Search results

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.

This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

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.

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.

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.

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.

The purpose of this paper is to investigate whether asset allocation across various industries listed on the Ghana Stock Exchange (GSE) varies across different monetary policy states.

This paper adopts the Markov Chain technique to split monetary policy into three different states. The authors further adopt the Markowitz portfolio optimization technique to find the minimum variance and optimum portfolio for the industries listed on the GSE.

The finding reveals a dynamic asset allocation, which varies the industry’s weight mix across the various monetary policy states enhance excess returns compared to the static asset allocation. Specifically, the authors find risk-return trade-off among industries listed on the GSE. Financial and Food and Beverage industries portfolios record high returns relative to the Government of Ghana 91-day Treasury bill. The Food and Beverage portfolio is the only portfolio that records relatively high excess returns across all the monetary policy states. The authors also find that, during expansionary state (high monetary policy rates) of the monetary policy, investors are to allocate about 69 and 30 percent of their investment into food and beverages and financials, respectively. Corner solution is found in the transient state where 100 percent of wealth is allocated to financial to obtain the optimum portfolio. The optimum portfolio in the contraction state assigns 52 percent to financials and 42 percent to manufacturing. In summary, the result supports the dependence of investors’ asset allocation decisions on monetary policy.

Therefore, the authors propose an investment strategy which is dynamic and takes into consideration the monetary policy states rather than static asset allocation which maintains the same industry weight mix over the investment period.

In sum, the authors interpret the result as support for the dependence of investors’ asset allocation decisions on monetary policy. In Ghana, an increase in the monetary policy appears to support industries listed on the equity market. The result also gives knowledge about investors’ asset allocation decisions on the GSE, which is practical balanced source of information for investors’ risk and return choices. For a prudent monetary policy framework, the monetary policy committee should monitor industries listed on the GSE. The result from the analysis has also an implication for investors, portfolio managers and fund managers to consider the state of the monetary policy in Ghana when making investment decisions.

The study differs from earlier research on asset allocation by breaking new grounds on two levels. First of all, based on the notion that different industries have different exposures to monetary policy states, the authors extend the portfolios by grouping the equities listed on the GSE into their industrial sectors. Second, the authors examine how investors’ optimal portfolio allocation may change depending on the state of monetary policy.

The asset allocation decision for a pension portfolio needs to consider several, sometimes conflicting, aspects. Most pension managers use models and processes that are developed for the traditional asset classes for analyzing this problem. The purpose of this paper is to investigate how real estate is included in this process, for what purpose and how the real estate portfolio is constructed.

Seven individuals responsible for the asset allocation process were interviewed, and their responses were analyzed with regards to organizational options and their real estate strategy.

It was found that real estate is held for three different purposes, risk diversification, inflation hedging/liability matching and return enhancement and that the allocation has increased over time. The allocation strategy has evolved at least in part in conjuncture with the organizational structure set in place to overcome real estate market frictions.

The interviews were geographically limited to pension funds domiciled in Sweden and Finland.

It is concluded that the organizational capabilities of the pension fund of handling real estate is an important consideration for the ensuing real estate portfolio.

The originality of this paper lies in that it is based on interviews with individuals who are responsible for the asset allocation decision at large pension funds. The findings of the paper identify areas of interest for future research.

It is widely believed that contrary to standard asset allocation theory, employees irrationally hold concentrated investments in company stock in their 401(k) plans thus bearing firm-specific risk that could otherwise have been diversified away (see e.g., Benartzi, 2001). However, in measuring any such lack of diversification costs, a unique tax benefit associated with such investments (available to those who choose the Net Unrealized Appreciation (NUA) strategy) has been hitherto ignored. We analyze an employee's optimal allocation of retirement assets among alternative investments, including company stock, in the presence of the NUA tax benefit. The employee has a standard power utility function and seeks to maximize expected utility from her after-tax wealth upon retirement. Based on simulations, we find that, even when company stock is stochastically dominated by investments in the market index, the employee will allocate a non-trivial part of her retirement funds to company stock for a wide range of parameter values. Consistent with empirical evidence, the allocation to company stock is greater for employees closer to retirement and when the company's stock has experienced substantial gain in value.