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analysis</title> <meta name="description" content="Search results for: portfolio analysis"> <meta name="keywords" content="portfolio analysis"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="portfolio analysis"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 27978</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: portfolio analysis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27978</span> A Mean–Variance–Skewness Portfolio Optimization Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Metaxiotis">Kostas Metaxiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title="evolutionary algorithms">evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=skewness" title=" skewness"> skewness</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20selection" title=" stock selection"> stock selection</a> </p> <a href="https://publications.waset.org/abstracts/102472/a-mean-variance-skewness-portfolio-optimization-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102472.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">198</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27977</span> Analyzing Essential Patents of Mobile Communication Based on Patent Portfolio: Case Study of Long Term Evolution-Advanced </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kujhin%20Jeong">Kujhin Jeong</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungjoo%20Lee"> Sungjoo Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the past, cross-licensing was made up of various application or commercial patents. Today, cross-licensing is restricted to essential patents, which has emphasized their importance significantly. Literature has shown that patent portfolio provides information for patent protection or strategy decision-making, but little empirical research has found strategic tool of essential patents. This paper will highlight four types of essential patent portfolio and analysis about each strategy in the field of LTE-A. Specifically we collected essential patents of mobile communication company through ETSI (European Telecommunication Standards Institute) and build-up portfolio activity, concentration, diversity, and quality. Using these portfolios, we can understand each company’s strategic character about the technology of LTE-A and comparison analysis of financial results. Essential patents portfolio displays a mobile communication company’s strategy and its strategy’s impact on the performance of a company. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=essential%20patent" title="essential patent">essential patent</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio" title=" portfolio"> portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=patent%20portfolio" title=" patent portfolio"> patent portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=essential%20patent%20portfolio" title=" essential patent portfolio"> essential patent portfolio</a> </p> <a href="https://publications.waset.org/abstracts/43598/analyzing-essential-patents-of-mobile-communication-based-on-patent-portfolio-case-study-of-long-term-evolution-advanced" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43598.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">393</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27976</span> The Empirical Analysis and Comparisons Using TAIEX Derivatives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pao-Peng%20Hsu">Pao-Peng Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying-Hsiu%20Chen"> Ying-Hsiu Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Historical data shows that there were high correlations among TAIEX Futures, Electronic Sector Index Futures, Finance Sector Index Futures and Taiwan Top 50 ETF. The performance under various futures is also discussed. We found that the worst portfolio is consisted of T50-ETF and T50-ETF futures and best portfolio is consisted of T50-ETF and TF. It implies that the annual return of a portfolio increases if a portfolio’s risk diversifies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arbitrage%20opportunities" title="arbitrage opportunities">arbitrage opportunities</a>, <a href="https://publications.waset.org/abstracts/search?q=ETF" title=" ETF"> ETF</a>, <a href="https://publications.waset.org/abstracts/search?q=futures" title=" futures"> futures</a>, <a href="https://publications.waset.org/abstracts/search?q=TAIEX" title=" TAIEX"> TAIEX</a> </p> <a href="https://publications.waset.org/abstracts/35758/the-empirical-analysis-and-comparisons-using-taiex-derivatives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35758.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">383</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27975</span> The Impact of Transaction Costs on Rebalancing an Investment Portfolio in Portfolio Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Marasovi%C4%87">B. Marasović</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Pivac"> S. Pivac</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20V.%20Vukasovi%C4%87"> S. V. Vukasović</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Constructing a portfolio of investments is one of the most significant financial decisions facing individuals and institutions. In accordance with the modern portfolio theory maximization of return at minimal risk should be the investment goal of any successful investor. In addition, the costs incurred when setting up a new portfolio or rebalancing an existing portfolio must be included in any realistic analysis. In this paper rebalancing an investment portfolio in the presence of transaction costs on the Croatian capital market is analyzed. The model applied in the paper is an extension of the standard portfolio mean-variance optimization model in which transaction costs are incurred to rebalance an investment portfolio. This model allows different costs for different securities, and different costs for buying and selling. In order to find efficient portfolio, using this model, first, the solution of quadratic programming problem of similar size to the Markowitz model, and then the solution of a linear programming problem have to be found. Furthermore, in the paper the impact of transaction costs on the efficient frontier is investigated. Moreover, it is shown that global minimum variance portfolio on the efficient frontier always has the same level of the risk regardless of the amount of transaction costs. Although efficient frontier position depends of both transaction costs amount and initial portfolio it can be concluded that extreme right portfolio on the efficient frontier always contains only one stock with the highest expected return and the highest risk. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Croatian%20capital%20market" title="Croatian capital market">Croatian capital market</a>, <a href="https://publications.waset.org/abstracts/search?q=Markowitz%20model" title=" Markowitz model"> Markowitz model</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20quadratic%20programming" title=" fractional quadratic programming"> fractional quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=transaction%20costs" title=" transaction costs"> transaction costs</a> </p> <a href="https://publications.waset.org/abstracts/21383/the-impact-of-transaction-costs-on-rebalancing-an-investment-portfolio-in-portfolio-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21383.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">385</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27974</span> A Comparative Analysis of Global Minimum Variance and Naïve Portfolios: Performance across Stock Market Indices and Selected Economic Regimes Using Various Risk-Return Metrics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lynmar%20M.%20Didal">Lynmar M. Didal</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramises%20G.%20Manzano%20Jr."> Ramises G. Manzano Jr.</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacque%20Bon-Isaac%20C.%20Aboy"> Jacque Bon-Isaac C. Aboy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study analyzes the performance of global minimum variance and naive portfolios across different economic periods, using monthly stock returns from the Philippine Stock Exchange Index (PSEI), S&P 500, and Dow Jones Industrial Average (DOW). The performance is evaluated through the Sharpe ratio, Sortino ratio, Jensen’s Alpha, Treynor ratio, and Information ratio. Additionally, the study investigates the impact of short selling on portfolio performance. Six-time periods are defined for analysis, encompassing events such as the global financial crisis and the COVID-19 pandemic. Findings indicate that the Naive portfolio generally outperforms the GMV portfolio in the S&P 500, signifying higher returns with increased volatility. Conversely, in the PSEI and DOW, the GMV portfolio shows more efficient risk-adjusted returns. Short selling significantly impacts the GMV portfolio during mid-GFC and mid-COVID periods. The study offers insights for investors, suggesting the Naive portfolio for higher risk tolerance and the GMV portfolio as a conservative alternative. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=portfolio%20performance" title="portfolio performance">portfolio performance</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20minimum%20variance" title=" global minimum variance"> global minimum variance</a>, <a href="https://publications.waset.org/abstracts/search?q=na%C3%AFve%20portfolio" title=" naïve portfolio"> naïve portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=risk-adjusted%20metrics" title=" risk-adjusted metrics"> risk-adjusted metrics</a>, <a href="https://publications.waset.org/abstracts/search?q=short-selling" title=" short-selling"> short-selling</a> </p> <a href="https://publications.waset.org/abstracts/171550/a-comparative-analysis-of-global-minimum-variance-and-naive-portfolios-performance-across-stock-market-indices-and-selected-economic-regimes-using-various-risk-return-metrics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171550.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">96</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27973</span> Mathematical Programming Models for Portfolio Optimization Problem: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mazura%20Mokhtar">Mazura Mokhtar</a>, <a href="https://publications.waset.org/abstracts/search?q=Adibah%20Shuib"> Adibah Shuib</a>, <a href="https://publications.waset.org/abstracts/search?q=Daud%20Mohamad"> Daud Mohamad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization problem has received a lot of attention from both researchers and practitioners over the last six decades. This paper provides an overview of the current state of research in portfolio optimization with the support of mathematical programming techniques. On top of that, this paper also surveys the solution algorithms for solving portfolio optimization models classifying them according to their nature in heuristic and exact methods. To serve these purposes, 40 related articles appearing in the international journal from 2003 to 2013 have been gathered and analyzed. Based on the literature review, it has been observed that stochastic programming and goal programming constitute the highest number of mathematical programming techniques employed to tackle the portfolio optimization problem. It is hoped that the paper can meet the needs of researchers and practitioners for easy references of portfolio optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title="portfolio optimization">portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20programming" title=" mathematical programming"> mathematical programming</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20programming" title=" multi-objective programming"> multi-objective programming</a>, <a href="https://publications.waset.org/abstracts/search?q=solution%20approaches" title=" solution approaches"> solution approaches</a> </p> <a href="https://publications.waset.org/abstracts/2654/mathematical-programming-models-for-portfolio-optimization-problem-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2654.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27972</span> Optimal Portfolio Selection under Treynor Ratio Using Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imad%20Zeyad%20Ramadan">Imad Zeyad Ramadan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a genetic algorithm was developed to construct the optimal portfolio based on the Treynor method. The GA maximizes the Treynor ratio under budget constraint to select the best allocation of the budget for the companies in the portfolio. The results show that the GA was able to construct a conservative portfolio which includes companies from the three sectors. This indicates that the GA reduced the risk on the investor as it choose some companies with positive risks (goes with the market) and some with negative risks (goes against the market). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=oOptimization" title="oOptimization">oOptimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20selection" title=" portfolio selection"> portfolio selection</a>, <a href="https://publications.waset.org/abstracts/search?q=Treynor%20method" title=" Treynor method"> Treynor method</a> </p> <a href="https://publications.waset.org/abstracts/43388/optimal-portfolio-selection-under-treynor-ratio-using-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43388.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">449</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27971</span> Advanced Technologies and Algorithms for Efficient Portfolio Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Liagkouras">Konstantinos Liagkouras</a>, <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Metaxiotis"> Konstantinos Metaxiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present a classification of the various technologies applied for the solution of the portfolio selection problem according to the discipline and the methodological framework followed. We provide a concise presentation of the emerged categories and we are trying to identify which methods considered obsolete and which lie at the heart of the debate. On top of that, we provide a comparative study of the different technologies applied for efficient portfolio construction and we suggest potential paths for future work that lie at the intersection of the presented techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=portfolio%20selection" title="portfolio selection">portfolio selection</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20techniques" title=" optimization techniques"> optimization techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20models" title=" financial models"> financial models</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic" title=" stochastic"> stochastic</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a> </p> <a href="https://publications.waset.org/abstracts/31917/advanced-technologies-and-algorithms-for-efficient-portfolio-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31917.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27970</span> Assessment of Korea's Natural Gas Portfolio Considering Panama Canal Expansion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Juhan%20Kim">Juhan Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinsoo%20Kim"> Jinsoo Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> South Korea cannot import natural gas in any form other than LNG because of the division of South and North Korea. Further, the high proportion of natural gas in the national energy mix makes this resource crucial for energy security in Korea. Expansion of Panama Canal will allow for reducing the cost of shipping between the Far East and U.S East. Panama Canal expansion can have significant impacts on South Korea. Due to this situation, we review the natural gas optimal portfolio by considering the uniqueness of the Korean Natural gas market and expansion of Panama Canal. In order to assess Korea’s natural gas optimal portfolio, we developed natural gas portfolio model. The model comprises two steps. First, to obtain the optimal long-term spot contract ratio, the study examines the price level and the correlation between spot and long-term contracts by using the Markowitz, portfolio model. The optimal long-term spot contract ratio follows the efficient frontier of the cost/risk level related to this price level and degree of correlation. Second, by applying the obtained long-term contract purchase ratio as the constraint in the linear programming portfolio model, we determined the natural gas optimal import portfolio that minimizes total intangible and tangible costs. Using this model, we derived the optimal natural gas portfolio considering the expansion of Panama Canal. Based on these results, we assess the portfolio for natural gas import to Korea from the perspective of energy security and present some relevant policy proposals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20gas" title="natural gas">natural gas</a>, <a href="https://publications.waset.org/abstracts/search?q=Panama%20Canal" title=" Panama Canal"> Panama Canal</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20analysis" title=" portfolio analysis"> portfolio analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Korea" title=" South Korea"> South Korea</a> </p> <a href="https://publications.waset.org/abstracts/67569/assessment-of-koreas-natural-gas-portfolio-considering-panama-canal-expansion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67569.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">291</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27969</span> Portfolio Selection with Active Risk Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marc%20S.%20Paolella">Marc S. Paolella</a>, <a href="https://publications.waset.org/abstracts/search?q=Pawel%20Polak"> Pawel Polak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and non-ellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio optimization with active risk monitoring. The former selects optimal portfolio weights. The latter, independently, initiates market exit in case of excessive risks. The strategy agrees with the stylized fact of stock market major sell-offs during the initial stage of market downturns. The advantages of the new framework are illustrated with an extensive empirical study. It leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk fear portfolio strategy outperforms various competing types of optimal portfolios, even in the presence of conservative transaction costs and frequent rebalancing. The risk monitoring of the optimal portfolio can serve as an early warning system against large market risks. In particular, the new strategy avoids all the losses during the 2008 financial crisis, and it profits from the subsequent market recovery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comfort" title="comfort">comfort</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20crises" title=" financial crises"> financial crises</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20monitoring" title=" risk monitoring"> risk monitoring</a> </p> <a href="https://publications.waset.org/abstracts/28504/portfolio-selection-with-active-risk-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">524</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27968</span> Evaluating Portfolio Performance by Highlighting Network Property and the Sharpe Ratio in the Stock Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahra%20Hatami">Zahra Hatami</a>, <a href="https://publications.waset.org/abstracts/search?q=Hesham%20Ali"> Hesham Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Volkman"> David Volkman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Selecting a portfolio for investing is a crucial decision for individuals and legal entities. In the last two decades, with economic globalization, a stream of financial innovations has rushed to the aid of financial institutions. The importance of selecting stocks for the portfolio is always a challenging task for investors. This study aims to create a financial network to identify optimal portfolios using network centralities metrics. This research presents a community detection technique of superior stocks that can be described as an optimal stock portfolio to be used by investors. By using the advantages of a network and its property in extracted communities, a group of stocks was selected for each of the various time periods. The performance of the optimal portfolios compared to the famous index. Their Sharpe ratio was calculated in a timely manner to evaluate their profit for making decisions. The analysis shows that the selected potential portfolio from stocks with low centrality measurement can outperform the market; however, they have a lower Sharpe ratio than stocks with high centrality scores. In other words, stocks with low centralities could outperform the S&P500 yet have a lower Sharpe ratio than high central stocks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=portfolio%20management%20performance" title="portfolio management performance">portfolio management performance</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20analysis" title=" network analysis"> network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=centrality%20measurements" title=" centrality measurements"> centrality measurements</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharpe%20ratio" title=" Sharpe ratio"> Sharpe ratio</a> </p> <a href="https://publications.waset.org/abstracts/148342/evaluating-portfolio-performance-by-highlighting-network-property-and-the-sharpe-ratio-in-the-stock-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148342.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27967</span> Mathematical Model of Corporate Bond Portfolio and Effective Border Preview</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Podluzhnyy">Sergey Podluzhnyy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most important tasks of investment and pension fund management is building decision support system which helps to make right decision on corporate bond portfolio formation. Today there are several basic methods of bond portfolio management. They are duration management, immunization and convexity management. Identified methods have serious disadvantage: they do not take into account credit risk or insolvency risk of issuer. So, identified methods can be applied only for management and evaluation of high-quality sovereign bonds. Applying article proposes mathematical model for building an optimal in case of risk and yield corporate bond portfolio. Proposed model takes into account the default probability in formula of assessment of bonds which results to more correct evaluation of bonds prices. Moreover, applied model provides tools for visualization of the efficient frontier of corporate bonds portfolio taking into account the exposure to credit risk, which will increase the quality of the investment decisions of portfolio managers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corporate%20bond%20portfolio" title="corporate bond portfolio">corporate bond portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=default%20probability" title=" default probability"> default probability</a>, <a href="https://publications.waset.org/abstracts/search?q=effective%20boundary" title=" effective boundary"> effective boundary</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization%20task" title=" portfolio optimization task"> portfolio optimization task</a> </p> <a href="https://publications.waset.org/abstracts/59174/mathematical-model-of-corporate-bond-portfolio-and-effective-border-preview" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59174.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27966</span> An Automated Stock Investment System Using Machine Learning Techniques: An Application in Australia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carol%20Anne%20Hargreaves">Carol Anne Hargreaves</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20trading" title=" stock market trading"> stock market trading</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20analysis" title=" cluster analysis"> cluster analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=factor%20analysis" title=" factor analysis"> factor analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=automated%20stock%20investment%20system" title=" automated stock investment system"> automated stock investment system</a> </p> <a href="https://publications.waset.org/abstracts/90984/an-automated-stock-investment-system-using-machine-learning-techniques-an-application-in-australia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90984.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27965</span> Smart Beta Portfolio Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saud%20Al%20Mahdi">Saud Al Mahdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditionally,portfolio managers have been discouraged from timing the market. This means, for example, that equity managers have been forced to adhere strictly to a benchmark with static or relatively stable components, such as the SP 500 or the Russell 3000. This means that the portfolio’s exposures to all risk factors should mimic as closely as possible the corresponding exposures of the benchmark. The main risk factor, of course, is the market itself. Effectively, a long-only portfolio would be constrained to have a beta 1. More recently, however, managers have been given greater discretion to adjust their portfolio’s risk exposures (in particular, the beta of their portfolio) dynamically to match the manager’s beliefs about future performance of the risk factors themselves. This freedom translates into the manager’s ability to adjust the portfolio’s beta dynamically. These strategies have come to be known as smart beta strategies. Adjusting beta dynamically amounts to attempting to "time" the market; that is, to increase exposure when one anticipates that the market will rise, and to decrease it when one anticipates that the market will fall. Traditionally, market timing has been believed to be impossible to perform effectively and consistently. Moreover, if a majority of market participants do it, their combined actions could destabilize the market. The aim of this project is to investigate so-called smart beta strategies to determine if they really can add value, or if they are merely marketing gimmicks used to sell dubious investment strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beta" title="beta">beta</a>, <a href="https://publications.waset.org/abstracts/search?q=alpha" title=" alpha"> alpha</a>, <a href="https://publications.waset.org/abstracts/search?q=active%20portfolio%20management" title=" active portfolio management"> active portfolio management</a>, <a href="https://publications.waset.org/abstracts/search?q=trading%20strategies" title=" trading strategies "> trading strategies </a> </p> <a href="https://publications.waset.org/abstracts/28119/smart-beta-portfolio-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28119.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27964</span> Leveraging Deep Q Networks in Portfolio Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Liu">Peng Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep Q networks (DQNs) represent a significant advancement in reinforcement learning, utilizing neural networks to approximate the optimal Q-value for guiding sequential decision processes. This paper presents a comprehensive introduction to reinforcement learning principles, delves into the mechanics of DQNs, and explores its application in portfolio optimization. By evaluating the performance of DQNs against traditional benchmark portfolios, we demonstrate its potential to enhance investment strategies. Our results underscore the advantages of DQNs in dynamically adjusting asset allocations, offering a robust portfolio management framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title="deep reinforcement learning">deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20Q%20networks" title=" deep Q networks"> deep Q networks</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-period%20optimization" title=" multi-period optimization"> multi-period optimization</a> </p> <a href="https://publications.waset.org/abstracts/189031/leveraging-deep-q-networks-in-portfolio-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189031.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">32</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27963</span> Using Analytic Hierarchy Process as a Decision-Making Tool in Project Portfolio Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Darius%20Danesh">Darius Danesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20J.%20Ryan"> Michael J. Ryan</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Abbasi"> Alireza Abbasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Project Portfolio Management (PPM) is an essential component of an organisation’s strategic procedures, which requires attention of several factors to envisage a range of long-term outcomes to support strategic project portfolio decisions. To evaluate overall efficiency at the portfolio level, it is essential to identify the functionality of specific projects as well as to aggregate those findings in a mathematically meaningful manner that indicates the strategic significance of the associated projects at a number of levels of abstraction. PPM success is directly associated with the quality of decisions made and poor judgment increases portfolio costs. Hence, various Multi-Criteria Decision Making (MCDM) techniques have been designed and employed to support the decision-making functions. This paper reviews possible option to improve the decision-making outcomes in the organisational portfolio management processes using the Analytic Hierarchy Process (AHP) both from academic and practical perspectives and will examine the usability, certainty and quality of the technique. The results of the study will also provide insight into the technical risk associated with current decision-making model to underpin initiative tracking and strategic portfolio management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytic%20hierarchy%20process" title="analytic hierarchy process">analytic hierarchy process</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20systems" title=" decision support systems"> decision support systems</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-criteria%20decision%20making" title=" multi-criteria decision making"> multi-criteria decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20portfolio%20management" title=" project portfolio management"> project portfolio management</a> </p> <a href="https://publications.waset.org/abstracts/39497/using-analytic-hierarchy-process-as-a-decision-making-tool-in-project-portfolio-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39497.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">321</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27962</span> Interaction between Mutual Fund Performance and Portfolio Turnover</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sheng-Ching%20Wu">Sheng-Ching Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines the interaction between mutual fund performance and portfolio turnover. Active trading could affect fund performance, but underperforming funds could also be traded actively at the same time to perform well. Therefore, we used two-stage least squares to address with simultaneity. The results indicate that funds with higher portfolio turnovers exhibit inferior performance compared with funds having lower turnovers. Moreover, funds with poor performance exhibit higher portfolio turnover. The findings support the assumptions that active trading erodes performance, and that fund managers with poor performance attempt to trade actively to retain employment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mutual%20funds" title="mutual funds">mutual funds</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20turnover" title=" portfolio turnover"> portfolio turnover</a>, <a href="https://publications.waset.org/abstracts/search?q=simultaneity" title=" simultaneity"> simultaneity</a>, <a href="https://publications.waset.org/abstracts/search?q=two-stage%20least%20squares" title=" two-stage least squares"> two-stage least squares</a> </p> <a href="https://publications.waset.org/abstracts/8033/interaction-between-mutual-fund-performance-and-portfolio-turnover" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8033.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">442</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27961</span> Dynamic Correlations and Portfolio Optimization between Islamic and Conventional Equity Indexes: A Vine Copula-Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imen%20Dhaou">Imen Dhaou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examines conditional Value at Risk by applying the GJR-EVT-Copula model, and finds the optimal portfolio for eight Dow Jones Islamic-conventional pairs. Our methodology consists of modeling the data by a bivariate GJR-GARCH model in which we extract the filtered residuals and then apply the Peak over threshold model (POT) to fit the residual tails in order to model marginal distributions. After that, we use pair-copula to find the optimal portfolio risk dependence structure. Finally, with Monte Carlo simulations, we estimate the Value at Risk (VaR) and the conditional Value at Risk (CVaR). The empirical results show the VaR and CVaR values for an equally weighted portfolio of Dow Jones Islamic-conventional pairs. In sum, we found that the optimal investment focuses on Islamic-conventional US Market index pairs because of high investment proportion; however, all other index pairs have low investment proportion. These results deliver some real repercussions for portfolio managers and policymakers concerning to optimal asset allocations, portfolio risk management and the diversification advantages of these markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CVaR" title="CVaR">CVaR</a>, <a href="https://publications.waset.org/abstracts/search?q=Dow%20Jones%20Islamic%20index" title=" Dow Jones Islamic index"> Dow Jones Islamic index</a>, <a href="https://publications.waset.org/abstracts/search?q=GJR-GARCH-EVT-pair%20copula" title=" GJR-GARCH-EVT-pair copula"> GJR-GARCH-EVT-pair copula</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a> </p> <a href="https://publications.waset.org/abstracts/81937/dynamic-correlations-and-portfolio-optimization-between-islamic-and-conventional-equity-indexes-a-vine-copula-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81937.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27960</span> Optimization of Black-Litterman Model for Portfolio Assets Allocation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Hidalgo">A. Hidalgo</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Desportes"> A. Desportes</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Bonin"> E. Bonin</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Kadaoui"> A. Kadaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Bouaricha"> T. Bouaricha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Present paper is concerned with portfolio management with Black-Litterman (B-L) model. Considered stocks are exclusively limited to large companies stocks on US market. Results obtained by application of the model are presented. From analysis of collected Dow Jones stock data, remarkable explicit analytical expression of optimal B-L parameter τ, which scales dispersion of normal distribution of assets mean return, is proposed in terms of standard deviation of covariance matrix. Implementation has been developed in Matlab environment to split optimization in Markovitz sense from specific elements related to B-L representation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Black-Litterman" title="Black-Litterman">Black-Litterman</a>, <a href="https://publications.waset.org/abstracts/search?q=Markowitz" title=" Markowitz"> Markowitz</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20data" title=" market data"> market data</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20manager%20opinion" title=" portfolio manager opinion"> portfolio manager opinion</a> </p> <a href="https://publications.waset.org/abstracts/6712/optimization-of-black-litterman-model-for-portfolio-assets-allocation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6712.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27959</span> Median-Based Nonparametric Estimation of Returns in Mean-Downside Risk Portfolio Frontier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Ben%20Salah">H. Ben Salah</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Gannoun"> A. Gannoun</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20de%20Peretti"> C. de Peretti</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Trabelsi"> A. Trabelsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Downside Risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical mean-variance model concerning the asymetry of returns and the risk perception of investors. This model optimization deals with a positive definite matrix that is endogenous with respect to portfolio weights. This aspect makes the problem far more difficult to handle. For this purpose, Athayde (2001) developped a new recurcive minimization procedure that ensures the convergence to the solution. However, when a finite number of observations is available, the portfolio frontier presents an appearance which is not very smooth. In order to overcome that, Athayde (2003) proposed a mean kernel estimation of the returns, so as to create a smoother portfolio frontier. This technique provides an effect similar to the case in which we had continuous observations. In this paper, taking advantage on the the robustness of the median, we replace the mean estimator in Athayde's model by a nonparametric median estimator of the returns. Then, we give a new version of the former algorithm (of Athayde (2001, 2003)). We eventually analyse the properties of this improved portfolio frontier and apply this new method on real examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Downside%20Risk" title="Downside Risk">Downside Risk</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20Method" title=" Kernel Method"> Kernel Method</a>, <a href="https://publications.waset.org/abstracts/search?q=Median" title=" Median"> Median</a>, <a href="https://publications.waset.org/abstracts/search?q=Nonparametric%20%20Estimation" title=" Nonparametric Estimation"> Nonparametric Estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Semivariance" title=" Semivariance"> Semivariance</a> </p> <a href="https://publications.waset.org/abstracts/19062/median-based-nonparametric-estimation-of-returns-in-mean-downside-risk-portfolio-frontier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19062.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">492</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27958</span> Portfolio Risk Management Using Quantum Annealing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Doutre">Thomas Doutre</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20De%20Meric%20De%20Bellefon"> Emmanuel De Meric De Bellefon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes the application of local-search metaheuristic quantum annealing to portfolio opti- mization. Heuristic technics are particularly handy when Markowitz’ classical Mean-Variance problem is enriched with additional realistic constraints. Once tailored to the problem, computational experiments on real collected data have shown the superiority of quantum annealing over simulated annealing for this constrained optimization problem, taking advantages of quantum effects such as tunnelling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20risk%20management" title=" portfolio risk management"> portfolio risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20annealing" title=" quantum annealing"> quantum annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic" title=" metaheuristic"> metaheuristic</a> </p> <a href="https://publications.waset.org/abstracts/40564/portfolio-risk-management-using-quantum-annealing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40564.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">383</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27957</span> Role of Cryptocurrency in Portfolio Diversification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Onur%20Arugaslan">Onur Arugaslan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ajay%20Samant"> Ajay Samant</a>, <a href="https://publications.waset.org/abstracts/search?q=Devrim%20Yaman"> Devrim Yaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Financial advisors and investors seek new assets which could potentially increase portfolio returns and decrease portfolio risk. Cryptocurrencies represent a relatively new asset class which could serve in both these roles. There has been very little research done in the area of the risk/return tradeoff in a portfolio consisting of fixed income assets, stocks, and cryptocurrency. The objective of this study is a rigorous examination of this issue. The data used in the study are the monthly returns on 4-week US Treasury Bills, S&P Investment Grade Corporate Bond Index, Bitcoin and the S&P 500 Stock Index. The methodology used in the study is the application Modern Portfolio Theory to evaluate the risk-adjusted returns of portfolios with varying combinations of these assets, using Sharpe, Treynor and Jensen Indexes, as well as the Sortino and Modigliani measures. The results of the study would include the ranking of various investment portfolios based on their risk/return characteristics. The conclusions of the study would include objective empirical inference for investors who are interested in including cryptocurrency in their asset portfolios but are unsure of the risk/return implications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20economics" title="financial economics">financial economics</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20diversification" title=" portfolio diversification"> portfolio diversification</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20income%20securities" title=" fixed income securities"> fixed income securities</a>, <a href="https://publications.waset.org/abstracts/search?q=cryptocurrency" title=" cryptocurrency"> cryptocurrency</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20indexes" title=" stock indexes"> stock indexes</a> </p> <a href="https://publications.waset.org/abstracts/173618/role-of-cryptocurrency-in-portfolio-diversification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173618.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27956</span> Numerical Solution of Portfolio Selecting Semi-Infinite Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alina%20Fedossova">Alina Fedossova</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Jorge%20Sierra%20Molina"> Jose Jorge Sierra Molina </a> </p> <p class="card-text"><strong>Abstract:</strong></p> SIP problems are part of non-classical optimization. There are problems in which the number of variables is finite, and the number of constraints is infinite. These are semi-infinite programming problems. Most algorithms for semi-infinite programming problems reduce the semi-infinite problem to a finite one and solve it by classical methods of linear or nonlinear programming. Typically, any of the constraints or the objective function is nonlinear, so the problem often involves nonlinear programming. An investment portfolio is a set of instruments used to reach the specific purposes of investors. The risk of the entire portfolio may be less than the risks of individual investment of portfolio. For example, we could make an investment of M euros in N shares for a specified period. Let yi> 0, the return on money invested in stock i for each dollar since the end of the period (i = 1, ..., N). The logical goal here is to determine the amount xi to be invested in stock i, i = 1, ..., N, such that we maximize the period at the end of ytx value, where x = (x1, ..., xn) and y = (y1, ..., yn). For us the optimal portfolio means the best portfolio in the ratio "risk-return" to the investor portfolio that meets your goals and risk ways. Therefore, investment goals and risk appetite are the factors that influence the choice of appropriate portfolio of assets. The investment returns are uncertain. Thus we have a semi-infinite programming problem. We solve a semi-infinite optimization problem of portfolio selection using the outer approximations methods. This approach can be considered as a developed Eaves-Zangwill method applying the multi-start technique in all of the iterations for the search of relevant constraints' parameters. The stochastic outer approximations method, successfully applied previously for robotics problems, Chebyshev approximation problems, air pollution and others, is based on the optimal criteria of quasi-optimal functions. As a result we obtain mathematical model and the optimal investment portfolio when yields are not clear from the beginning. Finally, we apply this algorithm to a specific case of a Colombian bank. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=outer%20approximation%20methods" title="outer approximation methods">outer approximation methods</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20problem" title=" portfolio problem"> portfolio problem</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-infinite%20programming" title=" semi-infinite programming"> semi-infinite programming</a>, <a href="https://publications.waset.org/abstracts/search?q=numerial%20solution" title=" numerial solution"> numerial solution</a> </p> <a href="https://publications.waset.org/abstracts/29163/numerical-solution-of-portfolio-selecting-semi-infinite-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29163.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27955</span> Optimization of a High-Growth Investment Portfolio for the South African Market Using Predictive Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mia%20Fran%C3%A7oise">Mia Françoise</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This report aims to develop a strategy for assisting short-term investors to benefit from the current economic climate in South Africa by utilizing technical analysis techniques and predictive analytics. As part of this research, value investing and technical analysis principles will be combined to maximize returns for South African investors while optimizing volatility. As an emerging market, South Africa offers many opportunities for high growth in sectors where other developed countries cannot grow at the same rate. Investing in South African companies with significant growth potential can be extremely rewarding. Although the risk involved is more significant in countries with less developed markets and infrastructure, there is more room for growth in these countries. According to recent research, the offshore market is expected to outperform the local market over the long term; however, short-term investments in the local market will likely be more profitable, as the Johannesburg Stock Exchange is predicted to outperform the S&P500 over the short term. The instabilities in the economy contribute to increased market volatility, which can benefit investors if appropriately utilized. Price prediction and portfolio optimization comprise the two primary components of this methodology. As part of this process, statistics and other predictive modeling techniques will be used to predict the future performance of stocks listed on the Johannesburg Stock Exchange. Following predictive data analysis, Modern Portfolio Theory, based on Markowitz's Mean-Variance Theorem, will be applied to optimize the allocation of assets within an investment portfolio. By combining different assets within an investment portfolio, this optimization method produces a portfolio with an optimal ratio of expected risk to expected return. This methodology aims to provide a short-term investment with a stock portfolio that offers the best risk-to-return profile for stocks listed on the JSE by combining price prediction and portfolio optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20stocks" title="financial stocks">financial stocks</a>, <a href="https://publications.waset.org/abstracts/search?q=optimized%20asset%20allocation" title=" optimized asset allocation"> optimized asset allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20modelling" title=" prediction modelling"> prediction modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Africa" title=" South Africa"> South Africa</a> </p> <a href="https://publications.waset.org/abstracts/168328/optimization-of-a-high-growth-investment-portfolio-for-the-south-african-market-using-predictive-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168328.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27954</span> Analyzing the Effects of Adding Bitcoin to Portfolio </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shashwat%20Gangwal">Shashwat Gangwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper analyses the effect of adding Bitcoin, to the portfolio (stocks, bonds, Baltic index, MXEF, gold, real estate and crude oil) of an international investor by using daily data available from 2<sup>nd</sup> of July, 2010 to 2<sup>nd of</sup> August, 2016. We conclude that adding Bitcoin to portfolio, over the course of the considered period, always yielded a higher Sharpe ratio. This means that Bitcoin’s returns offset its high volatility. This paper, recognizing the fact that Bitcoin is a relatively new asset class, gives the readers a basic idea about the working of the virtual currency, the increasing number developments in the financial industry revolving around it, its unique features and the detailed look into its continuously growing acceptance across different fronts (Banks, Merchants and Countries) globally. We also construct optimal portfolios to reflect the highly lucrative and largely unexplored opportunities associated with investment in Bitcoin. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bitcoin" title="bitcoin">bitcoin</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20instruments" title=" financial instruments"> financial instruments</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20management" title=" portfolio management"> portfolio management</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20adjusted%20return" title=" risk adjusted return"> risk adjusted return</a> </p> <a href="https://publications.waset.org/abstracts/57763/analyzing-the-effects-of-adding-bitcoin-to-portfolio" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57763.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">231</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27953</span> Assessment of E-Portfolio on Teacher Reflections on English Language Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hsiaoping%20Wu">Hsiaoping Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the wide use of Internet, learners are exposed to the wider world. This exposure permits learners to discover new information and combine a variety of media in order to reach in-depth and broader understanding of their literacy and the world. Many paper-based teaching, learning and assessment modalities can be transferred to a digital platform. This study examines the use of e-portfolios for ESL (English as a second language) pre-service teacher. The data were collected by reviewing 100 E-portfolio from 2013 to 2015 in order to synthesize meaningful information about e-portfolios for ESL pre-service teachers. Participants were generalists, bilingual and ESL pre-service teachers. The studies were coded into two main categories: learning gains, including assessment, and technical skills. The findings showed that using e-portfolios enhanced and developed ESL pre-service teachers’ teaching and assessment skills. Also, the E-portfolio also developed the pre-service teachers’ technical stills to prepare a comprehensible portfolio to present who they are. Finally, the study and presentation suggested e-portfolios for ecological issues and educational purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=assessment" title="assessment">assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=e-portfolio" title=" e-portfolio"> e-portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-service%20teacher" title=" pre-service teacher"> pre-service teacher</a>, <a href="https://publications.waset.org/abstracts/search?q=reflection" title=" reflection"> reflection</a> </p> <a href="https://publications.waset.org/abstracts/45473/assessment-of-e-portfolio-on-teacher-reflections-on-english-language-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45473.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27952</span> Portfolio Optimization under a Hybrid Stochastic Volatility and Constant Elasticity of Variance Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jai%20Heui%20Kim">Jai Heui Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sotheara%20Veng"> Sotheara Veng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper studies the portfolio optimization problem for a pension fund under a hybrid model of stochastic volatility and constant elasticity of variance (CEV) using asymptotic analysis method. When the volatility component is fast mean-reverting, it is able to derive asymptotic approximations for the value function and the optimal strategy for general utility functions. Explicit solutions are given for the exponential and hyperbolic absolute risk aversion (HARA) utility functions. The study also shows that using the leading order optimal strategy results in the value function, not only up to the leading order, but also up to first order correction term. A practical strategy that does not depend on the unobservable volatility level is suggested. The result is an extension of the Merton's solution when stochastic volatility and elasticity of variance are considered simultaneously. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20analysis" title="asymptotic analysis">asymptotic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=constant%20elasticity%20of%20variance" title=" constant elasticity of variance"> constant elasticity of variance</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20optimal%20control" title=" stochastic optimal control"> stochastic optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility" title=" stochastic volatility"> stochastic volatility</a> </p> <a href="https://publications.waset.org/abstracts/50103/portfolio-optimization-under-a-hybrid-stochastic-volatility-and-constant-elasticity-of-variance-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50103.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27951</span> Markowitz and Implementation of a Multi-Objective Evolutionary Technique Applied to the Colombia Stock Exchange (2009-2015)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Feijoo%20E.%20Colomine%20Duran">Feijoo E. Colomine Duran</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20E.%20Pe%C3%B1aloza%20Corredor"> Carlos E. Peñaloza Corredor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There modeling component selection financial investment (Portfolio) a variety of problems that can be addressed with optimization techniques under evolutionary schemes. For his feature, the problem of selection of investment components of a dichotomous relationship between two elements that are opposed: The Portfolio Performance and Risk presented by choosing it. This relationship was modeled by Markowitz through a media problem (Performance) - variance (risk), ie must Maximize Performance and Minimize Risk. This research included the study and implementation of multi-objective evolutionary techniques to solve these problems, taking as experimental framework financial market equities Colombia Stock Exchange between 2009-2015. Comparisons three multiobjective evolutionary algorithms, namely the Nondominated Sorting Genetic Algorithm II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Indicator-Based Selection in Multiobjective Search (IBEA) were performed using two measures well known performance: The Hypervolume indicator and R_2 indicator, also it became a nonparametric statistical analysis and the Wilcoxon rank-sum test. The comparative analysis also includes an evaluation of the financial efficiency of the investment portfolio chosen by the implementation of various algorithms through the Sharpe ratio. It is shown that the portfolio provided by the implementation of the algorithms mentioned above is very well located between the different stock indices provided by the Colombia Stock Exchange. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finance" title="finance">finance</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio" title=" portfolio"> portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=Markowitz" title=" Markowitz"> Markowitz</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a> </p> <a href="https://publications.waset.org/abstracts/56680/markowitz-and-implementation-of-a-multi-objective-evolutionary-technique-applied-to-the-colombia-stock-exchange-2009-2015" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56680.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27950</span> Optimization of Smart Beta Allocation by Momentum Exposure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20B.%20Frisch">J. B. Frisch</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Evandiloff"> D. Evandiloff</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Martin"> P. Martin</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Ouizille"> N. Ouizille</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Pires"> F. Pires </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Smart Beta strategies intend to be an asset management revolution with reference to classical cap-weighted indices. Indeed, these strategies allow a better control on portfolios risk factors and an optimized asset allocation by taking into account specific risks or wishes to generate alpha by outperforming indices called 'Beta'. Among many strategies independently used, this paper focuses on four of them: Minimum Variance Portfolio, Equal Risk Contribution Portfolio, Maximum Diversification Portfolio, and Equal-Weighted Portfolio. Their efficiency has been proven under constraints like momentum or market phenomenon, suggesting a reconsideration of cap-weighting.  To further increase strategy return efficiency, it is proposed here to compare their strengths and weaknesses inside time intervals corresponding to specific identifiable market phases, in order to define adapted strategies depending on pre-specified situations.  Results are presented as performance curves from different combinations compared to a benchmark. If a combination outperforms the applicable benchmark in well-defined actual market conditions, it will be preferred. It is mainly shown that such investment 'rules', based on both historical data and evolution of Smart Beta strategies, and implemented according to available specific market data, are providing very interesting optimal results with higher return performance and lower risk.  Such combinations have not been fully exploited yet and justify present approach aimed at identifying relevant elements characterizing them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=smart%20beta" title="smart beta">smart beta</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20variance%20portfolio" title=" minimum variance portfolio"> minimum variance portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=equal%20risk%20contribution%20portfolio" title=" equal risk contribution portfolio"> equal risk contribution portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20diversification%20portfolio" title=" maximum diversification portfolio"> maximum diversification portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=equal%20weighted%20portfolio" title=" equal weighted portfolio"> equal weighted portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=combinations" title=" combinations"> combinations</a> </p> <a href="https://publications.waset.org/abstracts/9011/optimization-of-smart-beta-allocation-by-momentum-exposure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9011.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">340</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27949</span> Comparison Study of Capital Protection Risk Management Strategies: Constant Proportion Portfolio Insurance versus Volatility Target Based Investment Strategy with a Guarantee</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olga%20Biedova">Olga Biedova</a>, <a href="https://publications.waset.org/abstracts/search?q=Victoria%20Steblovskaya"> Victoria Steblovskaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20Wallbaum"> Kai Wallbaum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the current capital market environment, investors constantly face the challenge of finding a successful and stable investment mechanism. Highly volatile equity markets and extremely low bond returns bring about the demand for sophisticated yet reliable risk management strategies. Investors are looking for risk management solutions to efficiently protect their investments. This study compares a classic Constant Proportion Portfolio Insurance (CPPI) strategy to a Volatility Target portfolio insurance (VTPI). VTPI is an extension of the well-known Option Based Portfolio Insurance (OBPI) to the case where an embedded option is linked not to a pure risky asset such as e.g., S&P 500, but to a Volatility Target (VolTarget) portfolio. VolTarget strategy is a recently emerged rule-based dynamic asset allocation mechanism where the portfolio’s volatility is kept under control. As a result, a typical VTPI strategy allows higher participation rates in the market due to reduced embedded option prices. In addition, controlled volatility levels eliminate the volatility spread in option pricing, one of the frequently cited reasons for OBPI strategy fall behind CPPI. The strategies are compared within the framework of the stochastic dominance theory based on numerical simulations, rather than on the restrictive assumption of the Black-Scholes type dynamics of the underlying asset. An extended comparative quantitative analysis of performances of the above investment strategies in various market scenarios and within a range of input parameter values is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CPPI" title="CPPI">CPPI</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20insurance" title=" portfolio insurance"> portfolio insurance</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20dominance" title=" stochastic dominance"> stochastic dominance</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility%20target" title=" volatility target"> volatility target</a> </p> <a href="https://publications.waset.org/abstracts/83288/comparison-study-of-capital-protection-risk-management-strategies-constant-proportion-portfolio-insurance-versus-volatility-target-based-investment-strategy-with-a-guarantee" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83288.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=portfolio%20analysis&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=portfolio%20analysis&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=portfolio%20analysis&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=portfolio%20analysis&page=5">5</a></li> 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