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Property is a key investment asset class that offers considerable benefits in a mixed-asset portfolio. Previous studies have concluded that property allocation should be within the 10-30 per cent range. However, there seems to be wide variation in theory and practice. Historical Australian superannuation data shows that the level of allocation to property asset class in institutional portfolios has remained constant in recent decades, restricted at 10 per cent or lower. This is seen by many in the property profession as a subjective measure and needs further investigation. The purpose of this paper is to compare the performance of the AU$431 billion industry superannuation funds’ strategic balanced portfolio against ten different passive and active investment strategies.

The analysis used 20 years (1995-2015) of quarterly data covering seven benchmark asset classes, namely: Australian equities, international equities, Australian fixed income, international fixed income, property, cash and alternatives. The 11 different asset allocation models are constructed within the modern portfolio theory framework utilising Australian ten-year bonds as the risk free rate. The Sharpe ratio is used as the key risk-adjusted return performance measure.

The ten different asset allocation models perform as well as the industry fund strategic approach. The empirical results show that there is scope to increase the property allocation level from its current 10-23 per cent. Upon excluding unconstrained strategies, the recommended allocation to property for industry funds is 19 per cent (12 per cent direct and 7 per cent listed). This high allocation is backed by improved risk-adjusted return performance.

The constrained optimal, tactical and dynamic models are limited to asset weight, no short selling and turnover parameters. Other institutional constraints that can be added to the portfolio optimisation problem include transaction costs, taxation, liquidity and tracking error constraints.

The 11 different asset allocation models developed to evaluate the property allocation component in industry superannuation funds portfolio will attract fund managers to explore alternative strategies (passive and active) where risk-adjusted returns can be improved, compared to the common strategic approach with increased allocation to property assets.

The research presents a unique perspective of investigating the optimal allocation to property assets within the context of active investment strategies, such as tactical and dynamic models, whereas previous studies have focused mainly on passive investment strategies. The investigation of these models effectively contributes to the transfer of broader finance and investment market theories and practice to the property discipline.

The existing literature offers various perspectives on integrating cryptocurrencies into investment portfolios; yet, there is a gap in understanding the behaviours, attitudes and cross-investment links of individual investors. This study, grounded in the modern portfolio theory and the random walk theory, aims to add empirical insights that are specific to the UK context. It explores four hypotheses related to the influence of socio-demographics, digital adoption, cross-investment behaviours and financial attitudes on cryptocurrency owners.

This study uses a logistic regression model with secondary data from the Financial Lives Survey 2020 to assess the factors impacting cryptocurrency ownership. A total of 29 variables are used, categorized into four groups aligned with the hypotheses. Additionally, hierarchical clustering analysis was conducted to further explore the cross-investment links.

The study reveals a significant lack of diversification among UK cryptocurrency investors, a pronounced inclination towards high-risk investments such as peer-to-peer lending and crowdfunding, and parallels with gambling behaviours, including financial dissatisfaction and a propensity for risk-taking. It highlights the influence of demographic traits, risk tolerance, technological literacy and emotional attitudes on cryptocurrency investment decisions.

This study provides valuable insights into cryptocurrency regulation and retail investor protection, underscoring the necessity for tailored financial education and a holistic regulatory approach for investment products with comparable risk levels, with the aim of minimizing regulatory arbitrage. It significantly enhances our understanding of the unique dynamics of cryptocurrency investments within the evolving financial landscape.

During the past decade, there has been a revolution in investment management. If you pick up any professional investment journal in the States you will find that the articles and advertisements continually refer to Modern Portfolio Theory or MPT. At the heart of MPT is the notion of risk measurement. And nowadays, portfolio managers in almost any large American investment institution use modern risk measurement to analyse their portfolios. They realise that investment is as much about risk as return and that professionalism in their approach to risk is crucial. There are more than 20 institutions providing risk measures, and the investment community spends over $200 million per annum on MPT services alone.

The paper aims to describe the behind-the-scenes strategy and processes that fund managers use to make investment decisions.

The research involved semi-structured, face-to-face interviews with 34 fund managers in Istanbul, London, Melbourne and New York during 2012. Results describe their approach, and tie it back to theoretical explanations.

Large investors make limited use of neoclassical finance theory. They believe that securities markets trend over the short term, mean revert over the long term, and have upward sloping demand curves. They rely on qualitative techniques, think of security prices rather than returns, acknowledge constraints by their employer and clients, are heavily socialised and see no limitation from using similar approaches to competitors.

This is the first interview-based evaluation of global manager techniques since the market crash after 2008, and provides an innovative depiction of actual processes followed by institutional investors.

Claims as property modelling and forecasting techniques have developed to take account of new investment theories, property researchers have tended to follow the approach of modern portfolio theory and, sometimes, the capital asset pricing model (CAPM). Argues that one of the reasons why property is often not included in actuarial property forecasting models for the purpose of asset allocation (which is a widespread perception in the property industry) is because actuaries have not made clear to property researchers the forms of their models, which are often quite different from those used in others parts of the finance literature. Explains how traditional investment theory can be adapted for actuarial use and how actuaries use forecasting models in asset allocation. Areas of property research which would assist actuaries develop better property forecasting models are identified.

This paper examines the effect of cryptocurrencies on the portfolio risk-adjusted returns of traditional and alternative investments within an emerging market economy.

The paper employs daily arithmetic returns from August 2015 to October 2018 of traditional assets (stocks, bonds, currencies), alternative assets (commodities, real estate) and cryptocurrencies. Using the mean-variance analysis, the Sharpe ratio, the conditional value-at-risk and the mean-variance spanning tests.

The paper documents evidence to support the diversification benefits of cryptocurrencies by utilising the mean-variance tests, improving the efficient frontier and the risk-adjusted returns of the emerging market economy portfolio of investments.

This paper firmly broadens the Modern Portfolio Theory by authenticating cryptocurrencies as assets with diversification benefits in an emerging market economy investment portfolio.

As far as the authors are concerned, this paper presents the first evidence of the effect of diversification benefits of cryptocurrencies on emerging market asset portfolios constructed using traditional and alternative assets.

Marketing/finance interface and application of its new insights in marketing decisions have recently found great interest among marketing researchers and practitioners. There is a relatively large body of marketing literature about incorporating modern portfolio theory (MPT) into customer portfolio context and taking advantage of it in marketing resource allocation decisions. Previous studies have modelled customer portfolio risk in the form of historical return/profitability volatility of customer base. However, the risk is a future-oriented measure, and deals with future volatility associated with return stream. This study aims to address this research problem.

The well-known Pareto/non-binomial distribution (NBD) approach is used to model customer purchases in a non-contractual setting of research practice. Then, the results were used to simulate the customers’ future buying behaviour and associated returns via the Monte Carlo simulation approach. Subsequently, the mean-variance portfolio optimization model was applied to find the optimal customer portfolio mix.

The results illustrated the better performance of the proposed efficient portfolio versus the current customer portfolio. These results are applicable in analyzing customer portfolio composition, and can be used as a guidance to make decisions about marketing resource allocation in different segments.

This study proposes a new approach to analyze customer portfolio by using the customers’ future buying behaviour. Taking advantage of rich marketing literature about statistical assumptions describing the customers’ buying behaviour, this study tries to take some steps forward in the application of the MPT theory in customer portfolio management context.

This paper presents a combined method of ensemble learning and genetics to rebalance the corporate portfolio. The primary purpose of this paper is to determine the amount of investment in each of the shares of the listed company and the time of purchase, holding or sale of shares to maximize total return and reduce investment risk.

To achieve the goals of the problem, a two-level combined intelligent method, such as a support vector machine, decision tree, network Bayesian, k-nearest neighbors and multilayer perceptron neural network as heterogeneous basic models of ensemble learning in the first level, was applied. Then, the majority vote method (weighted average) in the second stage as the final model of learning was collectively used. Therefore, the data collected from 208 listed companies active in the Tehran stock exchange (http://tsetmc.com) from 2011 to 2015 have been used to teach the data. For testing and analysis, the data of the same companies between 2016 and 2020 have been used.

The results showed that the method of combined ensemble learning and genetics has the highest total stock portfolio yield of 114.12%, with a risk of 0.905%. Also, by examining the rate of return on capital, it was observed that the proposed method has the highest average rate of return on investment of 110.64%. As a result, the proposed method leads to higher returns with lower risk than the purchase and maintenance method for fund managers and companies and predicts market trends.

In the forthcoming research, there were no limitations to obtain research data were easily extracted from the site of Tehran Stock Exchange Technology Management Company and Rahvard Novin software, and simulation was performed in MATLAB software.

In this paper, using combined machine learning methods, companies’ stock prices are predicted and stock portfolio optimization is optimized. As companies and private organizations are trying to increase their rate of return, so they need a way to predict stock prices based on specific indicators. It turned out that this algorithm has the highest stock portfolio return with reasonable investment risk, and therefore, investors, portfolio managers and market timers can be used this method to optimize the stock portfolio.

The homogeneous and heterogeneous two-level hybrid model presented in the research can be used to predict market trends by market timers and fund managers. Also, adjusting the portfolio with this method has a much higher return than the return on buying and holding, and with controlled risk, it increases the security of investors’ capital, and investors invest their capital in the funds more safely. And will achieve their expected returns. As a result, the psychological security gained from using this method for portfolio arrangement will eventually lead to the growth of the capital market.

This paper tries to present the best combination of stock portfolios of active companies of the Tehran Stock Exchange by using the two-level combined intelligent method and genetic algorithm.

Formal portfolio selection strategies based on modern portfolio theory have increasingly been applied to real estate markets, despite the difficulties posed by the characteristics of property as an investment asset. As markets have “gone global”, so portfolio theory has been used to define international property portfolio strategies. Suggests that many of these studies are flawed, in that they neglect both methodological developments in portfolio theory and, critically, the economic fundamentals that drive property markets. This can lead to the adoption of investment strategies that create portfolios with a high exposure to risk. Outlines alternative approaches to global real estate investment.

Since the inception of modern portfolio theory, traditional asset classes have been the standard investment products for portfolio construction. With the introduction of cryptoassets such as cryptocurrencies, tokenized securities, smart contracts, and blockchain-based token assets, the crypto “revolution” has created a new asset class for consideration in a modern portfolio. This chapter explains cryptoassets in a portfolio, including their limitations and parameters as an asset class in a diversified portfolio. Finally, it reports an improvement in a new portfolio’s reward/risk ratio using the Sharpe ratio when adding cryptoassets to simulated equity, bonds, and real estate portfolios. A caveat is that a cryptoasset’s contribution to a well-diversified traditional portfolio differs from the performance of investing in a single and isolated cryptoasset.