9i1.698.
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A portfolio is a collection of investment financial assets managed by financial institutions or individuals. In investment activities, investors expect minimal loss risk and optimal stock portfolio weight to get maximum profit. Investors can monitor changes in stock index values to compare portfolio performance. This research has discussed how to build a portfolio based on stock datasets with the LQ45 index using return predictions from the artificial neural network (ANN) method with semi-absolute deviation (SAD). Furthermore, the portfolio is optimized by looking for weights that match it. After that, a comparison of portfolio performance was carried out using the Sharpe ratio (SR) method between the semi-absolute deviation (SAD) portfolio and the portfolio resulting from the formation of the equal weight (EW) portfolio. Portfolio performance with ANN prediction and SAD is better than equal-weight portfolios in terms of mean return, standard deviation, and sharpe ratio for portfolios with few stocks, namely 2 and 3 stocks. In addition, a portfolio with a higher number of stocks can make the portfolio value from the ANN close prediction algorithm process and the selection of weights based on SAD is better than portfolios with equal weight for each list of stocks in the portfolio.
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Ziane, Mohammed, Chillali Sara, Belhabib Fatima, Chillali Abdelhakim, and Karim EL MOUTAOUAKIL. "Portfolio selection problem: main knowledge and models (A systematic review)." Statistics, Optimization & Information Computing 12, no. 3 (February 21, 2024): 799–816. http://dx.doi.org/10.19139/soic-2310-5070-1961.
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The challenge in portfolio optimization lies in creating a collection of assets that attains a target expected returnwhile mitigating risk. This problem is often framed as an optimization task, specifically Mean Variance Optimization (MVO). MVO involves formulating an objective function, typically quadratic, that is contingent on the composition of the portfolio, and linear constraints that represents the portfolio's asset allocation restriction. Several improvements have been proposed, such as adding constraints to MVO or using alternative risk measures. As a result, even though MVO model remains the most widely studied type of portfolio optimization, different types of portfolio optimization models, risk/return measurements and constraints have been suggested and used since its invention. In this work, we delve into the various risk and return measures, constraints, and mathematical models commonly used in portfolio optimization.We discuss the key risk measures employed in portfolio optimization, including the Sharpe ratio, beta, maximum drawdown and others, We explore the constraints commonly applied in portfolio optimization. Furthermore, we delve into the mathematical models utilized in portfolio optimization. Then, we emphasize the interplay between risk and return measures, constraints, and mathematical models in portfolio optimization. By providing a comprehensive overview of risk and return measures, constraints, and mathematical models, this work aims to enhance the understanding of portfolio optimization techniques and facilitate informed decisionmaking in the field of investment management. To illustrate different knowledge and models, several experiments were conducted on well-known real data portfolios.
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Mercurio, Peter Joseph, Yuehua Wu, and Hong Xie. "Option Portfolio Selection with Generalized Entropic Portfolio Optimization." Entropy 22, no. 8 (July 22, 2020): 805. http://dx.doi.org/10.3390/e22080805.
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In this third and final paper of our series on the topic of portfolio optimization, we introduce a further generalized portfolio selection method called generalized entropic portfolio optimization (GEPO). GEPO extends discrete entropic portfolio optimization (DEPO) to include intervals of continuous returns, with direct application to a wide range of option strategies. This lays the groundwork for an adaptable optimization framework that can accommodate a wealth of option portfolios, including popular strategies such as covered calls, married puts, credit spreads, straddles, strangles, butterfly spreads, and even iron condors. These option strategies exhibit mixed returns: a combination of discrete and continuous returns with performance best measured by portfolio growth rate, making entropic portfolio optimization an ideal method for option portfolio selection. GEPO provides the mathematical tools to select efficient option portfolios based on their growth rate and relative entropy. We provide an example of GEPO applied to real market option portfolio selection and demonstrate how GEPO outperforms traditional Kelly criterion strategies.
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Liu, Dong. "Portfolio Optimization for Industries in Chinas A-shares Market." Advances in Economics, Management and Political Sciences 4, no. 1 (March 21, 2023): 572–79. http://dx.doi.org/10.54254/2754-1169/4/2022959.
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Portfolio optimization has always been a hot topic in financial research. Rational investors seek to balance risk and return in asset allocation. This article conducts a portfolio analysis of the military, liquor, new energy vehicles, medical service, and real estate industries in Chinese market. We select a representative asset from each industry and construct a large number of portfolios using the mean-variance model. Among the efficient frontier, we mainly focus on two portfolios. One is the minimum variance portfolio, and the other is the tangency portfolio. Finally, we compare these two portfolios to the SCI 300 Index. The results show that the real estate industry has the largest proportion in the minimum variance portfolio and the military industry contributes most to the maximum Sharpe portfolio. These two portfolios perform better than the SCI 300 Index.
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Wang, Lijuan, and Chunyan He. "Review of Research on Portfolios in ESL/EFL Context." English Language Teaching 13, no. 12 (November 26, 2020): 76. http://dx.doi.org/10.5539/elt.v13n12p76.
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The portfolio is considered a useful tool both for instruction and assessment. Properly designed and implemented, it provides authentic language material for assessment, increases learners’ involvement in learning process and promotes self-reflection. This article mainly reviews the empirical research on portfolios in ESL/EFL context and offers suggestions for future research. The article starts by providing a brief introduction to portfolio and the framework for systematically designing and implementing portfolio assessment in the classroom. Then it reviews the empirical studies of portfolios in ESL/EFL context from three perspectives, i.e. portfolio assessment on writing, portfolios as a means to promote autonomy and e-portfolios. The article concludes by emphasizing the benefits of portfolios in language learning, indicating challenges in carrying out portfolio assessment, and providing suggestions for future research.
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Boloș, Marcel-Ioan, Ioana-Alexandra Bradea, and Camelia Delcea. "Neutrosophic Portfolios of Financial Assets. Minimizing the Risk of Neutrosophic Portfolios." Mathematics 7, no. 11 (November 3, 2019): 1046. http://dx.doi.org/10.3390/math7111046.
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This paper studies the problem of neutrosophic portfolios of financial assets as part of the modern portfolio theory. Neutrosophic portfolios comprise those categories of portfolios made up of financial assets for which the neutrosophic return, risk and covariance can be determined and which provide concomitant information regarding the probability of achieving the neutrosophic return, both at each financial asset and portfolio level and also information on the probability of manifestation of the neutrosophic risk. Neutrosophic portfolios are characterized by two fundamental performance indicators, namely: the neutrosophic portfolio return and the neutrosophic portfolio risk. Neutrosophic portfolio return is dependent on the weight of the financial assets in the total value of the portfolio but also on the specific neutrosophic return of each financial asset category that enters into the portfolio structure. The neutrosophic portfolio risk is dependent on the weight of the financial assets that enter the portfolio structure but also on the individual risk of each financial asset. Within this scientific paper was studied the minimum neutrosophic risk at the portfolio level, respectively, to establish what should be the weight that the financial assets must hold in the total value of the portfolio so that the risk is minimum. These financial assets weights, after calculations, were found to be dependent on the individual risk of each financial asset but also on the covariance between two financial assets that enter into the portfolio structure. The problem of the minimum risk that characterizes the neutrosophic portfolios is of interest for the financial market investors. Thus, the neutrosophic portfolios provide complete information about the probabilities of achieving the neutrosophic portfolio return but also of risk manifestation probability. In this context, the innovative character of the paper is determined by the use of the neutrosophic triangular fuzzy numbers and by the specific concepts of financial assets, in order to substantiating the decisions on the financial markets.
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Shon, Jin Gon. "A Study on e-Portfolio Standardization." Journal of Lifelong Learning Society 7, no. 2 (August 31, 2011): 137–56. http://dx.doi.org/10.26857/jlls.2011.08.7.2.137.
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Cloutier, Richard, and Alan C. Mikkelson. "The effect of absolute return strategies on risk-factor diversification and portfolio performance." Investment Management and Financial Innovations 20, no. 3 (August 3, 2023): 91–101. http://dx.doi.org/10.21511/imfi.20(3).2023.08.
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Absolute return strategies attempt to generate positive returns that are uncorrelated with equity or bond markets and can be used to increase diversification and performance within multi-asset class portfolios. The current paper compared diversification and portfolio performance between traditional multi-asset class portfolios and multi-asset class portfolios with the addition of absolute return strategies. Using closing prices from January 1, 2000 – June 30, 2018, this paper back-tested two multi-asset class portfolios, one composed of equities, fixed income securities, and real return strategies, and the other portfolio composed of the same asset classes but with the addition of absolute return strategies. In particular, the absolute return strategies that this paper added were equity market neutral strategies, managed futures, and global macro strategies. Results indicated that the use of absolute return strategies improved diversification by increasing the portfolio’s effective number of bets (ENB) and enhanced risk adjusted returns as measured by improved Sharpe ratios, Treynor ratios, Jensen’s Alphas, and Sortino ratios. In addition, results showed that the benefits of adding absolute return strategies accrued throughout a full market cycle, which included declines and advances. These results support previous research on the individual absolute return strategies and demonstrate that the portfolio performance and investor wealth can be improved with the addition of these absolute return strategies to multi-asset class portfolios.
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Shon, Jin Gon. "e-Portfolio Standardization for Sustainable Learning Communities." Asian Association of Open Universities Journal 6, no. 1 (September 1, 2011): 32–42. http://dx.doi.org/10.1108/aaouj-06-01-2011-b004.
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A portfolio is used to plan, organize and document learning, and it accumulates results of learning. However, there are some problems in managing a portfolio. For example, it is very difficult to keep the portfolio in the original form physically, and it requires a lot of time and effort to keep it updated. These problems can be solved by an electronic portfolio (e-portfolio), a collection of electronic evidence assembled and managed by a user, usually on the Web. It can efficiently support a learner to manage his or her learning history and keep on learning more to move on to the next level in life so that the learning community becomes more sustainable. This paper explains why e-portfolios are needed to nurture sustainable learning communities. An overview of e-portfolios has been introduced with definitions, advantages, and types of e-portfolios. Furthermore, global trends of e-portfolio applications and Korean activities in e-portfolio applications have been described. Finally, global e-portfolio standardization activities are explained, followed by Korean e-portfolio standardization activities.
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Hsieh, Heng-Hsing. "A Review of Performance Evaluation Measures for Actively-Managed Portfolios." Journal of Economics and Behavioral Studies 5, no. 12 (December 30, 2013): 815–24. http://dx.doi.org/10.22610/jebs.v5i12.455.
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In the recognition that investment management is an on-going process, the performance of actively-managed portfolios need to be monitored and evaluated to ensure that funds under management are efficiently invested in order to satisfy the mandate specified in the policy statement. This paper discusses the primary performance evaluation techniques used to measure a portfolio’s basic risk and return characteristics, risk-adjusted performance, performance attribution and market timing ability. It is concluded that the Treynor measure is more suitable for evaluating portfolios that are constituents of a broader portfolio, while the information ratio is useful for evaluating hedge funds with an absolute return objective. Although the Sharpe ratio and M-squared arrive at the same evaluation result, M-squared provides a direct comparison between the portfolio and the benchmark. With regard to the analysis of portfolio performance attribution, it is found that the return-based multifactor model of Sharpe (1992) is not suitable for analyzing the performance of hedge funds that engage in short-selling, leverage and derivatives. Additional factors generated by factor analysis could be used as factors in the extended model of Sharpe (1992) to analyze hedge fund return attributions. Finally, the Treynor and Mazuy (1966) model and the Henriksson and Merton (1981) model essentially distinguish the market timing ability from the security selection ability of the portfolio manager.
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Nugroho, Sulistyo Adi, Tony Irawan SE MappEc, and Ir Aruddy, Msi. "Portfolio Analysis Using the Single Index Method in the COVID-19 Pandemic Period." International Journal of Research and Review 8, no. 6 (June 29, 2021): 215–25. http://dx.doi.org/10.52403/ijrr.20210626.
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The COVID-19 outbreak that occurred in early 2020 put pressure on economic activity in many countries, including Indonesia. The pressure on economic activity can be seen from the index movement in the capital market. The JCI as a composite index that reflects transaction activity in the Indonesian capital market has weakened due to the impact of the COVID-19 outbreak in a number of business sectors. The decline in the index is a warning for investors to rearrange the composition of assets in their portfolios so that returns can remain optimal during a pandemic. The single index model (SIM) can be used by investors to make investment decisions, including to rearrange their investment portfolios. The share price data analyzed covers the period from 2 September 2019 to 7 December 2020, where the government confirmed the first positive case of COVID-19 in Indonesia on 3 March 2020. The Single Index Model is used to select assets to form an optimal portfolio. Portfolio performance is measured using the Sharpe, Treynor and Jensen index. The sector rotation strategy results in five selected sectors whose assets will be selected to form an optimal portfolio, namely the consumption sector (JKCONS), the basic and chemical industry sector (JKBIND), the infrastructure sector (JKINFA), the mining sector (JKMING) and the financial sector (JKFINA). The listed companies for analysis were 25 out of 184 issuers in the five sectors. The Single Index Model selects 3 issuers for the pre-COVID period and 10 issuers for the COVID period. The allocation of portfolio funds for the pre-COVID period showed BTPS of 44.94%; CPIN 47.61% and BYAN 7.46%. 2.8% allocation of portfolio funds during the COVID period to BTPS issuers; PBID 22.57%; TKIM 15.96%; BYAN 5.86%; ITMG 17.89%; MYOH 1.56%; PTBA 1.76%; ADRO 12.54% and PPRE 19.05%. The portfolio's expected return is positive, which means that the portfolio formed has the potential to generate profits. The Sharpe, Treynor and Jensen indexes are positive, which means that portfolios formed using a single index model have the potential to have good performance. Keywords: investors, IHSG, portfolio performance, single index model, optimal portfolio.
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Khan, Ameer Tamoor, Xinwei Cao, Bolin Liao, and Adam Francis. "Bio-Inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem." Biomimetics 7, no. 3 (August 29, 2022): 124. http://dx.doi.org/10.3390/biomimetics7030124.
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The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios.
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Willim, Andre Prasetya. "Analisis Komparatif Tingkat Pengembalian Value Stocks dan Growth Stocks di Bursa Efek Indonesia." Jurnal Pasar Modal dan Bisnis 1, no. 1 (August 30, 2019): 13–22. http://dx.doi.org/10.37194/jpmb.v1i1.8.
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Purpose- This study aims to examine the difference in returns between portfolio value stocks and growth stocks with comparative analysis.
Methods- The population of this research is all companies in the Indonesia Stock Exchange. Based on the results of the purposive sampling method, there were 396 companies that were sampled in this study with IPO criteria before 2011. There were four portfolios formed, namely small growth, small value, big growth and big value portfolios, each consisting of 59 companies. Portfolio performance in this study was measured by the Sharpe, Treynor and Jensen indices.
Finding- The results showed a difference in portfolio return value stocks and growth stocks. Return portfolio value stocks are lower than growth stocks portfolios and portfolio performance value stocks are also lower than growth stocks portfolios.
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Pandey, Manas. "Application of Markowitz model in analysing risk and return a case study of BSE stock." Risk Governance and Control: Financial Markets and Institutions 2, no. 1 (2012): 7–15. http://dx.doi.org/10.22495/rgcv2i1art1.
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In this paper the optimal portfolio formation using real life data subject to two different constraint sets is attempted. It is a theoretical framework for the analysis of risk return choices. Decisions are based on the concept of efficient portfolios. Markowitz portfolio analysis gives as output an efficient frontier on which each portfolio is the highest return earning portfolio for a specified level of risk. The investors can reduce their risks and can maximize their return from the investment, The Markowitz portfolio selections were obtained by solving the portfolio optimization problems to get maximum total returns, constrained by minimum allowable risk level. Investors can get lot of information knowledge about how to invest when to invest and why to invest in the particular portfolio. It basically calculates the standard deviation and returns for each of the feasible portfolios and identifies the efficient frontier, the boundary of the feasible portfolios of increasing returns.
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Hausner, Jan Frederick, and Gary van Vuuren. "Portfolio performance under tracking error and benchmark volatility constraints." Journal of Economics, Finance and Administrative Science 26, no. 51 (June 7, 2021): 94–111. http://dx.doi.org/10.1108/jefas-06-2019-0099.
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Purpose Using a portfolio comprising liquid global stocks and bonds, this study aims to limit absolute risk to that of a standardised benchmark and determine whether this has a significant impact on expected return in both high volatility period (HV) and low volatility period (LV). Design/methodology/approach Using a traditional benchmark comprising 40% equity and 60% bonds, a constant tracking error (TE) frontier was constructed and implemented. Portfolio performance for different TE constraints and different economic periods (expansion and contraction) was explored. Findings Results indicate that during HV, replicating benchmark portfolio risk produces portfolios that outperform both the maximum return (MR) portfolio and the benchmark. MR portfolios outperform those with the same risk as that of the benchmark in LV. The MR portfolio weights assets to obtain the highest return on the TE frontier. During HV, the benchmark replicated risk portfolio obtained a higher absolute risk value than that of the MR portfolio because of an inefficient benchmark. In HV, the benchmark replicated risk portfolio favoured intermediate maturity treasury bills. Originality/value There is a dearth of literature exploring the performance of active portfolios subject to TE constraints. This work addresses this gap and demonstrates, for the first time, the relative portfolio performance of several standard portfolio choices on the frontier.
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Li, Lin. "Selecting Portfolios Directly Using Recurrent Reinforcement Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13857–58. http://dx.doi.org/10.1609/aaai.v34i10.7201.
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Portfolio selection has attracted increasing attention in machine learning and AI communities recently. Existing portfolio selection using recurrent reinforcement learning (RRL) heavily relies on single asset trading system to heuristically obtain the portfolio weights. In this paper, we propose a novel method, the direct portfolio selection using recurrent reinforcement learning (DPS-RRL), to select portfolios directly. Instead of trading single asset one by one to obtain portfolio weights, our method learns to quantify the asset allocation weight directly via optimizing the Sharpe ratio of financial portfolios. We empirically demonstrate the effectiveness of our method, which is able to outperform state-of-the-art portfolio selection methods.
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Li, Yanru. "Portfolio Optimization for Several Industries among the U.S. Stock Market." BCP Business & Management 38 (March 2, 2023): 1523–29. http://dx.doi.org/10.54691/bcpbm.v38i.3927.
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Optimizing portfolio has been a popular topic since it was proposed because it can reduce the investment risk. This study selected five active stocks from different industries, including Online E-Commerce, Commercial Banks, Motor Vehicles, Mobile Communication Production, and Telecommunications. Then they were allocated into five kinds of portfolios, which are tangency portfolios and minimum variance portfolios under Capital Asset Pricing Model and Fama-French three-factor Model, as well as the 1/N portfolio. The results found that ‘BAC’ and ‘T’ have the largest weight and the lowest weight respectively in the two models. The comparison of the five portfolios showed that the portfolio with the highest cumulative return is the tangency portfolio under FF3F Model, and the 1/N portfolio also performed well. Only these two portfolios outperformed the SPDR S&P 500EFT Trust. This research may help investors focused on the five sectors mentioned above to have a better idea of how to allocate their capital.
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Zoričić, Davor, Denis Dolinar, and Zrinka Lovretin Golubić. "Factor-Based Optimization of a Fundamentally-Weighted Portfolio in the Illiquid and Undeveloped Stock Market." Journal of Risk and Financial Management 13, no. 12 (December 1, 2020): 302. http://dx.doi.org/10.3390/jrfm13120302.
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In this paper, the possibility of using fundamental weighting as a tool to intentionally tilt a portfolio toward specific and unobservable risk factors in the illiquid and undeveloped Croatian stock market is explored. Thus far, fundamental-weighting has been shown to be able to outperform the cap-weighted index in such environments but no attempt regarding control for implicit factor exposure of such portfolios has been reported. Therefore, in this study principal component analysis is performed to capture the underlying risk factors of the fundamentally-weighted portfolio in order to optimize the portfolio’s performance by minimizing its volatility. Previous attempts focusing purely on portfolio risk reduction by estimating minimum variance portfolios failed both from an in-sample and out-of-sample perspective. Results in this study are based on 22 in-sample and out-of-sample tests in the period from March 2009 till March 2020. On the in-sample estimation basis, the proposed approach significantly improves the portfolio’s performance and, if restrictions to weights are imposed, it can outperform the cap-weighted benchmark. However, out-of-sample testing yielded poor results both in terms of risk and return. Such results are in contrast to findings for the developed markets but corroborate the claim that a broad investment base is needed for successful risk exposure in the long run.
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Al-Nator, Mohammed S., and Sofya V. Al-Nator. "OPTIMAL PORTFOLIO SELECTION WITH FIXED COMMISSION." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 4/2, no. 145 (2024): 144–51. http://dx.doi.org/10.36871/ek.up.p.r.2024.04.02.017.
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In this work the portfolio transactions with commission are investigated. It is shown that for portfolios allowing short positions, the problem of finding an optimal portfolio with a commission is non-smooth. It is also shown that the return of such portfolios is a non-smooth rational and bounded function on the weights themselves and their absolute value. Moreover, the optimal portfolio with commission may differ from the optimal portfolio without commission.
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Aliu, Florin, Artor Nuhiu, Besnik Krasniqi, and Fisnik Aliu. "Modeling the Optimal Portfolio: the Case of the Largest European Stock Exchanges." Comparative Economic Research. Central and Eastern Europe 23, no. 2 (June 30, 2020): 41–51. http://dx.doi.org/10.18778/1508-2008.23.11.
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Portfolio optimization is the main concern for portfolio managers. Financial securities are placed within the portfolio based on the investor’s risk tolerance. The study measures the risk-reward relationship when the number of stocks in the portfolio increases. Six diverse portfolios have been created with a different number of stocks, such as portfolios with 47 stocks, 95 stocks, 142 stocks, 190 stocks, 239 stocks, and 287 stocks. Stock prices and trading volume were collected on a weekly basis from the six largest European stock exchanges (FTSE100, CAC40, FTSE MIB, IBEX35, DAX, and MDAX). Markowitz’s (1952) diversification formula has been used to measure the risk level of the individual portfolios. The results of the study show that the diversification risk constantly decreases when we move from the portfolios with 47 stocks to the portfolios with 287 stocks. The weighted average returns increase on the portfolios with a higher number of stocks, which is contrary to the standard portfolio theories. The results of the study indicate managerial implications for financial investors that are focused exclusively on the largest European stock exchanges.
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Gubu, La, Dedi Rosadi, and Abdurakhman Abdurakhman. "Pembentukan Portofolio Saham Menggunakan Klastering Time Series K-Medoid dengan Ukuran Jarak Dynamic Time Warping." Jurnal Aplikasi Statistika & Komputasi Statistik 13, no. 2 (December 31, 2021): 35–46. http://dx.doi.org/10.34123/jurnalasks.v13i2.295.
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This research will present the formation of stock portfolios by preprocessing data using time series clustering with a distance measure of Dynamic Time Warping (DTW). First, stocks are grouped into several clusters using the Partitioning Around Medoids (PAM) time series cluster based on the DTW distance measure. After the clustering process, stocks are selected to represent each cluster to build the optimum portfolio. The stock selected from each cluster is the one with the highest Sharpe ratio. The optimal portfolio is determined using three portfolio models, namely: the classic MV portfolio model, the FMCD robust MV portfolio model and the S robust portfolio model. Using this procedure, an optimum portfolio can be obtained efficiently if there are many stocks involved in the portfolio formation process. Sharpe ratio is used to measure the performance of the portfolios. The results of the empirical study show that the portfolio performance generated using the PAM time series clustering with DWT distance dissimilarity measure combined with the classic MV portfolio model outperforms the resulting portfolio performance in combination with other models.
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White, Edward M. "The Scoring of Writing Portfolios: Phase 2." College Composition & Communication 56, no. 4 (June 1, 2005): 581–600. http://dx.doi.org/10.58680/ccc20054823.
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Although most portfolio evaluation currently uses some adaptation of holistic scoring, the problems with scoring portfolios holistically are many, much more than for essays, and the problems are not readily resolvable. Indeed, many aspects of holistic scoring work against the principles behind portfolio assessment. We have from the start needed a scoring methodology that responds to and reflects the nature of portfolios, not merely an adaptation of essay scoring. I here propose a means for scoring portfolios that allows for relatively efficient grading where portfolio scores are needed and where time and money are in short supply. It is derived conceptually from portfolio theory rather than essay-testing theory and supports the key principle behind portfolios, that students should be involved with reflection about and assessment of their own work. It is time for the central role that reflective writing can play in portfolio scoring to be put into practice.
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Gao, Wenxiang. "Portfolio Optimization Based on U.S. Stock." Advances in Economics, Management and Political Sciences 59, no. 1 (January 5, 2024): 258–64. http://dx.doi.org/10.54254/2754-1169/59/20231130.
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In the financial domain, portfolio optimization is of immense significance due to its potential for risk diversification and return enhancement. Particularly in today's society where inflation is on the rise, a well-constructed portfolio can effectively assist investors in maintaining or increasing real assets. This study selected 10 U.S. stocks and analyzed their data for the past decade. Utilizing Monte Carlo simulation, the study identified the efficient frontier and constructed three types of portfolios: equal-weighted (1/N) portfolio, global minimum variance portfolio (hereinafter referred to as GMVP), and maximum Sharpe ratio portfolio. The findings show that Microsoft and Nvidia have the greatest investment weights in the portfolio with the maximum Sharpe ratio. In the GMVP, JNJ and KO have the highest investment weights. By contrasting the three portfolios' combined performance with the S&P 500 index, both the maximum Sharpe ratio and the 1/N portfolios outperformed the benchmark index, while the GMVP performed below the benchmark. The findings of this study may offer insights into portfolio management for investors looking to increase their real assets.
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Rubesam, Alexandre, and André Lomonaco Beltrame. "Carteiras de Variância Mínima no Brasil." Brazilian Review of Finance 11, no. 1 (May 30, 2013): 81. http://dx.doi.org/10.12660/rbfin.v11n1.2013.5830.
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We investigate minimum variance portfolios in the Brazilian equity market using different methods to estimate the covariance matrix, from the simple model of using the sample covariance to multivariate GARCH models. We compare the performance of the minimum variance portfolios to those of the following benchmarks: (i) the IBOVESPA equity index, (ii) an equally-weighted portfolio, (iii) the maximum Sharpe ratio portfolio and (iv) the maximum growth portfolio. Our results show that the minimum variance portfolio has higher returns with lower risk compared to the benchmarks. We also consider long-short 130/30 minimum variance portfolios and obtain similar results. The minimum variance portfolio invests in relatively few stocks with low βs measured with respect to the IBOVESPA index, being easily replicable by individual and institutional investors alike.
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Luo, Nan. "Optimized Portfolio Structured by 5 Stock Indexes." Advances in Economics, Management and Political Sciences 24, no. 1 (September 13, 2023): 13–19. http://dx.doi.org/10.54254/2754-1169/24/20230406.
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Equity market declines fiercely caused by the current liquidity crisis. Investors pay more attention to optimized portfolio in order to reduce losses. 5 representative indexes were chosen from capital market to construct financial portfolios. The sketch of the efficient frontier was plotted, and portfolios with the 1/N, the maximum sharpe ratio and the minimum volatility were acquired based on 10000 scipy optimization calculations, the FTSE100 accounts for the largest section of the portfolio with the maximum sharpe ratio while the SSE Composite Index possesses the biggest shares in the portfolio with minimum volatility. Comparing three portfolio returns of accumulations, it could be observed that the maximum sharpe ratio portfolios performance surpassed the 1/N and the minimum variance portfolio, several investors could make reasonable decisions refer to this result in the special tough period.
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Ji, Xinyue. "Comparison of Portfolio Optimizations under Markowitz Model in Technology Sector and Financial Services Sector." Highlights in Business, Economics and Management 24 (January 22, 2024): 1194–202. http://dx.doi.org/10.54097/32f00f69.
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In the period of Covid-19, different sectors received different levels of shocks, which gave investors a degree of caution when investing in various sectors. Therefore, portfolio optimization - using specific model to assign weights of stocks to achieve a higher return while reducing risk – becomes a popular strategy. This paper chooses the Markowitz model to find optimal sector-based portfolios, specifically in technology sector and financial services sector, as well as portfolios that contains stocks in both sectors. The study uses Python to do Monte Carlo simulation, finding two optimal portfolios with maximum Sharpe ratio and minimum volatility for each sector(s), and finally comparing performances to test if the sector-based portfolio works better than the inter-sector portfolio. According to results, the minimum volatility portfolio in combined sectors reaches the same return of 0.11 as the minimum volatility portfolio in technology sector, but with lower volatility. It means the inter-sectors portfolio is better off when seeking minimum volatility. On the other hand, the maximum Sharpe ratio portfolios in technology sector, financial services sector, and combined sectors have values of returns and volatility ordering from highest to lowest. As a result, with current information, without investors’ investment preference, the optimal maximum Sharpe ratio portfolio cannot be determined and needed further exploration.
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Prakash, A. Arun. "A Study on Comparison of Index Returns and Returns of Portfolio Created Using Equal Weight Age Index Method." International Journal of Advances in Management and Economics 9, no. 2 (February 28, 2020): 28–31. http://dx.doi.org/10.31270/ijame/v09/i02/2020/3.
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The main aim of the study is to select a list of companies from the basic indices of NSE like Nifty50, Next50 and Small Cap 50, to create a portfolio of the companies with respect to the indices selected for the study which results in 3 different portfolios. Portfolio 1 represents 29 scrips selected from Nifty50, Portfolio 2 represents 30 scrips selected from the Next50 index, and Portfolio 3 represents 34 scrips selected from Small Cap index. After creating the portfolios, Each scrip under each portfolio will be invested with Rs.1,00,000, each scrip in each portfolio will be given equal weightage so the total investment will be Rs.29,00,000, Rs.30,00,000 and Rs.34,00,000, under Portfolio 1, 2 and 3 respectively. To prove the factor that the returns of the created portfolio has beaten the index returns the price data of all the scrips for the past 10 years were taken and the returns were computed for the portfolio year on year and from 2008 to 2018. Keywords: Index Returns, Returns of Portfolio, Equal Weight Age Index Method
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Zhu, Hongbing, and Lihua Yang. "portfolio: A command for conducting portfolio analysis in Stata." Stata Journal: Promoting communications on statistics and Stata 22, no. 4 (December 2022): 941–57. http://dx.doi.org/10.1177/1536867x221141021.
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Portfolio analysis is widely used in empirical asset pricing to explore the cross-sectional relation between two or more variables. In this article, we introduce the methodology of portfolio analysis and describe a new command, portfolio, that provides a one-step solution for portfolio analysis. portfolio calculates the equal- or value-weighted returns with a t statistic for the portfolio and tests the significance of a long-short strategy in portfolios. portfolio also provides the Newey–West standard-error adjustment option for alleviating the impact of potential autocorrelation and heteroskedasticity in financial time series.
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Simonian, Joseph. "Policy Portfolios and Portfolio Characteristics." Journal of Portfolio Management 46, no. 1 (September 12, 2019): 52–59. http://dx.doi.org/10.3905/jpm.2019.1.108.
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