”Digestible” Asset Allocation

Jahnke, William W.

doi:10.3905/joi.1998.408445

Cited by 6 publications

(7 citation statements)

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“…To explain the variability in performance by strategic asset allocation, we performed a simple regression of the fund's performance against the benchmarks [15], calculated and analyzed coefficients of determination R-square. 8 The calculations were also redone taking into consideration three differentiating variables, namely: the category of the funds (stock, bond or balanced), the fund size [21], and the level of the tracking error [22,23].…”

Section: Methodsmentioning

confidence: 99%

Strategic Asset Allocation and Active Management: Evidence from Moroccan Pension Funds

Kabiri

Msiyah

Nouisser

2022

jour

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The subject of the study is to evaluate the contribution of strategic asset allocation to the variability of Moroccan pension funds performance. The aim of the paper is to identify the role of active management factors, namely tactical allocation and security selection, in generating a performance surplus compared to strategic allocation. The relevance of the study is justified by the need to identify the sources of performance creation in order to face the commitments of Moroccan pension funds and to compensate for the decline and volatility of asset returns. The article addresses, through the use of simple linear regression methods, the relative importance of strategic asset allocation in explaining the variability of the performance of Moroccan pension funds. It introduces a scientific novelty through the use of the “performance attribution” method. The conclusions of the paper confirm the main role of strategic asset allocation, which varies according to the size of the fund, the asset classes, and the risk aversion of the manager.

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Section: Methodsmentioning

confidence: 99%

Strategic Asset Allocation and Active Management: Evidence from Moroccan Pension Funds

Kabiri

Msiyah

Nouisser

2022

jour

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“…Their result revealed that the Markowitz optimization model is the worstperforming model and dynamic allocation model outperforms static allocation model. Jahnke (1997) pointed asset allocation should be viewed as a dynamic process. It should take into consideration both pension obligation and capital market opportunities, including risk, otherwise makes no economic sense.…”

Section: Investment Portfoliomentioning

confidence: 99%

“…Nevertheless, such artificial intelligence techniques as neural networks, decision trees, Genetic algorithms, and genetic programming all use historical data for learning and training to produce fixed forecasting models. Historical data don't constitute a good representation of the forthcoming period (Amenc and Le Sourd 2003) and historical returns are not only unreliable indicators of future returns but also perverse indicators (Jahnke 1997). Therefore, this type of forecasting model cannot perform very well when the real environment is completely different from the past.…”

Section: Introductionmentioning

confidence: 98%

Constructing a dynamic stock portfolio decision-making assistance model: using the Taiwan 50 Index constituents as an example

2007

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There are several decisions in investment management process. Security selection is the most time-consuming stage. Tatical allocation is in order to take advantage of market opportunities based on short-term prediction (Amenc and Le Sourd in Portfolio theory and performance analysis. Wiley, 2003). Although it is difficult to keep track of the fluctuations of volatile financial markets, the capacity of artificial intelligence to perform spatial search and obtain feasible solutions has led to its recent widespread adoption in the resolution of financial problems. Classifier systems possess a dynamic learning mechanism, they can be used to constantly explore environmental conditions, and immediately provide appropriate decisions via self-aware learning. This study consequently employs a classifier system in conjunction with real number encoding to investigate how to obtain optimal stock portfolio based on investor adjustment cycle. We examine the constituents of the TSEC Taiwan 50 Index taking moving average (MA), stochastic indicators (KD), moving average convergence divergence (MACD), relative strength index (RSI) and Williams %R (WMS %R) as input factors, adopting investor-determined adjustment cycle to allocate capital, and then constructing stock portfolio. We have conducted empirical testing using weekly and monthly adjustment cycle; the results revealed that this study's decision-making assistance model yields average annual interest rate of 49.35%, which is significantly better than the −6.59% of a random purchase model. This research indicates that a classifier system can effectively monitor market fluctuations and help investors obtain relatively optimal returns. The assistance model proposed in this study thus can provide really helpful decision-making information to investors.

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Section: Introductionmentioning

confidence: 99%

“…However, these findings have often been misinterpreted by academics and professional investors causing a controversy that stems from using the same results to answer the inappropriate questions. 1 In this sense, Jahnke (1997) argues that Brinson et al (1986that Brinson et al ( , 1991 analyse the wrong question, focusing on the return variability explained by asset allocation rather than on the level of return. 2 More recently, Nuttall (2000) and Jahnke (2000) state that Brinson et al studies bear responsibility for the confusion surrounding the matter because they provide unclear conclusions in their results.…”

Section: Introductionmentioning

confidence: 99%

The importance of asset allocation in Spanish equity pension plans

Andreu

Agudo

Vicente

2007

Journal of Pension Economics and Finance

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Following the methodological approach taken by Ibbotson and Kaplan (2000), we provide evidence of a major contribution of strategic asset allocation to Spanish equity personal pension plan performance, finding that on average more than 90 % of variability of returns over time, and about 70 % of the variation of returns among plans, are explained by strategic policy.Furthermore, we also have evidence that survivor and look-ahead bias detected in previous research have very little impact on the conclusions about the importance of asset allocation on the variability of returns over time.The importance of asset allocation to explain the variability of returns over time is quite similar for the different investment vocations considered in our study, Euro zone and global equity. Very similar results are also found when we consider the size of the Spanish plans as an explanatory factor for the contribution of asset allocation to performance.Finally, the value that active management adds to the mere passive tracking of the strategic policy is not statistically different to the management costs of the plans.

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”Digestible” Asset Allocation

Cited by 6 publications

References 0 publications

Strategic Asset Allocation and Active Management: Evidence from Moroccan Pension Funds

Strategic Asset Allocation and Active Management: Evidence from Moroccan Pension Funds

Constructing a dynamic stock portfolio decision-making assistance model: using the Taiwan 50 Index constituents as an example

The importance of asset allocation in Spanish equity pension plans

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