The effects of decision flexibility in the hierarchical investment decision process

Large institutional investors allocate their funds over a number of classes (e.g. equity, fixed income and real estate), various geographical regions and different industries. In practice, these allocation decisions are usually made in a hierarchical (top-down), consecutive way. At the higher decision level, the allocation is made on basis of benchmark portfolios (indexes). Such indexes are then set as targets for the lower levels. For example, at the top level the allocation decision is made on the basis of asset class benchmark indexes, on the second level the decisions are made on the basis of sector benchmark indexes, etc. Obviously, the lower levels have considerable flexibility to deviate from these targets. That is the reason why targets often come with limits on the maximally allowed deviation (or "tracking error") from these targets. The potential consequences of deviations from the benchmark portfolios have received very little attention in the literature. In this paper, we discuss and illustrate this influence. The lower level tracking errors with respect to the benchmark indexes propagate to the top level. As a result the risk-return characteristics of the actual aggregate portfolio will be different from those of the initial benchmark-based portfolio. We illustrate this effect for a two level process to allocate funds over individual US stocks and sectors. We show that the benchmark allocation approaches used in practice yield inferior solutions when compared to a non-hierarchical approach where full information about individual lower level investment opportunities is available. Our results reveal that even small deviations from the benchmark portfolios can cause large shifts in the top-level risk-return space. This implies that the incorporation of lower level information in the initial top-level decision process will lead to a different (possibly better) allocation.decision flexibility;multi-level decision process;porfolio management;tracking error analysis

The effects of decision flexibility in the hierarchical investment decision process

Large institutional investors allocate their funds over a number of classes (e.g. equity, fixed income and real estate), various geographical regions and different industries. In practice, these allocation decisions are usually made in a hierarchical (top-down), consecutive way. At the higher decision level, the allocation is made on basis of benchmark portfolios (indexes). Such indexes are then set as targets for the lower levels. For example, at the top level the allocation decision is made on the basis of asset class benchmark indexes, on the second level the decisions are made on the basis of sector benchmark indexes, etc. Obviously, the lower levels have considerable flexibility to deviate from these targets. That is the reason why targets often come with limits on the maximally allowed deviation (or "tracking error") from these targets. The potential consequences of deviations from the benchmark portfolios have received very little attention in the literature. In this paper, we discuss and illustrate this influence. The lower level tracking errors with respect to the benchmark indexes propagate to the top level. As a result the risk-return characteristics of the actual aggregate portfolio will be different from those of the initial benchmark-based portfolio. We illustrate this effect for a two level process to allocate funds over individual US stocks and sectors. We show that the benchmark allocation approaches used in practice yield inferior solutions when compared to a non-hierarchical approach where full information about individual lower level investment opportunities is available. Our results reveal that even small deviations from the benchmark portfolios can cause large shifts in the top-level risk-return space. This implies that the incorporation of lower level information in the initial top-level decision process will lead to a different (possibly better) allocation

A Framework for Managing a Portfolio of Socially Responsible Investments

In this paper we present and illustrate using real-life data a framework for managing an investment portfolio in which the investment opportunities are described in terms of a set of attributes and part of this set is intended to capture the effects on society. Here we link with the emerging literature on SRI: Socially Responsible Investment. Given the multifarious descriptions of the individual investment opportunities we show how these can be combined into portfolios with the same attributes at the portfolio level. Also we show how a manager can systematically be supported in the choice between different portfolio profiles. As part of the framework we use multi-criteria decision tools

A multiobjective model for passive portfolio management: an application on the S&P 100 index

This is an author's accepted manuscript of an article published in: “Journal of Business Economics and Management"; Volume 14, Issue 4, 2013; copyright Taylor & Francis; available online at: http://dx.doi.org/10.3846/16111699.2012.668859Index tracking seeks to minimize the unsystematic risk component by imitating the movements of a reference index. Partial index tracking only considers a subset of the stocks in the index, enabling a substantial cost reduction in comparison with full tracking. Nevertheless, when heterogeneous investment profiles are to be satisfied, traditional index tracking techniques may need different stocks to build the different portfolios. The aim of this paper is to propose a methodology that enables a fund s manager to satisfy different clients investment profiles but using in all cases the same subset of stocks, and considering not only one particular criterion but a compromise between several criteria. For this purpose we use a mathematical programming model that considers the tracking error variance, the excess return and the variance of the portfolio plus the curvature of the tracking frontier. The curvature is not defined for a particular portfolio, but for all the portfolios in the tracking frontier. This way funds managers can offer their clients a wide range of risk-return combinations just picking the appropriate portfolio in the frontier, all of these portfolios sharing the same shares but with different weights. An example of our proposal is applied on the S&P 100.García García, F.; Guijarro Martínez, F.; Moya Clemente, I. (2013). A multiobjective model for passive portfolio management: an application on the S&P 100 index. Journal of Business Economics and Management. 14(4):758-775. doi:10.3846/16111699.2012.668859S758775144Aktan, B., Korsakienė, R., & Smaliukienė, R. (2010). TIME‐VARYING VOLATILITY MODELLING OF BALTIC STOCK MARKETS. 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V., & Stasytyte, V. (s. f.). Decision Making Strategies in Global Exchange and Capital Markets. Advances and Innovations in Systems, Computing Sciences and Software Engineering, 17-22. doi:10.1007/978-1-4020-6264-3_4Tabata, Y., & Takeda, E. (1995). Bicriteria Optimization Problem of Designing an Index Fund. Journal of the Operational Research Society, 46(8), 1023-1032. doi:10.1057/jors.1995.139Teresienė, D. (2009). LITHUANIAN STOCK MARKET ANALYSIS USING A SET OF GARCH MODELS. Journal of Business Economics and Management, 10(4), 349-360. doi:10.3846/1611-1699.2009.10.349-36