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Journal articles on the topic 'Multi-Asset Portfolio'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Cloutier, Richard, and Alan C. Mikkelson. "The effect of absolute return strategies on risk-factor diversification and portfolio performance." Investment Management and Financial Innovations 20, no. 3 (2023): 91–101. http://dx.doi.org/10.21511/imfi.20(3).2023.08.

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Absolute return strategies attempt to generate positive returns that are uncorrelated with equity or bond markets and can be used to increase diversification and performance within multi-asset class portfolios. The current paper compared diversification and portfolio performance between traditional multi-asset class portfolios and multi-asset class portfolios with the addition of absolute return strategies. Using closing prices from January 1, 2000 – June 30, 2018, this paper back-tested two multi-asset class portfolios, one composed of equities, fixed income securities, and real return strate

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Gupta, Nupur, Pradip Mitra, and Bharath Supra. "Enhancing portfolio resilience during crisis periods: Lessons from BRICS indices and multi asset strategies." Investment Management and Financial Innovations 20, no. 4 (2023): 99–111. http://dx.doi.org/10.21511/imfi.20(4).2023.09.

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This paper uses Markowitz’s mean-variance model to construct an investment portfolio incorporating multiple assets – BRICS equity indices, Gold, crude oil, bonds, and cryptocurrencies. The optimally created risky portfolios outperform alternative portfolio optimization methods – the naive portfolio and the equal risk contribution portfolio; and established indices – the S&amp;amp;P 500 and the MSCI Emerging Equity Index in terms of metrics – adjusted Sharpe ratio, modified Sharpe ratio, and the modified Value at Risk. The findings are validated across different periods, including the COVID

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Berger, Theo, and Christian Fieberg. "On portfolio optimization." Journal of Risk Finance 17, no. 3 (2016): 295–309. http://dx.doi.org/10.1108/jrf-09-2015-0094.

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Purpose The purpose of this paper is to show how investors can incorporate the multi-scale nature of asset and factor returns into their portfolio decisions and to evaluate the out-of-sample performance of such strategies. Design/methodology/approach The authors decompose daily return series of common risk factors and of all stocks listed in the Dow Jones Industrial Index (DJI) from 2000 to 2015 into different time scales to separate short-term noise from long-run trends. Then, the authors apply various (multi-scale) factor models to determine variance-covariance matrices which are used for mi

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Acheampong, Prince, and Sydney Kwesi Swanzy. "Empirical Test of Single Factor and Multi-Factor Asset Pricing Models: Evidence from Non Financial Firms on the Ghana Stock Exchange (GSE)." International Journal of Economics and Finance 8, no. 1 (2015): 99. http://dx.doi.org/10.5539/ijef.v8n1p99.

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<p>This paper examines the explanatory power of a uni-factor asset pricing model (CAPM) against a multi-factor model (The Fama-French three factor model) in explaining excess portfolio returns on non-financial firms on the Ghana Stock Exchange (GSE). Data covering the period January 2002 to December 2011 were used. A six Size- Book-to-Market (BTM) ratio portfolios were formed and used for the analysis. The paper revealed that, a uni-factor model like the (CAPM) could not predict satisfactorily, the excess portfolio returns on the Ghana Stock Exchange. By using the multi-factor asset pric

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Nisani, Doron. "Portfolio selection using the Riskiness Index." Studies in Economics and Finance 35, no. 2 (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 base

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Lim, Byounghyo, Sol Kim, and Ingoo Han. "Does Bitcoin Contribute to Portfolio Performance?" Korean Journal of Financial Studies 51, no. 6 (2022): 665–92. http://dx.doi.org/10.26845/kjfs.2022.12.51.6.665.

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This study analyzes the role of Bitcoin as an investment asset in a global multi-asset portfolio. For portfolio construction, we use a risk-based portfolio strategy that excludes estimates of future expected returns. We derive the optimal asset allocation ratio of Bitcoin included in the global multi-asset portfolio and compare the performance with the general portfolio. We find that the average investment weight of Bitcoin in the portfolio is 1.8%, and the portfolio containing Bitcoin shows superior investment performance compared to the portfolio without Bitcoin.

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Moodley, Fabian, Sune Ferreira-Schenk, and Kago Matlhaku. "Determinants of South African Asset Market Co-Movement: Evidence from Investor Sentiment and Changing Market Conditions." Risks 13, no. 1 (2025): 14. https://doi.org/10.3390/risks13010014.

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The co-movement of multi-asset markets in emerging markets has become an important determinant for investors seeking diversified portfolios and enhanced portfolio returns. Despite this, studies have failed to examine the determinants of the co-movement of multi-asset markets such as investor sentiment and changing market conditions. Accordingly, this study investigates the effect of investor sentiment on the co-movement of South African multi-asset markets by introducing alternating market conditions. The Markov regime-switching autoregressive (MS-AR) model and Markov regime-switching vector a

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Lolic, Marin. "Practical Improvements to Mean-Variance Optimization for Multi-Asset Class Portfolios." Journal of Risk and Financial Management 17, no. 5 (2024): 183. http://dx.doi.org/10.3390/jrfm17050183.

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In the more than 70 years since Markowitz introduced mean-variance optimization for portfolio construction, academics and practitioners have documented numerous weaknesses in the approach. In this paper, we propose two easily understandable improvements to mean-variance optimization in the context of multi-asset class portfolios, each of which provides less extreme and more stable portfolio weights. The first method sacrifices a small amount of expected optimality for reduced weight concentration, while the second method randomly resamples the available assets. Additionally, we develop a proce

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9

KORN, RALF, and ELISABETH LEOFF. "MULTI-ASSET WORST-CASE OPTIMAL PORTFOLIOS." International Journal of Theoretical and Applied Finance 22, no. 04 (2019): 1950019. http://dx.doi.org/10.1142/s0219024919500195.

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We generalize the worst-case portfolio approach of Korn & Wilmott (2002) to a multi-asset setting. The nonuniqueness of indifference strategies results in a much more complicated portfolio optimization problem as in the single risky asset framework. To determine the worst-case optimal portfolio processes we develop two new approaches, a Lagrangian multiplier approach in the log-utility case and a combined constrained HJB equation and indifference strategy approach for dealing with power-utility functions. Various examples illustrate remarkable effects and differences compared to the single

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Tokat-Acikel, Yesim, Marco Aiolfi, and Yiwen Jin. "Multi-Asset Value Payoff: Is Recent Underperformance Cyclical?" Journal of Risk and Financial Management 14, no. 10 (2021): 477. http://dx.doi.org/10.3390/jrfm14100477.

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Recent value factor underperformance has called into question whether the value factor payoff is cyclically low, or if there are more structural challenges. We use a new approach to explore a link between the well-known macroeconomic exposures of traditional asset classes and those of value premia in a multi-asset context, focusing on country equities, bonds, and currencies in developed markets. Taking advantage of the cross-country inflation and growth expectations implicit in every value portfolio, we derive the net inflation and real growth characteristics embedded in each asset class carry

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Lekander, Jon R. G. M. "Real estate portfolio construction for a multi-asset portfolio." Journal of Property Investment & Finance 33, no. 6 (2015): 548–73. http://dx.doi.org/10.1108/jpif-02-2015-0013.

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Purpose – The purpose of this paper is to explore how tenant end demand dependence and investment market segmentation, as estimated through sector type, impacts real estate portfolio strategy in the context of the multi-asset portfolio. Design/methodology/approach – The analysis is performed for six investor domeciles, for domestic and international investments over several cycles. The analysis is performed in a mean variance framework. Findings – The findings are consistent with the hypothesis that an investor benefits from investing in real estate assets where end demand is dependent on loca

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Moodley, Fabian, Sune Ferreira-Schenk, and Kago Matlhaku. "Time–Frequency Co-Movement of South African Asset Markets: Evidence from an MGARCH-ADCC Wavelet Analysis." Journal of Risk and Financial Management 17, no. 10 (2024): 471. http://dx.doi.org/10.3390/jrfm17100471.

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The growing prominence of generating a well-diversified portfolio by holding securities from multi-asset markets has, over the years, drawn criticism. Various financial market events have caused asset markets to co-move, especially in emerging markets, which reduces portfolio diversification and enhances return losses. Consequently, this study examines the time–frequency co-movement of multi-asset classes in South Africa by using the Multivariate Generalized Autoregressive Conditional Heteroscedastic–Asymmetrical Dynamic Conditional Correlation (MGARCH-DCC) model, Maximal Overlap Discrete Wave

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Liang, Sihan. "Application of Multi-Factor Financial Models in Asset Pricing." Advances in Economics and Management Research 11, no. 1 (2024): 121. http://dx.doi.org/10.56028/aemr.11.1.121.2024.

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This paper explores the theoretical underpinnings and practical applications of multi-factor models in stock portfolio management. It outlines the theoretical frameworks of multi-factor asset pricing models such as APT theory, Fama-French three-factor, and five-factor models, analyzing how various risk factors influence asset returns. The study then discusses methods for identifying core risk factors empirically, constructing portfolios, optimizing weight allocations, and evaluating investment performance. Empirical testing using data from the CSI 300 and CSI 800 indices confirms that optimize

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Zhao, Hanyun. "Study on the Effectiveness of Multi-asset Allocation in Combating Inflation." Modern Economics & Management Forum 6, no. 3 (2025): 402. https://doi.org/10.32629/memf.v6i3.4021.

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This paper aims to explore the effectiveness of diversified asset allocation in combating inflation. By analyzing the mechanism by which inflation affects asset prices, and based on modern portfolio theory and other foundations, empirical research is conducted using historical data analysis methods. The results show that diversified asset allocation portfolios generally outperform single-asset portfolios in better withstanding inflation shocks and achieving asset preservation and appreciation. At the same time, practical strategies are proposed, including asset class selection, ratio adjustmen

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Balbás, Alejandro, Beatriz Balbás, and Raquel Balbás. "Optimal Design of Multi-Asset Options." Risks 13, no. 1 (2025): 16. https://doi.org/10.3390/risks13010016.

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The combination of stochastic derivative pricing models and downside risk measures often leads to the paradox (risk, return) = (−infinity, +infinity) in a portfolio choice problem. The construction of a portfolio of derivatives with high expected returns and very negative downside risk (henceforth “golden strategy”) has only been studied if all the involved derivatives have the same underlying asset. This paper also considers multi-asset derivatives, gives practical methods to build multi-asset golden strategies for both the expected shortfall and the expectile risk measure, and shows that the

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Atkinson, C., and S. Mokkhavesa. "Multi‐asset portfolio optimization with transaction cost." Applied Mathematical Finance 11, no. 2 (2004): 95–123. http://dx.doi.org/10.1080/13504860410001693496.

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ÖZER, Gökhan, and Ayşegül YILDIRIM KUTBAY. "Testing multi-factor asset pricing models in Borsa Istanbul." Business & Management Studies: An International Journal 10, no. 2 (2022): 555–68. http://dx.doi.org/10.15295/bmij.v10i2.2043.

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This study aims to test the validity of multi-factor asset pricing models on the portfolios of non-financial companies whose shares are traded on Borsa Istanbul and to identify the model with the best explanatory power. Accordingly, the relationships between annual book-to-market equity ratio, firm size, market portfolio return, return on capital, operating profitability, momentum and value-added intellectual coefficient between 2008-2019 were analyzed using panel data analysis. As a result of the analyses made, it has been observed that Fama French's three and five factors, Carhart (momentum)

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18

Ntare, Hamdan Bukenya, John Weirstrass Muteba Mwamba, and Franck Adekambi. "Dynamic Portfolio Optimization with Diversification Analysis and Asset Selection Amidst High Correlation Using Cryptocurrencies and Bank Equities." Risks 13, no. 6 (2025): 113. https://doi.org/10.3390/risks13060113.

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There has been growing interest among investors to include cryptocurrencies in their portfolios because of their diversification potential. However, the diversification role of cryptocurrencies when added to South African bank equities is yet to be determined. This study rigorously evaluates asset co-movement and diversification benefits of integrating cryptocurrencies into South African bank equity portfolios. Using advanced financial engineering techniques, including multi-asset particle swarm optimizer (MA-PSO), random optimizer, and a static equal-weighted portfolio (EWP) model, this study

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19

Piasecki, Krzysztof, and Joanna Siwek. "Multi-asset portfolio with trapezoidal fuzzy present values." Przegląd Statystyczny 65, no. 2 (2019): 183–99. http://dx.doi.org/10.5604/01.3001.0014.0535.

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The main purpose of the following paper is to present characteristics of a multi-asset portfolio in case of present values of composing financial instruments being modelled by a trapezoidal fuzzy number. Throughout the analysis a fuzzy expected discount factor and imprecision risk assessments are calculated. Thanks to that, there arises a possibility to describe the influence of portfolio diversification on imprecision risk. Presented theoretical inference and obtained conclusions are supported by numerical example.

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BISWAS, SUBHOJIT, and DIGANTA MUKHERJEE. "A PROPOSAL FOR MULTI-ASSET GENERALIZED VARIANCE SWAPS." Annals of Financial Economics 14, no. 04 (2019): 1950019. http://dx.doi.org/10.1142/s2010495219500192.

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This paper proposes swaps on two important new measures of generalized variance, namely the maximum eigenvalue and trace of the covariance matrix of the assets involved. We price these generalized variance swaps for financial markets with Markov-modulated volatilities. We consider multiple assets in the portfolio for theoretical purpose and demonstrate our approach with numerical examples taking three stocks in the portfolio. The results obtained in this paper have important implications for the commodity sector where such swaps would be useful for hedging risk.

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Bányai, Attila, Tibor Tatay, Gergő Thalmeiner, and László Pataki. "The Impact of Rebalancing Strategies on ETF Portfolio Performance." Journal of Risk and Financial Management 17, no. 12 (2024): 533. http://dx.doi.org/10.3390/jrfm17120533.

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This research explores the efficacy of rebalancing strategies in a diversified portfolio constructed exclusively with exchange-traded funds (ETFs). We selected five ETF types: short-term U.S. Treasury bonds, U.S. equities, global commodities, U.S. real estate investment trusts (REITs), and a multi-strategy hedge fund. Using a 10-year historical period, we applied a unique simulation model to generate random portfolios with varying asset weights and rebalancing tolerance bands, assessing the impact of rebalancing premiums on portfolio performance. Our study reveals a significant positive correl

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Syeda Fizza Abbas, Sumiya Tahir, and Sayyid Haider Mustafa Rizavi. "Multi-Asset Portfolio Optimization for Green and Non-Green Cryptocurrencies in G7 Using Machine Learning." Journal for Social Science Archives 3, no. 1 (2025): 1184–225. https://doi.org/10.59075/jssa.v3i1.197.

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This study examines the financial performance of diversified portfolios composed of various asset categories, including green cryptocurrencies, non-green cryptocurrencies, energy cryptocurrencies, stocks of leading companies, stocks of top energy companies, and stocks of prominent sustainable companies within the context of G7 nations. Additionally, it investigates the financial performance of green and non-green cryptocurrency portfolios across these regions. It aims to compare returns while examining the initiatives undertaken by these countries to foster sustainable financial systems. The r

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Kolluri, Bharat, Susan Wahab, and Mahmoud Wahab. "Systematic Covariations and Emerging Asian Equity Markets’ Diversification Benefits to US Equity Investors." Review of Pacific Basin Financial Markets and Policies 23, no. 02 (2020): 2050009. http://dx.doi.org/10.1142/s0219091520500095.

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Unconditional and conditional correlations have played a central role in portfolio analysis, optimization, and performance measurement. However, recent studies show these two correlation measures are inappropriate for measuring both financial integration and, therefore, diversification benefits. We use an alternative correlation measure that we refer to by factor model-implied correlation estimated from the systematic (predictable) portion of returns of a multi-factor model with several global risk factors. Estimated implied correlations, covariances, variances, and in-sample (predicted) mean

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Cai, Liang, and Zhixin Wu. "Intelligent Asset Allocation Portfolio Division and Recommendation." Journal of Organizational and End User Computing 36, no. 1 (2024): 1–23. http://dx.doi.org/10.4018/joeuc.354707.

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With the continuous development of financial markets, intelligent asset allocation has become a topic of great concern in the investment field. However, traditional asset allocation methods often face difficulties in grasping the relationship between diversity, risk and return, which limits its application in complex market environments. To solve this problem, this study introduces deep learning and knowledge graphs and proposes an intelligent asset allocation model. Our model makes full use of the advantages of the Knowledge Graph Embedding Model (KGE), LSTM, and Genetic Algorithm (GA) to bui

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Doeswijk, Ronald, Trevin Lam, and Laurens Swinkels. "The Global Multi-Asset Market Portfolio, 1959–2012." Financial Analysts Journal 70, no. 2 (2014): 26–41. http://dx.doi.org/10.2469/faj.v70.n2.1.

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Akian, Marianne, Jose Luis Menaldi, and Agnès Sulem. "Multi-asset portfolio selection problem with transaction costs." Mathematics and Computers in Simulation 38, no. 1-3 (1995): 163–72. http://dx.doi.org/10.1016/0378-4754(93)e0079-k.

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Dias, Alexandra. "Semiparametric estimation of multi-asset portfolio tail risk." Journal of Banking & Finance 49 (December 2014): 398–408. http://dx.doi.org/10.1016/j.jbankfin.2014.05.033.

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Zilbering, Yan, Victor Zhu, Greg Banis, and Harshdeep Ahluwalia. "Tax-Aware Portfolio Construction: A Multi-Asset Approach." Journal of Portfolio Management 51, no. 5 (2025): 64–83. https://doi.org/10.3905/jpm.2025.51.5.064.

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Qiu, Haiyang. "Investment Portfolio with Convex Optimization and Risk Adjustment Using Multi-Factor Model and Multi-Armed Bandit Algorithm." Advances in Economics, Management and Political Sciences 104, no. 1 (2024): 63–76. http://dx.doi.org/10.54254/2754-1169/104/2024ed0075.

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This paper examines the creation of investment portfolios through convex optimization, multifactor models, and the multi-armed bandit (MAB) algorithms, focusing on the KL-UCB strategy to optimize decisions in uncertain settings. It explores the impact of systematic risk factors using the Fama-French three-factor model, estimating the influence of market, size, and value premiums via linear regression. The use of Monte Carlo simulation is detailed for generating potential asset allocations and calculating their expected returns, volatility, and Sharpe ratios. The optimize minimize function from

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Qiu, Haiyang. "Investment Portfolio with Convex Optimization and Risk Adjustment Using Multi-Factor Model and Multi-Armed Bandit Algorithm." Advances in Economics, Management and Political Sciences 102, no. 1 (2024): 28–41. http://dx.doi.org/10.54254/2754-1169/102/2024ed0075.

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This paper examines the creation of investment portfolios through convex optimization, multifactor models, and the multi-armed bandit (MAB) algorithms, focusing on the KL-UCB strategy to optimize decisions in uncertain settings. It explores the impact of systematic risk factors using the Fama-French three-factor model, estimating the influence of market, size, and value premiums via linear regression. The use of Monte Carlo simulation is detailed for generating potential asset allocations and calculating their expected returns, volatility, and Sharpe ratios. The optimize minimize function from

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Shlonchak, Vasyl. "Diversification of the banks investment portfolio: an adaptive management model in the conditions of financial volatility." Galician economic journal 94, no. 3 (2025): 91–101. https://doi.org/10.33108/galicianvisnyk_tntu2025.03.091.

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In the context of increased financial volatility and macroeconomic instability, enhancing the efficiency of banks’ investment activity has become a strategic imperative. One of the most relevant approaches is the structural optimization of investment portfolios through asset diversification. However, existing models often fail to fully consider the correlation between portfolio components, their volatility, and market sensitivity, which significantly limits the accuracy of investment decision-making. This research aims to develop a scientifically grounded approach to improving the efficiency o

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Glas, Tobias N., and Thorsten Poddig. "Kryptowährungen in der Asset-Allokation: Eine empirische Untersuchung auf Basis eines beispielhaften deutschen Multi-Asset-Portfolios." Vierteljahrshefte zur Wirtschaftsforschung 87, no. 3 (2018): 107–28. http://dx.doi.org/10.3790/vjh.87.3.107.

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Zusammenfassung: Dieser Artikel zeigt, dass eine Beimischung von Kryptowährungen in ein Portfolio, bestehend aus mehreren deutschen Asset-Klassen, mit Vorsicht zu betrachten ist. Auf Grund einer hohen realisierten Volatilität werden Kryptowährungen unter einem Markowitz- und Risikoparitätsansatz nur geringfügig in ein Referenzportfolio aufgenommen. Gleichzeitig wird die Aufnahme der Kryptowährungen durch Mean-Variance-Spanning-Tests nicht unterstützt. Ferner stellt die Handelbarkeit dieser neuen Asset-Klasse sowie ihre Datenverfügbarkeit Probleme dar, die die Ergebnisse verfälschen könnte. Sum

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Wei, Pei. "Long-term General Asset Allocation for individual investors in Chinese securities market." BCP Business & Management 20 (June 28, 2022): 1207–16. http://dx.doi.org/10.54691/bcpbm.v20i.1120.

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Facing the boom of online transaction applications in Chinese securities market, individual investors in China vacillate between different assets while allocating assets. On account of the individual investors’ inferior ability to take risk, portfolio will be their best choices. However due to their unacquaintance to modern portfolio theory, individual investors need some instructions. As proved by Brinson in 1986, over 90% of the success of a portfolio owe to general asset selection. Hence, this paper primarily focused on providing suggestions to individual investors on general asset selectio

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Petukhina, Alla, and Erin Sprünken. "Evaluation of multi-asset investment strategies with digital assets." Digital Finance 3, no. 1 (2021): 45–79. http://dx.doi.org/10.1007/s42521-021-00031-9.

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AbstractThe drastic growth of the cryptocurrencies market capitalization boosts investigation of their diversification benefits in portfolio construction. In this paper with a set of classical and modern measurement tools, we assess the out-of-sample performance of eight portfolio allocation strategies relative to the naive 1/N rule applied to traditional and crypto-assets investment universe. Evaluated strategies include a range from classical Markowitz rule to the recently introduced LIBRO approach (Trimborn et al. in Journal of Financial Econometrics 1–27, 2019). Furthermore, we also compar

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Stöckl, Sebastian, Michael Hanke, and Martin Angerer. "PRIX – A risk index for global private investors." Journal of Risk Finance 18, no. 2 (2017): 214–31. http://dx.doi.org/10.1108/jrf-09-2016-0118.

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Purpose The purpose of this paper is to create a universal (asset-class-independent) portfolio risk index for a global private investor. Design/methodology/approach The authors first discuss existing risk measures and desirable properties of a risk index. Then, they construct a universal (asset-class-independent) portfolio risk measure by modifying Financial Turbulence of Kritzman and Li (2010). Finally, the average portfolio of a representative global private investor is determined, and, by applying the new portfolio risk measure, they derive the Private investor Risk IndeX. Findings The auth

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Zhuohuan, Hu, Lei Fu, Fan Yuxin, Ke Zong, Shi Ge, and Li Zichao. "Research on Financial Multi-Asset Portfolio Risk Prediction Model Based on Convolutional Neural Networks and Image Processing." Applied Science and Engineering Journal for Advanced Research 3, no. 6 (2024): 39–50. https://doi.org/10.5281/zenodo.14214385.

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In today's complex and volatile financial market environment, risk management of multi-asset portfolios faces significant challenges. Traditional risk assessment methods, due to their limited ability to capture complex correlations between assets, find it difficult to effectively cope with dynamic market changes. This paper proposes a multi-asset portfolio risk prediction model based on Convolutional Neural Networks (CNN). By utilizing image processing techniques, financial time series data are converted into two-dimensional images to extract high-order features and enhance the accuracy of ris

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Garafutdinov, Robert. "Development and approbation of a software solution for the investment portfolios formation using fractal analysis and predictive models." Applied Mathematics and Control Sciences, no. 4 (December 12, 2022): 201–23. http://dx.doi.org/10.15593/2499-9873/2022.4.11.

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This paper describes the development and approbation of the software solution, which implements the methodology of forming recommendations for the composition of investment portfolios using fractal analysis and long memory predictive models, which is the result of research conducted over several years at the Department of Information Systems and Mathematical Methods in Economics of PSU. The general algorithm of the program includes four main steps: 1) obtaining and preparing data; 2) sorting assets by the fractal dimension of their price series; 3) forecasting asset returns; 4) forming portfol

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Bamidele Oyedele, Joseph. "Performance and significance of UK-listed infrastructure in a mixed-asset portfolio." Journal of European Real Estate Research 7, no. 2 (2014): 199–215. http://dx.doi.org/10.1108/jerer-08-2013-0015.

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Purpose – This paper aims to examine the performance of UK-listed infrastructure over a unique investment period covering the global financial crisis and investigates the significance of UK infrastructure in a multi-asset portfolio. The analysis reveals the level of correlation of UK infrastructure with other major assets classes and substantiates the potential diversification benefits of including UK infrastructure within a mixed-asset portfolio. Design/methodology/approach – The study uses monthly investment return indices obtained from Thomson Reuters DataStream over a ten-year period (2001

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Ascioglu, Asli, and Kevin John Maloney. "From stock selection to multi-asset investment management." Managerial Finance 46, no. 5 (2019): 647–61. http://dx.doi.org/10.1108/mf-07-2018-0304.

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Purpose The purpose of this paper is to trace the evolution of the Archway Investment Fund (AIF) at Bryant University from its founding in 2005 as a portfolio focused exclusively on US equities to a multi-asset program that incorporates US equities, non-US equities, equity ETFs, REITs, individual bonds, fixed income ETFs and options. It also describes the explicit introduction of environmental, social and governance (ESG) considerations into the investment process. Design/methodology/approach The paper follows a case study approach. Findings The paper describes the programmatic changes that ac

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Filipozzi, Fabio, and Kersti Harkmann. "Optimal currency hedge and the carry trade." Review of Accounting and Finance 19, no. 3 (2020): 411–27. http://dx.doi.org/10.1108/raf-10-2018-0219.

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Purpose This paper aims to investigate the efficiency of different hedging strategies for an investor holding a portfolio of foreign currency bonds. Design/methodology/approach The simplest strategies of no hedge and fully hedged are compared with the more sophisticated strategies of the ordinary least squares (OLS) approach and the optimal hedge ratios found by the dynamic conditional correlation-generalised autoregressive conditional heteroskedasticity approach. Findings The sophisticated hedging strategies are found to be superior to the simple strategies because they lower the portfolio ri

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Erwin, Kyle, and Andries Engelbrecht. "Multi-Guide Set-Based Particle Swarm Optimization for Multi-Objective Portfolio Optimization." Algorithms 16, no. 2 (2023): 62. http://dx.doi.org/10.3390/a16020062.

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Portfolio optimization is a moop with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio optimization requires a trade-off coefficient to be specified in order to balance the two objectives. Erwin and Engelbrecht proposed a set-based approach to single-objective portfolio optimization, namely, sbpso. sbpso selects a sub-set of assets that form a search space for a secondary optimization task to optimize the asset weights. The authors found that sbpso was able to identify good solutions to portfolio optimization problems and noted the benefits of redef

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Aliaga-Diaz, Roger, Giulio Renzi-Ricci, Brennan O’Connor, and Harshdeep Ahluwalia. "Integrating Private Equity in a Liquid Multi-Asset Portfolio." Journal of Portfolio Management 48, no. 9 (2022): 39–60. http://dx.doi.org/10.3905/jpm.2022.48.9.039.

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Levy, Haim. "Futures, spots, stocks and bonds: Multi-asset portfolio analysis." Journal of Futures Markets 7, no. 4 (1987): 383–95. http://dx.doi.org/10.1002/fut.3990070404.

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Lian, Yu-Min, and Jun-Home Chen. "Portfolio selection in a multi-asset, incomplete-market economy." Quarterly Review of Economics and Finance 71 (February 2019): 228–38. http://dx.doi.org/10.1016/j.qref.2018.08.006.

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Zhang, Yu, and Harshdeep Ahluwalia. "A Rational Multi-Asset Portfolio Rebalancing Decision-Making Framework." Journal of Portfolio Management 50, no. 5 (2024): 11–24. http://dx.doi.org/10.3905/jpm.2024.50.5.011.

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Makarov, Roman N. "Option Pricing and Portfolio Optimization under a Multi-Asset Jump-Diffusion Model with Systemic Risk." Risks 11, no. 12 (2023): 217. http://dx.doi.org/10.3390/risks11120217.

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We explore a multi-asset jump-diffusion pricing model, combining a systemic risk asset with several conditionally independent ordinary assets. Our approach allows for analyzing and modeling a portfolio that integrates high-activity security, such as an exchange trading fund (ETF) tracking a major market index (e.g., S&P500), along with several low-activity securities infrequently traded on financial markets. The model retains tractability even as the number of securities increases. The proposed framework allows for constructing models with common and asset-specific jumps with normally or e

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Pooja, R., Parthajit Kayal, and Moinak Maiti. "Enhancing portfolio decision-making: a capital asset pricing model-based clustering analysis." Journal of Economic Studies 51, no. 9 (2024): 358–79. https://doi.org/10.1108/jes-08-2024-0573.

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PurposeTo enhance portfolio decision-making using a capital asset pricing model-based clustering analysis.Design/methodology/approachCapital asset pricing model (CAPM); K-means clustering; agglomerative clustering.FindingsEmploying clustering along with CAPM to identify varying levels of risk appetite among customers enables the customization of security recommendations, enhancing client satisfaction and portfolio performance.Originality/valueBy employing multi-factor models as the foundation for clustering, thereby integrating additional dimensions of risk and return.

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Huang, Siyu. "Advancing portfolio optimization: The convergence of machine learning and traditional financial models." Applied and Computational Engineering 57, no. 1 (2024): 206–11. http://dx.doi.org/10.54254/2755-2721/57/20241335.

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This paper delves into the transformative impact of machine learning (ML) on portfolio optimization, showcasing how ML algorithms can significantly enhance traditional financial models such as the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). Through a comprehensive examination of regression analysis, classification algorithms, and reinforcement learning, we illustrate the methodologies by which ML refines the prediction of asset returns, assesses investment risks, and dynamically adjusts portfolio allocations. We discuss the integration of ML with CAPM and APT to

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Tian, Manwen, Shurong Yan, and Xiaoxiao Tian. "Discrete approximate iterative method for fuzzy investment portfolio based on transaction cost threshold constraint." Open Physics 17, no. 1 (2019): 41–47. http://dx.doi.org/10.1515/phys-2019-0005.

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Abstract There are many non-probability factors affecting financial markets and the return on risk assets is fuzzy and uncertain. The authors propose new risk measurement methods to describe or measure the real investment risks. Currently many scholars are studying fuzzy asset portfolios. Based on previous research and in view of the threshold value constraint and entropy constraint of transaction costs and transaction volume, the multiple-period mean value -mean absolute deviation investment portfolio optimization model was proposed on a trial basis. This model focuses on a dynamic optimizati

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Hirano, Masanori, Kiyoshi Izumi, Takashi Shimada, Hiroyasu Matsushima, and Hiroki Sakaji. "Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations." Journal of Risk and Financial Management 13, no. 4 (2020): 75. http://dx.doi.org/10.3390/jrfm13040075.

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In this study, we assessed the impact of capital adequacy ratio (CAR) regulation in the Basel regulatory framework. This regulation was established to make the banking network robust. However, a previous work argued that CAR regulation has a destabilization effect on financial markets. To assess impacts such as destabilizing effects, we conducted simulations of an artificial market, one of the computer simulations imitating real financial markets. In the simulation, we proposed and used a new model with continuous double auction markets, stylized trading agents, and two kinds of portfolio trad

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