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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.

To achieve long‐term performance, superannuation balanced funds typically invest in a range of defined asset classes based on a strategic asset allocation approach. In an Australian context, the purpose of this paper is to examine the performance of the balanced investment option against eight different investment strategies and how the property allocation changes with different asset allocation models.

The analysis is based on ex post data covering 17 years (1995 to 2011). The selected passive and active allocation models are set 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.

Property provided the second highest risk adjusted return profile behind the alternative asset class. The different asset allocation models perform as well as the conventional strategic approach and in many instances property allocation is found to be under‐allocated on a return optimisation basis. Depending on the asset allocation model, property when included within a multi‐asset portfolio improves the portfolio risk‐adjusted return profile by 2 per cent to 28 per cent.

For an Australian superannuation balanced fund, the empirical results show that there is scope to increase the property allocation level from current 10 per cent to 23 per cent. This knowledge will be beneficial for funds currently re‐profiling investment portfolios to achieve stable risk‐adjusted returns.

The research contributes to both practical and academic fields, as it offers a methodological approach on how allocation to property assets can be improved using a series of passive and active asset allocation strategies.

In Australia, the A$2.2 trillion managed funds industry including the large pension funds (known locally as superannuation funds) are the dominant institutional property investors. While statistical information on the level of Australian managed fund investments in property assets is widely available, comprehensive practical evidence on property asset allocation decision-making process is underdeveloped. The purpose of this research is to identify Australian fund manager's property asset allocation strategies and decision-making frameworks at strategic level.

The research was undertaken in May-August 2011 using an in-depth semi-structured questionnaire administered by mail. The survey was targeted at 130 leading managed funds and asset consultants within Australia.

The evaluation of the 79 survey respondents indicated that Australian fund manager's property allocation decision-making process is an interactive, sequential and continuous process involving multiple decision-makers (internal and external) complete with feedback loops. It involves a combination of quantitative analysis (mainly mean-variance analysis) and qualitative overlay (mainly judgement, or “gut-feeling”, and experience). In addition, the research provided evidence that the property allocation decision-making process varies depending on the size and type of managed fund.

This research makes important contributions to both practical and academic fields. Information on strategic property allocation models and variables is not widely available, and there is little guiding theory related to the subject. Therefore, the conceptual frameworks developed from the research will help enhance academic theory and understanding in the area of property allocation decision making. Furthermore, the research provides small fund managers and industry practitioners with a platform from which to improve their own property allocation processes.

In contrast to previous property decision-making research in Australia which has mainly focused on strategies at the property fund investment level, this research investigates the institutional property allocation decision-making process from a strategic position involving all major groups in the Australian managed funds industry.

Over the years a number of tactical, dynamic and strategic approaches for asset allocation have been developed to improve the objectivity of portfolio management. One of the most popular approaches is to annually rebalance a portfolio of six to ten assets classes back to an equal or fixed percentage. Most researchers agree that this is essentially a contrarian strategy. The purpose of this paper is to develop and evaluate an asset allocation methodology using a biasing factor that can implement a momentum strategy for investors who might prefer momentum investing.

Three portfolio strategies, buy and hold, equal rebalancing and bias factor rebalancing are compared using 20 years of performance data and a diversified set of eight asset classes. The biased approach is then tested using two years of data not included in the original analysis data.

This research demonstrates that there is a wide range of active rebalancing approaches that can easily implement either a momentum or a stronger contrarian strategy. In addition, the findings present considerable evidence that a partial or full biased momentum approach can result in improved portfolio performance with reduced risk over longer time periods.

The results for buy and hold show that the traditional equal rebalancing strategy may not be worth the extra effort required to implement it.

Even though the full momentum approaches are less diversified than the buy and hold or the equal rebalancing strategies, it resulted in superior risk-adjusted returns as measured by the Sharpe ratio.

In a bid to broaden the understanding of the real estate investment decision-making framework, the purpose of this paper is to examine the real estate asset allocation decision-making practices of real estate funds in Nigeria, a developing economy. This is with a view to providing information toward enhancing real estate investment decisions.

A mixed-methods approach comprising a combination of literature review, expert interviews and semi-structured questionnaire survey is adopted for this study. Through literature review and expert interviews, the asset allocation decision-making process of institutional real estate funds was identified. Based on the literature review and expert discussions, a semi-structured questionnaire was developed and self-administered on fund/portfolio managers of 59 institutional real estate funds in Nigeria to investigate their asset allocation decision-making practice. Data were analyzed using descriptive and inferential statistics for the closed-ended questions while the open-ended questions were content analyzed.

The findings revealed that the asset allocation decision-making process utilized by public and private real estate funds follows an opportunistic asset accumulation approach. The decision-making process also varies depending on the nature of the fund. Further findings showed that government policies, political uncertainties and regulatory mechanism motivate asset allocation decisions. Moreover, majority of the sampled real estate funds employed a combination of in-house personnel and external consultants (hybrid), while mean/standard deviation and cash flow analysis (DCF, NPV) were mostly utilized by the funds in making property investment decisions.

The findings implied that the real estate asset allocation decision-making process of institutional property investors in Nigeria deviates from the normative model of the asset allocation process prescribed in the literature and varies depending on the nature of the real estate funds. As such, familiarization of institutional investors with government policies, political climate and other regulatory mechanism (barriers to entry) guiding the ownership and operation of real estate assets in the country could improve their real estate investment decisions.

The study complements and extends existing literature on real estate asset allocation decision-making process of institutional investors from the viewpoint of the actors involved in a developing African economy.

The purpose of this study is to investigate the relationship between predictability in return and investment strategy performance. Two measures that characterize investment strategies within a mean‐variance framework, an activity measure and a style measure, are developed and the performance of alternative strategies (e.g. contrarian, momentum, etc.) is examined when risky asset returns are mean reverting.

Returns are assumed to follow a multivariate Ornstein‐Uhlenbeck process, where reversion to a time‐varying mean is governed by an additional variable set, similar to that proposed by Lo and Wang (1995). Depending on its parameterization, this process is capable of producing an autocorrelation pattern consistent with empirical evidence, that is, positive autocorrelation in short‐horizon returns and negative autocorrelation in long‐horizon returns.

The results, for four uninformed investment strategies and assuming that returns are generated by a simple univariate Ornstein‐Uhlenbeck process, show that the unadjusted returns from the contrarian (momentum) strategy are greater than those from the other strategies when the mean reversion parameter, α, is greater than (less than) one. The results are expected, given the relationship between α and the first‐order autocorrelation in returns. The risk level (measured by either the standard deviation of returns or beta) of the contrarian strategy is the lowest at essentially all levels of mean reversion and the risk‐adjusted returns from the contrarian strategy, measured by the both the Sharpe and Treynor ratios, dominate those from the other strategies.

In future research, a number of issues not considered in this study may be investigated. The style measure developed here can be used to determine whether the results obtained hold when an informed, mean‐variance efficient active strategy is employed. In addition, the performance of both the informed and uninformed strategies may be examined under the assumption that the risky return process follows a multivariate Ornstein‐Uhlenbeck process. This work should provide findings that facilitate the separation of fund risk due to dynamic strategies from that due to time‐varying expected returns.

The methodology used here may be easily extended to consider a number of important issues, such as the frequency of portfolio rebalancing, transactions costs, and multiple asset portfolios, that are encountered in practice.

The approach used here provides insight into how predictability affects the relative performance of tactical investment strategies and, thus, may serve as a basis for determining the magnitude and persistence in autocorrelation required for active investment strategies to yield profits significantly different from those of passive strategies. In this sense, this study may have appeal for both academics and investment professionals.

The authors aim to demonstrate the importance of taking into account “mean reversion” in asset prices and show that this type of modeling leads to a high share of equities in pension funds’ asset allocations.

First, the authors will study the long-run statistical characteristics of selected financial assets during the 1895-2011 period. Such an analysis corroborates the fact that, for long holding periods, equities exhibit lower risk than other asset classes. Moreover, they will provide empirical evidence that stock market returns are negatively skewed in the short term and show that this negative skewness vanishes over longer time horizons. Both these characteristics favor the use of a semi-parametric methodology.

This empirical study led to two major findings. First, the authors noticed that the distribution of stock returns is negatively skewed over short time horizons. Second, they observed that the fat-tailed shape of the returns distribution disappears for time periods longer than five years. Finally, they demonstrated that stock returns exhibit “mean-reversion”. Consequently, the optimization program should not only take into account the non-Gaussian nature of returns in the short run but also incorporate the speed at which volatility “mean reverts” to its long-run mean.

To simulate portfolio allocation, the authors used a Cornish–Fisher Value-at-Risk criterion with the advantage of providing an allocation that is independent of the saver’s preferences parameters. A backtesting analysis including a calculation of replacement rates shows a clear dominance of the “non-Gaussian” strategy because the retirement outcomes under such a strategy would be positively affected.

The purpose of this paper is to conduct a controlled experiment to examine the effect of goal setting and affect framed feedback on repeated asset allocation investment decisions.

The design of the experiment is a 2×2 between subject design. Subjects allocated monies among four investments for 20 periods. One manipulation varied whether subjects received performance feedback in the form of a happy or sad face, while another manipulation varied whether subjects set a financial goal for themselves and received goal attainment performance feedback.

The main findings include: subjects initially allocate assets in a manner roughly consistent with their stated preference for risk; prior year asset performance leads subjects to make significant changes in portfolio asset allocation in a manner consistent with beliefs of positive autocorrelation in asset returns; and the addition of happy or sad faces to performance feedback information leads to even greater changes in asset allocation.

Using ideas from the theory on the self‐regulation of behavior and the role of affect in decision making, the authors develop an original framework to account for the results.

This article aims to explore three facets of the historical performance of a sample of actively managed unit trusts available to New Zealand investors: asset allocation, style analysis, and return attribution.

Because New Zealand does not require unit trusts to disclose their security holdings, the paper used returns‐based style analysis to infer how these trusts have allocated their funds among asset classes.

The research has found that, for unit trusts available to New Zealand investors, asset allocation can explain a significant amount of the differences in return across time and between trusts. Across time, asset allocation accounts for about 80 per cent of the variation in actual return. Between trusts, asset allocation explains about 60 per cent of the variation in returns. From either perspective, the choice of asset allocation is an important factor in explaining returns.

The paper suggests that active management barely earns its fees and that passive investments might do as well or better.