Relevant bibliographies by topics / Portfolio selection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Portfolio selection'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2024

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Journal articles on the topic "Portfolio selection"

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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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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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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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Adcock, C. J. "An Empirical Study of Portfolio Selection for Optimally Hedged Portfolios." Multinational Finance Journal 7, no. 1/2 (June 1, 2003): 83–106. http://dx.doi.org/10.17578/7-1/2-4.

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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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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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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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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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LEVINA, TATSIANA, and GLENN SHAFER. "PORTFOLIO SELECTION AND ONLINE LEARNING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 04 (August 2008): 437–73. http://dx.doi.org/10.1142/s0218488508005364.

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This paper studies a new strategy for selecting portfolios in the stock market. The strategy is inspired by two streams of previous work: (1) work on universalization of strategies for portfolio selection, which began with Thomas Cover's work on constant rebalanced portfolios, published in 1991,4 and (2) more general work on universalization of online algorithms,17,21,23,30 especially Vladimir Vovk's work on the aggregating algorithm and Markov switching strategies.32 The proposed investment strategy achieves asymptotically the same exponential rate of growth as the portfolio that turns out to be best expost in the long run and does not require any underlying statistical assumptions on the nature of the stock market.
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Wise, A. J. "Matching and portfolio selection: Part 1." Journal of the Institute of Actuaries 114, no. 1 (June 1987): 113–33. http://dx.doi.org/10.1017/s0020268100019028.

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1.1 My paper entitled ‘The Matching of Assets to Liabilities’ described a new study of the subject of matching. The paper outlined the mathematical framework, showed some calculations, and suggested various applications in the field of actuarial valuation.1.2 Subsequently A. D. Wilkie published a note entitled ‘Portfolio Selection in the Presence of Fixed Liabilities: a Comment on “The Matching of Assets to Liabilities”’. Wilkie's note appears to represent a new extension of portfolio selection theory which enables him to encompass the conventional portfolio theory and to show where my approach to matching fits into the picture.1.3 This paper is in the nature of a reply to that of Wilkie. My objective at the stage of the first draft was to re-examine the matching portfolio, as defined in relation to specified liabilities, in view of the wide range of alternative portfolios which Wilkie has described. The scope of this work progressed well beyond my original plans when I found a most interesting mathematical relationship between the ‘matching portfolios’ described in my earlier paper and the ‘efficient portfolios’ described in Wilkie's.
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Dissertations / Theses on the topic "Portfolio selection"

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Souza, Thiago de Oliveira. "Essays on portfolio selection." Thesis, Queen Mary, University of London, 2012. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8682.

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This thesis began with an introduction and literature review in Chapter 1. In Chapter 2, I propose a new intertemporal asset-pricing model based on heterogeneous beliefs to bring together the concurrent theories that could generate value and momentum effects. In this model, I assume that such behaviour occurs simply due to an agnostic view of forecasting returns considering the dominant strategy in the market. Given the endogenous price determination in the model, individuals were expected to adjust their own strategies to match the dominant strategy to obtain higher profits (from more accurate fore- casts). The idea was to bridge the literature on intertemporal asset allocation with the one on heterogeneous beliefs. In Chapters 3 and 4, I consider the empirical problem of implementing Markowitz (1952) mean-variance optimisation on a portfolio of stocks. In particular, I focus on the out-of-sample performance of the minimum-variance portfolio obtained from the use of asset group information and regularisation methods to obtain more stable estimates of the parameters in the model. Specifically, in Chapter 3, I introduce the use of regularisation methods to the portfolio selection problem and a literature review on the subject. In Chapter 4, I propose two alternative approaches for the use of the group structure information and to obtain more stable and regularised minimum-variance portfolios. I show that these procedures produce significantly better results in the portfolios compared with the unconstrained minimum-variance portfolios estimated from the whole data set in terms of portfolio variance and the Sharpe ratio.
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Memmel, Christoph. "Schätzrisiken in der Portfoliotheorie : Auswirkungen und Möglichkeiten der Reduktion /." Lohmar ; Köln : Eul, 2004. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=012869843&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA.

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Brinkmann, Ulf. "Robuste Asset-Allocation /." Bad Soden/Ts. : Uhlenbruch, 2007. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016280816&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Schmieder, Christian. "Multi-period credit portfolio selection /." Marburg : Tectum-Verl, 2006. http://deposit.ddb.de/cgi-bin/dokserv?id=2771399&prov=M&dok_var=1&dok_ext=htm.

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Schmieder, Christian. "Multi-period credit portfolio selection." Marburg Tectum-Verl, 2005. http://deposit.ddb.de/cgi-bin/dokserv?id=2771399&prov=M&dok_var=1&dok_ext=htm.

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Norman, Andrew R. "Portfolio selection with transaction costs." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/11848.

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Ferreira, Pedro Miguel Barreirão. "Diversification and portfolio selection methods." Master's thesis, Instituto Superior de Economia e Gestão, 2010. http://hdl.handle.net/10400.5/2227.

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Mestrado em Finanças
This paper studies several portfolio selection methods in order to achieve higher returns and lower risk than the market. The main objective of this paper is to conclude if it is possible to achieve higher returns and lower risk than the market using only daily close stocks price data. It is important however, to know how the number of assets affects the risk of portfolio (benefits of diversification). Therefore, in the early stage, the impact of the introduction of stocks in the portfolio in terms of risk will be analyzed in order to choose a minimum number of stocks to maximize the benefits of diversification. Several techniques of portfolio selection (optimal portfolio, minimum variance and equal weights) are tested in order to achieve higher returns and lower risk levels than the sectors indexes. The benefits of diversification can be achieved with few stocks. This is the first conclusion of this paper that allows a reduction of the cost of transactions in the techniques used. Some of the portfolio selection methods in this paper achieved quite good results, revealed better performance than the index markets over the ten year period. However the best technique isn't equal to all sectors, there are slight differences between the best techniques among sectors.
Este trabalho estuda diversos métodos de selecção de carteiras de forma a obter maiores retornos e menor risco que o mercado. O principal objectivo é obter maiores rendibilidades e menores níveis de risco que o mercado usando apenas os preços das acções. Contudo, é importante saber como o número de activos afecta o risco de uma carteira (benefícios da diversificação). Portanto, numa primeira fase, será analisado o impacto da introdução de activos numa carteira em termos de risco, para escolher um número mínimo de acções para constituir uma carteira maximizando o benefício da diversificação. Diversas técnicas de selecção de carteiras (carteira óptima, variância mínima e pesos iguais) são testadas de forma a obter maiores retornos e menores nível de risco que o índice sectorial. Os benefícios da diversificação podem ser atingidos com poucas acções. Esta foi a primeira conclusão, que permitiu a redução dos custos de transacção nas técnicas utilizadas. Alguns métodos de selecção de carteiras estudados obtiveram bons resultados, revelando melhor performance que o índice de mercado ao longo dos dez anos. Contudo, a melhor técnica não é igual para todos os sectores, existem ligeiras diferenças entre as melhores técnicas entre os sectores.
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Ashwood, Andrew J. "Portfolio selection using artificial intelligence." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/66229/1/Andrew_Ashwood_Thesis.pdf.

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The application of artificial intelligence in finance is relatively new area of research. This project employed artificial neural networks (ANNs) that use both fundamental and technical inputs to predict future prices of widely held Australian stocks and use these predicted prices for stock portfolio selection over a long investment horizon. The research involved the creation and testing of a large number of possible network configurations and draws conclusions about ANN architectures and their overall suitability for the purpose of stock portfolio selection.
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Ciani, Gabriele <1993&gt. "Portfolio Selection with Swarm Intelligence." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/12769.

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The introduction of budget, cardinality and composition constraints to the portfolio selection problem implies the utilization of modern techniques for the achievement of the solution. In particular, this thesis will analyse Particle Swarm Optimization, a bio-inspired metaheuristic algorithm that aims to explore the search space in order to find optimal solutions. The problem considered consists in the minimization of a coherent risk measure, the expected shortfall, subject to risk adjusted performance constraints, budget, cardinality and fractions constraints. In practice, a chosen number of particles are exploring the set of feasible solutions. To the position of each particle is assigned a value of the objective function which accounts for the risk measure and for penalties associated to the constraints. Particles move according to signals given by their neighbors, by the particle with the best result and by their own memory. The implementation of the PSO algorithm is used to find a feasible and well diversified portfolio composed by Exchange Traded Funds sold on the Italian market, Borsa Italiana.
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Puhle, Michael. "Bond portfolio optimization." Berlin Heidelberg Springer, 2007. http://d-nb.info/985928115/04.

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Books on the topic "Portfolio selection"

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Wang, Shouyang, and Yusen Xia. Portfolio Selection and Asset Pricing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-55934-1.

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Hausmann, Wilfried, Kathrin Diener, and Joachim Käsler. Derivate, Arbitrage und Portfolio-Selection. Wiesbaden: Vieweg+Teubner Verlag, 2002. http://dx.doi.org/10.1007/978-3-322-80223-1.

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Aït-Sahalia, Yacine. Variable selection for portfolio choice. Cambridge, MA: National Bureau of Economic Research, 2001.

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Kwiatkowski, J. W. An algorithm for portfolio selection. Edinburgh: University of Edinburgh. Department of Business Studies, 1986.

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Yusen, Xia, ed. Portfolio selection and asset pricing. Berlin: Springer, 2002.

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Mawby, William D. Project portfolio selection for Six Sigma. Milwaukee, Wis: ASQ Quality Press, 2007.

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Agarwal, Saurabh. Portfolio Selection Using Multi-Objective Optimisation. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54416-8.

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Markowitz, H. Portfolio selection: Efficient diversification of investments. 2nd ed. Cambridge, Mass: B. Blackwell, 1991.

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Ramaswamy, Srichander. Portfolio selection using fuzzy decision theory. Basle, Switzerland: Bank for International Settlements, Monetary and Economic Dept., 1998.

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Boyle, Phelim P. Optimal portfolio selection with transaction costs. Toronto: University of Toronto, Dept. of Statistics, 1994.

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Book chapters on the topic "Portfolio selection"

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Hunanyan, Gevorg. "Portfolio Selection." In Finanzwirtschaft, Banken und Bankmanagement I Finance, Banks and Bank Management, 9–32. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-27956-1_2.

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Fabozzi, Frank J., Harry M. Markowitz, Petter N. Kolm, and Francis Gupta. "Portfolio Selection." In The Theory and Practice of Investment Management, 45–78. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118267028.ch3.

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Ruppert, David, and David S. Matteson. "Portfolio Selection." In Statistics and Data Analysis for Financial Engineering, 465–93. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2614-5_16.

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Huang, Xiaoxia. "Probabilistic Portfolio Selection." In Portfolio Analysis, 11–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11214-0_2.

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Huang, Xiaoxia. "Credibilistic Portfolio Selection." In Portfolio Analysis, 61–115. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11214-0_3.

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Huang, Xiaoxia. "Uncertain Portfolio Selection." In Portfolio Analysis, 117–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11214-0_4.

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Xidonas, Panos, George Mavrotas, Theodore Krintas, John Psarras, and Constantin Zopounidis. "Stock Selection." In Multicriteria Portfolio Management, 23–55. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3670-6_3.

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Harpum, Pete. "Strategic portfolio selection." In Strategic Portfolio Management, 176–205. London: Routledge, 2022. http://dx.doi.org/10.4324/9780367853129-14.

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Dochow, Robert. "Portfolio Selection Problems." In Online Algorithms for the Portfolio Selection Problem, 9–43. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13528-7_2.

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Kavadias, Stylianos, and Christoph H. Loch. "The Portfolio Selection Problem." In Project Selection Under Uncertainty, 1–12. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9080-8_1.

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Conference papers on the topic "Portfolio selection"

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Elselmy, Heba Salah, Ayman Ghoneim, and Ihab A. Elkhodary. "Portfolio Selection Factors." In ICSIE '19: 2019 8th International Conference on Software and Information Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3328833.3328858.

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Vovk, V., and C. Watkins. "Universal portfolio selection." In the eleventh annual conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/279943.279947.

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Dal Molim, Thales F., and Francisco Carlos M. Souza. "Multistage, Multiswarm Particle Swarm Optimization for Investment Portfolio Selection." In Brazilian Workshop on Artificial Intelligence in Finance. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/bwaif.2023.230652.

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This study proposes a new metaheuristic-based approach, identified in the literature as MS2PSO, to solve the problem of investment portfolio selection in the Brazilian stock market. The study was divided into two experiments that evaluated the quality of the solutions obtained by the algorithm and the effectiveness of the recommended portfolios in different time windows and risk profiles. The results indicated that MS2PSO presented slower convergence but offered more satisfactory results compared to PSO. In addition, the portfolios recommended by the proposed method showed positive and negative performances according to the risk profile, and all of them outperformed the benchmark in terms of the overall highest gain obtained during the period. This study contributes to the development of the area of finance and economics by providing a sophisticated and efficient solution for investment portfolio selection.
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Buzzetto-More, Nicole, and Ayodele Alade. "The Pentagonal E-Portfolio Model for Selecting Adopting Building and Implementing an E-Portfolio." In InSITE 2008: Informing Science + IT Education Conference. Informing Science Institute, 2008. http://dx.doi.org/10.28945/3240.

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Electronic portfolios are a student-centered outcomes-based assessment regime involving learners in the gathering, selection, and organization of artifacts synthesized into a compilation purposed to demonstrate knowledge, skills, and/or achievements supported by reflections that articulate the relevance, credibility, and meaning of the artifacts being presented. Electronic portfolios have been found to be a valid way to document student progress, encourage student involvement in assessment, showcase student work samples, promote students professionally, and provide a method of student learning outcomes and curriculum evaluation. However, electronic portfolio adoption represents a sizable commitment that is influenced by a number of variables and that requires foresight as well as a thoughtful strategy. This paper presents a model for selecting, designing, and implementing an electronic portfolio project and illustrates its application through the presentation of a detailed case study of a successfully implemented and ongoing electronic portfolio project used as a comprehensive assessment measure to determine degree mastery in the Department of Business, Management, and Accounting at the University of Maryland Eastern Shore. The model introduced in this paper is known as the Pentagonal E-Portfolio Model, named such for its five levels: 1) Level 1 - Identification of Needs; 2) Level 2 - Determination, Assessment, & Budgeting; 3) Level 3 - System Selection and Strategic Planning; 4) Level 4 - Development; and 5) Level 5 - Implementation and Continuation.
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Ordentlich, Erik, and Thomas M. Cover. "On-line portfolio selection." In the ninth annual conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/238061.238161.

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Zhang, Xiaoxiong, Yajie Dou, Qingsong Zhao, and Kai Zhao. "The tradeoffs between portfolios in multi-attribute project portfolio selection." In 2017 Annual IEEE International Systems Conference (SysCon). IEEE, 2017. http://dx.doi.org/10.1109/syscon.2017.7934756.

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Chang-Li Wu, Yan-Qing Wang, and Cai-Ying Huang. "Portfolio selection with credibility criterion." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212651.

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Hao, Fang-Fang, and Yan-Kui Liu. "Fuzzy Random Portfolio Selection Problem." In 2007 International Conference on Computational Intelligence and Security (CIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/cis.2007.101.

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Jin, Hanqing, and Xun Yu Zhou. "Continuous-time behavioral portfolio selection." In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4738833.

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Wu, Meng, Le Wang, Yang Wang, and Nan-jing Huang. "Portfolio Selection with Stock Funds." In 2011 International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII). IEEE, 2011. http://dx.doi.org/10.1109/iciii.2011.361.

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Reports on the topic "Portfolio selection"

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Yoo, Peter S. Age Dependent Portfolio Selection. Federal Reserve Bank of St. Louis, 1994. http://dx.doi.org/10.20955/wp.1994.003.

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Ait-Sahalia, Yacine, and Michael Brandt. Variable Selection for Portfolio Choice. Cambridge, MA: National Bureau of Economic Research, February 2001. http://dx.doi.org/10.3386/w8127.

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Brandt, Michael, and Pedro Santa-Clara. Dynamic Portfolio Selection by Augmenting the Asset Space. Cambridge, MA: National Bureau of Economic Research, March 2004. http://dx.doi.org/10.3386/w10372.

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Haliassos, Michael, and Andrew Lyon. Progressivity of Capital Gains Taxation with Optimal Portfolio Selection. Cambridge, MA: National Bureau of Economic Research, January 1993. http://dx.doi.org/10.3386/w4253.

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Gemmo, Irina, Pierre-Carl Michaud, and Olivia Mitchell. Selection into Financial Education and Effects on Portfolio Choice. Cambridge, MA: National Bureau of Economic Research, September 2023. http://dx.doi.org/10.3386/w31682.

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Gemmo, Irina, Pierre-Carl Michaud, and Olivia S. Mitchell. Selection into Financial Education and Effects on Portfolio Choice. CIRANO, September 2023. http://dx.doi.org/10.54932/zjnj5054.

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To examine how financial education affects financial outcomes, one must evaluate whether and how sample selection may bias inferences regarding program impacts. Our incentivized experiment reveals how such selection influences estimated financial education effects. The more financially literate and those expecting higher gains pay more to purchase education, while those who consider themselves very financially literate pay less. Using portfolio allocation tasks, we show that the financial education increases portfolio efficiency and welfare by almost 20 and 3 percentage points, respectively. In our setting, selection does not greatly influence estimated program effects, comparing those participating and those who do not.
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MacKinlay, A. Craig, and Lubos Pastor. Asset Pricing Models: Implications for Expected Returns and Portfolio Selection. Cambridge, MA: National Bureau of Economic Research, June 1999. http://dx.doi.org/10.3386/w7162.

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Gans, Joshua, and Fiona Murray. Funding Scientific Knowledge: Selection, Disclosure and the Public-Private Portfolio. Cambridge, MA: National Bureau of Economic Research, April 2011. http://dx.doi.org/10.3386/w16980.

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Carrasco, Marine, and N'golo Koné. Test for Trading Costs Effect in a Portfolio Selection Problem with Recursive Utility. CIRANO, January 2023. http://dx.doi.org/10.54932/bjce8546.

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This paper addresses a portfolio selection problem with trading costs on stock market. More precisely, we develop a simple GMM-based test procedure to test the significance of rading costs effect in the economy with a áexible form of transaction costs. We also propose a two-step procedure to test overidentifying restrictions in our GMM estimation. In an empirical analysis, we apply our test procedures to the class of anomalies used in Novy-Marx and Velikov (2016). We show that transaction costs have a significant effect on investors behavior for many anomalies. In that case, investors significantly improve the out-of-sample performance of their portfolios by accounting for trading costs.
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Gálvez, Julio. Household portfolio choices under (non-)linear income risk: an empirical framework. Madrid: Banco de España, September 2023. http://dx.doi.org/10.53479/33792.

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This paper develops a flexible, semi-structural framework to empirically quantify the non-linear transmission of income shocks to household portfolio choice decisions both at the extensive and intensive margins. I model stock market participation and portfolio allocation rules as age-dependent functions of persistent and transitory earnings components, wealth and unobserved taste shifters. I establish non-parametric identification and propose a tractable, simulation-based estimation algorithm, building on recent developments in the sample selection literature. Using recent waves of PSID data, I find heterogeneous income and wealth effects on both extensive and intensive margins, over the wealth and life-cycle dimensions. These results suggest that preferences are heterogeneous across the wealth distribution and over the life cycle. Moreover, in impulse response exercises, I find sizeable extensive margin responses to persistent income shocks. Finally, I find heterogeneity in participation costs across households in the wealth distribution.
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