Relevant bibliographies by topics / Portfolio optimisation

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

Academic literature on the topic 'Portfolio optimisation'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Portfolio optimisation"

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Bulani, Vivek, Marija Bezbradica, and Martin Crane. "Improving Portfolio Management Using Clustering and Particle Swarm Optimisation." Mathematics 13, no. 10 (2025): 1623. https://doi.org/10.3390/math13101623.

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Portfolio management, a critical application of financial market analysis, involves optimising asset allocation to maximise returns while minimising risk. This paper addresses the notable research gap in analysing historical financial data for portfolio optimisation purposes. Particularly, this research examines different approaches for handling missing values and volatility, while examining their effects on optimal portfolios. For this portfolio optimisation task, this study employs a metaheuristic approach through the Swarm Intelligence algorithm, particularly Particle Swarm Optimisation and
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Shabe, Refiloe, Andries Engelbrecht, and Kian Anderson. "Incremental Reinforcement Learning for Portfolio Optimisation." Computers 14, no. 7 (2025): 242. https://doi.org/10.3390/computers14070242.

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Portfolio optimisation is a crucial decision-making task. Traditionally static, this problem is more realistically addressed as dynamic, reflecting frequent trading within financial markets. The dynamic nature of the portfolio optimisation problem makes it susceptible to rapid market changes or financial contagions, which may cause drifts in historical data. While reinforcement learning (RL) offers a framework that allows for the formulation of portfolio optimisation as a dynamic problem, existing RL approaches lack the ability to adapt to rapid market changes, such as pandemics, and fail to c
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Vasant, Jiten, Laurent Irgolic, Ryan Kruger, and Kanshukan Rajaratnam. "A Comparison Of Mean-Variance And Mean-Semivariance Optimisation On The JSE." Journal of Applied Business Research (JABR) 30, no. 6 (2014): 1587. http://dx.doi.org/10.19030/jabr.v30i6.8876.

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<p>This study investigates the effectiveness of semivariance versus mean-variance optimisation on a risk-adjusted basis on the JSE. We compare semivariance and mean-variance optimisation prior to, during and after the recent financial crisis period. Additionally, we investigate the inclusion of a fixed-income asset in the optimal portfolio. The results suggest that semivariance optimisation on the JSE in a pure equity case produces lower absolute returns, yet superior risk-adjusted returns. Further investigation suggests that semivariance metrics are effective within a certain range of p
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Yin, X. A., Z. F. Yang, and C. L. Liu. "Portfolio optimisation for hydropower producers that balances riverine ecosystem protection and producer needs." Hydrology and Earth System Sciences 18, no. 4 (2014): 1359–68. http://dx.doi.org/10.5194/hess-18-1359-2014.

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Abstract. In deregulated electricity markets, hydropower portfolio design has become an essential task for producers. The previous research on hydropower portfolio optimisation focused mainly on the maximisation of profits but did not take into account riverine ecosystem protection. Although profit maximisation is the major objective for producers in deregulated markets, protection of riverine ecosystems must be incorporated into the process of hydropower portfolio optimisation, especially against a background of increasing attention to environmental protection and stronger opposition to hydro
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Yin, X. A., Z. F. Yang, and C. L. Liu. "Portfolio optimisation for hydropower producers that balances riverine ecosystem protection and producer needs." Hydrology and Earth System Sciences Discussions 10, no. 12 (2013): 15841–69. http://dx.doi.org/10.5194/hessd-10-15841-2013.

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Abstract. In deregulated electricity markets, hydropower portfolio design has become an essential task for producers. The previous research on hydropower portfolio optimisation focused mainly on the maximisation of profits but did not take into account riverine ecosystem protection. Although profit maximisation is the major objective for producers in deregulated markets, protection of riverine ecosystems must be incorporated into the process of hydropower portfolio optimisation, especially against a background of increasing attention to environmental protection and stronger opposition to hydro
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Rutkauskas, Aleksandras Vytautas, and Grigorij Žilinskij. "Investment Portfolio Optimisation Model Based on Stocks Investment Attractiveness." Business: Theory and Practice 13, no. (3) (2012): 242–52. https://doi.org/10.3846/btp.2012.26.

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Firm's performance and potential return on investments in its stocks are determined by many factors. However, most of portfolio optimisation methods are oriented to decision- making based on stock price changes in the past. Recent financial crisis has showed that often the biggest downfall in the period of crisis is experienced by stocks, which had the biggest growth before crisis. So decision- making based on stock price tendencies analysis by ignoring fundamental factors can be inefficient. The variety of MCDM methods was briefly described and their application possibilities for portfolio op
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T, Vorkut, Bilonoh O, Petunin A, Tretynychenko Y, Kharuta V, and Chechet A. "PROJECT PORTFOLIOS OPTIMISATION OF COLLECTIVE STRATEGIES IMPLEMENTATION IN SUPPLY CHAIN NETWORKS." National Transport University Bulletin 1, no. 48 (2021): 44–62. http://dx.doi.org/10.33744/2308-6645-2021-1-48-044-062.

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The actuality of the theme arises from the need to improve and further develop the methodological support of portfolio management processes in the context of the portfolio management introduction of collective strategies implementation in supply chain networks. The purpose of the study is to develop a mathematical model for determining the optimal portfolio components as a means of single system implementation of collective strategies in supply chain networks (SCNs) to meet the target value of selected criteria of efficiency and cost-effectiveness of supply chains as integral objects, taking i
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T, Vorkut, Bilonoh O, Petunin A, Tretynychenko Y, Kharuta V, and Chechet A. "PROJECT PORTFOLIOS OPTIMISATION OF COLLECTIVE STRATEGIES IMPLEMENTATION IN SUPPLY CHAIN NETWORKS." National Transport University Bulletin 1, no. 48 (2021): 44–62. http://dx.doi.org/10.33744/2308-6645-2021-1-48-044-062.

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The actuality of the theme arises from the need to improve and further develop the methodological support of portfolio management processes in the context of the portfolio management introduction of collective strategies implementation in supply chain networks. The purpose of the study is to develop a mathematical model for determining the optimal portfolio components as a means of single system implementation of collective strategies in supply chain networks (SCNs) to meet the target value of selected criteria of efficiency and cost-effectiveness of supply chains as integral objects, taking i
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Holovatiuk, Olha. "Cryptocurrencies as an asset class in portfolio optimisation." Central European Economic Journal 7, no. 54 (2020): 33–55. http://dx.doi.org/10.2478/ceej-2020-0004.

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AbstractIn this paper, cryptocurrencies are analysed as investment instruments. The study aims to verify whether they can be classified as an asset class and what kind of benefits they may bring to the investor's portfolio. We used 6 indices as proxies for the major asset classes, including the cryptocurrency index CRIX, for all cryptographic assets.Cryptocurrencies relatively fully satisfied 7 asset class requirements, namely stable aggregation, investability, internal homogeneity, external heterogeneity, expected utility, selection skill and cost-effective access. It was found that crypto as
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Slate, N., E. Matwiejew, S. Marsh, and J. B. Wang. "Quantum walk-based portfolio optimisation." Quantum 5 (July 28, 2021): 513. http://dx.doi.org/10.22331/q-2021-07-28-513.

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This paper proposes a highly efficient quantum algorithm for portfolio optimisation targeted at near-term noisy intermediate-scale quantum computers. Recent work by Hodson et al. (2019) explored potential application of hybrid quantum-classical algorithms to the problem of financial portfolio rebalancing. In particular, they deal with the portfolio optimisation problem using the Quantum Approximate Optimisation Algorithm and the Quantum Alternating Operator Ansatz. In this paper, we demonstrate substantially better performance using a newly developed Quantum Walk Optimisation Algorithm in find
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Dissertations / Theses on the topic "Portfolio optimisation"

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Arbex, Valle Cristiano. "Portfolio optimisation models." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/10343.

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In this thesis we consider three different problems in the domain of portfolio optimisation. The first problem we consider is that of selecting an Absolute Return Portfolio (ARP). ARPs are usually seen as financial portfolios that aim to produce a good return regardless of how the underlying market performs, but our literature review shows that there is little agreement on what constitutes an ARP. We present a clear definition via a three-stage mixed-integer zero-one program for the problem of selecting an ARP. The second problem considered is that of designing a Market Neutral Portfolio (MNP)
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Hagströmer, Björn. "Liquidity and portfolio optimisation." Thesis, Aston University, 2009. http://publications.aston.ac.uk/15679/.

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This thesis presents research within empirical financial economics with focus on liquidity and portfolio optimisation in the stock market. The discussion on liquidity is focused on measurement issues, including TAQ data processing and measurement of systematic liquidity factors (FSO). Furthermore, a framework for treatment of the two topics in combination is provided. The liquidity part of the thesis gives a conceptual background to liquidity and discusses several different approaches to liquidity measurement. It contributes to liquidity measurement by providing detailed guidelines on the data
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Woodside-Oriakhi, Maria. "Portfolio optimisation with transaction cost." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5839.

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Portfolio selection is an example of decision making under conditions of uncertainty. In the face of an unknown future, fund managers make complex financial choices based on the investors perceptions and preferences towards risk and return. Since the seminal work of Markowitz, many studies have been published using his mean-variance (MV) model as a basis. These mathematical models of investor attitudes and asset return dynamics aid in the portfolio selection process. In this thesis we extend the MV model to include the cardinality constraints which limit the number of assets held in the portfo
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Pillay, Divanisha. "Robustness of bond portfolio optimisation." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/20783.

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Korn and Koziol (2006) apply the Markowitz (1952) mean-variance framework to bond portfolio selection by proposing the use of term structure models to estimate the time-varying moments of bond returns. Duffee (2002) introduces a distinction between completely affine and essentially affine term structure models. A completely affine model uses a market price of risk specification that is proportional to the volatility of the risk factors. However, this assumption of proportionality of the market price of risk contradicts the observed behaviour of bond returns. In response, Duffee (2002) introduc
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Landman, Jayson. "Flexible risk-based portfolio optimisation." Master's thesis, Faculty of Commerce, 2021. http://hdl.handle.net/11427/32787.

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The purpose of this study is to present and test a general framework for risk-based investing. It permits various risk-based portfolios such as the global minimum variance, equal risk contribution and equal weight portfolios. The framework also allows for different estimation techniques to be used in finding the portfolios. The design of the study is to collate the existing research on risk-based investing, to analyse some modern methods to reduce estimation risk, to incorporate them in a single coherent framework, and to test the result with South African equity data. The techniques to reduce
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Wang, Jianshen. "Portfolio optimisation and dynamic trading." Thesis, University of Bristol, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702879.

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Chatsanga, Nonthachote. "International portfolio optimisation under uncertainty." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/42729/.

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Portfolio optimisation problems are generally concerned with allocating funds to investments. The goal is to find an allocation that minimises risk subject to some certain constraints. To attain robust solutions from the optimisation, it is vital to ensure that the model is able to properly represent the underlying uncertainty in portfolio management. The main source of uncertainty in managing portfolios is from asset returns fluctuation. Typically, it is depicted through scenarios or return distributions which are commonly assumed to be normal. Such assumption, however, does not illustrate th
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Mårtensson, Jonathan. "Portfolio optimisation : improved risk-adjusted return?" Thesis, Uppsala University, Department of Economics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6397.

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<p>In this thesis, portfolio optimisation is used to evaluate if a specific sample of portfolios have</p><p>a higher risk level or lower expected return, compared to what may be obtained through</p><p>optimisation. It also compares the return of optimised portfolios with the return of the original</p><p>portfolios. The risk analysis software Aegis Portfolio Manager developed by Barra is used for</p><p>the optimisations. With the expected return and risk level used in this thesis, all portfolios can</p><p>obtain a higher expected return and a lower risk. Over a six-month period, the optimised</
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Zuo, Fei. "Passive and active currency portfolio optimisation." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/22612.

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This thesis examines the performance of currency-only portfolios with different strategies, in out-of-sample analysis. I first examine a number of passive portfolio strategies into currency market in out-of-sample analysis. The strategies I applied in this chapter include sample-based mean-variance portfolio and its extension, minimum variance portfolio, and equally-weighted risk contribution model. Moreover, I consider GDP portfolio and Trade portfolio as market value portfolio for currency market. With naïve portfolio, there are 12 different asset allocation models. In my out-of-sample analy
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Jin, Yan. "Advanced computational methods in portfolio optimisation." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/39023/.

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Portfolio optimisation is the process of making optimal investment decisions, where a set of assets are selected and invested with certain amount of the capital in the portfolio. Since the milestone work, Markowitz’s Mean-Variance (MV) model, it has boosted the research for new portfolio optimisation models and applications for last 60 years. Despite its theoretical values, the MV model has been widely criticised for underlying simplistic assumptions which ignore real world conditions and fail to take the market uncertainty of the mean and variance into account. To correct these, a large numbe
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Books on the topic "Portfolio optimisation"

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

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Rasmussen, Mikkel. Quantitative Portfolio Optimisation, Asset Allocation and Risk Management. Palgrave Macmillan UK, 2003. http://dx.doi.org/10.1057/9780230512856.

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Vollmer, Markus. A Beta-return Efficient Portfolio Optimisation Following the CAPM. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-06634-5.

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Tomasini, Emilio. Trading systems: A new approach to system development and portfolio optimisation. Harriman House, 2009.

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Shadbolt, Jimmy. Neural Networks and the Financial Markets: Predicting, Combining and Portfolio Optimisation. Springer London, 2002.

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Agarwal, Saurabh. Portfolio Selection Using Multi-Objective Optimisation. Palgrave Macmillan, 2018.

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Rasmussen, Mikkel. Quantitative Portfolio Optimisation, Asset Allocation and Risk Management. Palgrave Macmillan, 2002.

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Credit derivatives: Applications for risk management, investment and portfolio optimisation. Risk Books, 1998.

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Best, Michael J. Portfolio Optimization. Taylor & Francis Group, 2010.

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Portfolio optimization. Chapman & Hall/CRC, 2010.

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Book chapters on the topic "Portfolio optimisation"

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Adcock, C. J. "Portfolio Optimisation." In Perspectives in Neural Computing. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0151-2_25.

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Brianton, Geoffrey. "Portfolio Optimisation." In Risk Management and Financial Derivatives. Palgrave Macmillan UK, 1997. http://dx.doi.org/10.1007/978-1-349-14605-5_12.

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Flener, Pierre, Justin Pearson, and Luis G. Reyna. "Financial Portfolio Optimisation." In Principles and Practice of Constraint Programming – CP 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30201-8_19.

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Fairley, Michael, and Jeremy Goldhaber-Fiebert. "EVSI Portfolio Optimisation." In Value of Information for Healthcare Decision-Making. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003156109-10.

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Rasmussen, Mikkel. "Quantitative Portfolio Optimisation and Efficient Portfolios." In Quantitative Portfolio Optimisation, Asset Allocation and Risk Management. Palgrave Macmillan UK, 2003. http://dx.doi.org/10.1057/9780230512856_6.

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Fieldsend, Jonathan E., John Matatko, and Ming Peng. "Cardinality Constrained Portfolio Optimisation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_117.

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Rasmussen, Mikkel. "Portfolio Characterisation." In Quantitative Portfolio Optimisation, Asset Allocation and Risk Management. Palgrave Macmillan UK, 2003. http://dx.doi.org/10.1057/9780230512856_5.

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Agarwal, Saurabh. "Recent Advances in Portfolio Optimisation." In Portfolio Selection Using Multi-Objective Optimisation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54416-8_3.

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Wagner, Niklas F. "Portfolio Optimisation with Cap Weight Restrictions." In Decision Technologies for Computational Finance. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5625-1_32.

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Gilli, Manfred, and Enrico Schumann. "Large-Scale Portfolio Optimisation with Heuristics." In Advanced Statistical Methods for the Analysis of Large Data-Sets. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21037-2_17.

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Conference papers on the topic "Portfolio optimisation"

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Li, Zhenglong, and Vincent Tam. "Developing An Attention-Based Ensemble Learning Framework for Financial Portfolio Optimisation." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650941.

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Li, Zhenglong, Vincent Tam, and Kwan L. Yeung. "A Multimodal and Sentiment-Based Trading System for Financial Portfolio Optimisation." In 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10930124.

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Garg, Aayushi, Aditya Pratap Singh, Anushka Saraswat, Avnish Kumar, and Anuradha Taluja. "Optimisation Techniques in Investment Risk Management :Scipy, SLSQP and Modern Portfolio Theory." In 2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC). IEEE, 2025. https://doi.org/10.1109/isacc65211.2025.10969208.

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Ilade, Tudor-Florentin, and Eugen Croitoru. "A Comparative Analysis of Genetic Algorithms and NSGA-II on the Portfolio Optimisation Problem." In 2024 26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2024. https://doi.org/10.1109/synasc65383.2024.00041.

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Liu, Yang, and Lijun Yu. "Deep Learning with Gated Recurrent Unit Recurrent Neural Networks and Multi-Objective Optimisation for Portfolio Management." In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2024. https://doi.org/10.1109/prai62207.2024.10826634.

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Maumela, Tshifhiwa, Fulufhelo Nelwamondo, and Tshilidzi Marwala. "Portfolio Optimisation Using Ulimisana Optimisation Algorithm." In 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2022. http://dx.doi.org/10.1109/codit55151.2022.9803923.

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Patel, S., and C. D. Clack. "ALPS evaluation in financial portfolio optimisation." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424554.

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Busetti, F. "Heuristic approaches to realistic portfolio optimisation." In COMPUTATIONAL FINANCE 2006. WIT Press, 2006. http://dx.doi.org/10.2495/cf060351.

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"Optimisation of Real Estate Investment Portfolio." In 6th European Real Estate Society Conference: ERES Conference 1999. ERES, 1999. http://dx.doi.org/10.15396/eres1999_145.

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Sethia, Akhil Mukesh. "Application of Swarm Intelligence to Portfolio Optimisation." In 2018 International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2018. http://dx.doi.org/10.1109/gucon.2018.8675083.

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Reports on the topic "Portfolio optimisation"

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Kaufmann, Joachim, Peter Kaufmann, and Simone Maria Grabner. Assessment of completed BRIDGE Discovery projects Synthesis at programme level. BMK - Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, 2023. http://dx.doi.org/10.22163/fteval.2023.640.

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The aim of this short evaluation was to systematically collect information from the completed projects of the BRIDGE Discovery programme as of June 2023. This will be used for strategic optimisation and decision making for the funding period 2025-2028. BRIDGE Discovery is an open-topic funding programme at the interface between basic and applied research, which is jointly funded and implemented by the Swiss National Science Foundation (SNSF) and Innosuisse - Swiss Agency for Innovation Promotion. The study used a mixed-methods approach to gather information about the programme context and the
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