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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.

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.

The purpose of this paper is to investigate the existence of behavioral biases, disposition effect and house money effect in investment decisions of defined benefit pension funds. It investigates the determinants of portfolios by examining whether pensions display risk seeking or risk aversion behavior in reaction to prior gains and losses.

The first research question is to examine the impact of prior period’s return and αs on existing portfolio allocation in equity, debt, real estate and other assets. In order to test this relationship, four separate regressions are estimated using the pooled data. Regression helps in examining the relationship between prior gains with current allocation in four categories of assets of varying degrees of riskiness (stocks, debt, real estate and other assets). In order to investigate the second research question on whether pension funds increase (decrease) their investments in risky (safer) assets due to prior gains and αs, the four variables representing the changes in portfolio allocation for each asset class over one period are employed. These changes in allocation are regressed against the prior year’s actual return, expected return, αs and a set of control variables.

The results suggest significant negative (positive) relationship between prior positive returns and αs with portfolio allocation in risky (safer) assets. Also, there is an increased (decreased) investment in safer (risky) assets following prior period’s positive returns and αs. The findings confirm the existence of disposition effect, while there is no evidence of house money effect.

The portfolio allocation of pension plans provides unique setting to investigate the relevance of behavioral finance and examine the role of psychological biases on risk taking. This study attempts to contribute to the literature by empirically investigating whether the tenets of behavioral finance are relevant in defined benefit pension fund’s portfolio allocation decisions. Specifically, it focuses on the determinants of portfolio choices by directly investigating pension funds’ reaction to prior period’s actual as well as risk adjusted return (or αs – the difference between the actual and expected return).

Mutual funds are the second most preferred investment option in India and have garnered considerable research interest. The focus of Indian studies thus far has been restricted to the bottom-up approach of investing which rewards a fund manager for picking winner stocks and generates superior returns. While changing portfolio allocation as per varying macro-trends has been instrumental in generating superior returns, it has not been given the desired attention. This study addresses this important research gap.

The authors analyze the industry selection ability of the fund manager on a robust sample by decomposing alpha into alpha due to industry selection and alpha attributable to stock selection. Alpha estimates are computed on a robust sample of 34 open-ended Indian equity mutual funds for a 10-year duration 2011–2020 using three base models of asset pricing – single-factor, four-factor and five-factor alpha under panel data methodology.

The study leads us to four major findings. One, industry selection explains more than two-fifth of the alpha both in cross-section and time series of returns; two, industry selection exhibits persistence for more than four quarters across asset pricing model; third, younger funds have level playing when alpha from picking right industries is concerned; four, broad industry allocation continues to explain superior returns as sector allocation undergoes consolidation during ongoing COVID-19 pandemic and funds increase exposure to defensive stocks, consistent with folio allocations as per macroeconomic conditions.

The authors find strong evidence of persistence in the case of alpha attributable to the industry selection component, and the findings are consistent with the persistence results reported in the empirical literature. While some funds excel in stock-picking skills and others excel in picking the right industries, both skills together make for winner funds that attract larger investor flows as investors chase superior performance. The authors also find no evidence of diseconomies of scale in the case of industry allocation alpha generated by the fund managers.

The results suggest a fresh approach for investors while making mutual fund investment decisions; the investors can achieve superior returns by assessing industry selection skills as it tends to provide a more holistic picture concerning a perennial question – why some funds outperform and continue to contribute to investor's wealth?

Mutual funds have become a favored investment option for Indian investors more so as a disciplined investment option owing to dismal financial literacy rates. The study throws light on a relatively unaddressed dimension of choosing winner funds. The significance of right sector allocation assumed even more significance with the onset of the pandemic which lends further credence to the findings of the study.

Research has been conducted on secondary data extracted from a well-cited database for Indian mutual funds. Empirical analysis and conclusion drawn are based on authentic statistical analysis and adds to the existing literature.

The purpose of this article is to describe the process of determining a fund allocation process that would further collection development goals by reflecting the university curriculum and support towards different programs.

The paper is based on a library's experience in using a Percentage Based Allocation formula.

The author describes the process and steps that led to the decision to use a Percentage Based Allocation formula, and the results of its implementation.

An allocation process that is tied into other collection development activities (a collection development policy; vendor slip plan profiles) strengthens the collecting goals of a library as a whole.

This article provides a model that other libraries can use as a template for developing their own budget allocation processes.

This study aims to empirically analyze the asset allocation capabilities of Outsourced Chief Investment Officers (OCIOs) in Korea. The empirical analysis used data from 35 funds that were evaluated by the Ministry of Strategy and Finance from 2012 to 2020. The results of the analysis are summarized as follows. First, this study found that funds that adopted OCIO improved their asset allocation performance. Second, the sensitivity between risk-taking and performance decreased for funds that adopted OCIO. Third, it is found that OCIO adoption improves a fund's asset management execution (tactical capabilities). This study has methodological limitations in which the methodology used in this study is not based on theoretical prior research, but on practical applications. However, considering the need to clearly analyze the capabilities of OCIO and the timeliness of the topic, this study is valuable and can provide meaningful information to funders who are considering adopting OCIO in the future.

Risk allocation as currently practiced in pension funds is typically characterized by: 1) measurement of the risk of returns relative to a benchmark, and 2) the assignment of asset class and profit center limits on potential losses that remain within the fund's overall risk appetite, yet do not excessively hinder achievement of performance targets. Recently, there has been an increased effort to make risk allocation more proactive, i.e., to use risk allocation as a tool for adding value, rather than simply as part of a monitoring or “warning flag” system (see “Covering Bases” [1998, p. 45] and Hemmerick [1998, p. 1]). The underlying notion is that allocated risk capital can improve performance when viewed as a target, rather than a limit, if the goal of the fund is to maximize returns, or, specifically, to maximize expected surplus growth aggregated across the fund, for a given total value at risk (VaR) of returns relative to the benchmark.

The purpose of this paper is to analyse whether the development of a needs‐based funding formula for resource allocation incorporates the needs of funders or the needs of the service providers.

The paper analyses interview data and documentary evidence gathered from a UK local education authority about the creation of a “needs‐based” formula for sharing resources to schools. It employs and extends a framework developed by Levačić and Ross to evaluate needs‐based formula funding.

Although formula funding is purported to be a more objective method of resource allocation, the paper finds that as with other resource allocation methods the power relations between the funder and the service provider impacts on the extent to which service provider needs are incorporated into the funding formula.

This paper considers only the funding of schools. Further work is needed to investigate formula funding for other public services.

Debates between funders and service providers should be encouraged by policy makers to ensure that allocations based on the funding formula are acceptable to service providers.

The paper provides a useful analysis of a needs‐based funding formula for resource allocation in schools and whether this incorporates the needs of funders or the needs of the service providers.

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.

Examines the construction of the funding formula, following the 1988 Education Act, used to determine the levels of devolved budgets in three English local education authorities (LEAs). Explains that, in each LEA, a team was formed to determine the funding formula. Also explains that, as most schools pre‐local management of schools (LMS) only kept aggregate records showing the cost of education at the levels of primary/secondary sectors rather than individual school level, the LMS teams faced serious problems in defining budget parameters, identifying cost elements and attributing costs to functions. More critically, points out that while the 1988 Education Act made it clear that the new budgeting system should be comprehensive in the sense of not merely reflecting past expenditure patterns but being based on perceived education needs, the LMS teams developed funding formulae which predominantly preserved the status quo established by historical expenditure patterns. Explores both the arguments and the mechanisms which each LMS team deployed in order to produce an incrementalist budgeting system and the constraints that operated on incrementalism.