Essays on robust portfolio selection and pension finance

This thesis examines three different, but related problems in the broad area of portfolio management for long-term institutional investors, and focuses mainly on the case of pension funds. The first idea (Chapter 3) is the application of a novel numerical technique – robust optimization – to a real-world pension scheme (the Universities Superannuation Scheme, USS) for first time. The corresponding empirical results are supported by many robustness checks and several benchmarks such as the Bayes-Stein and Black-Litterman models that are also applied for first time in a pension ALM framework, the Sharpe and Tint model and the actual USS asset allocations. The second idea presented in Chapter 4 is the investigation of whether the selection of the portfolio construction strategy matters in the SRI industry, an issue of great importance for long term investors. This study applies a variety of optimal and naïve portfolio diversification techniques to the same SRI-screened universe, and gives some answers to the question of which portfolio strategies tend to create superior SRI portfolios. Finally, the third idea (Chapter 5) compares the performance of a real-world pension scheme (USS) before and after the recent major changes in the pension rules under different dynamic asset allocation strategies and the fixed-mix portfolio approach and quantifies the redistributive effects between various stakeholders. Although this study deals with a specific pension scheme, the methodology can be applied by other major pension schemes in countries such as the UK and USA that have changed their rules.

The Effects of Credit Risk on Dynamic Portfolio Management: A New Computational Approach

The study investigates the role of credit risk in a continuous time stochastic asset allocation model, since the traditional dynamic framework does not provide credit risk flexibility. The general model of the study extends the traditional dynamic efficiency framework by explicitly deriving the optimal value function for the infinite horizon stochastic control problem via a weighted volatility measure of market and credit risk. The model's optimal strategy was then compared to that obtained from a benchmark Markowitz-type dynamic optimization framework to determine which specification adequately reflects the optimal terminal investment returns and strategy under credit and market risks. The paper shows that an investor's optimal terminal return is lower than typically indicated under the traditional mean-variance framework during periods of elevated credit risk. Hence I conclude that, while the traditional dynamic mean-variance approach may indicate the ideal, in the presence of credit-risk it does not accurately reflect the observed optimal returns, terminal wealth and portfolio selection strategies.

Inhibitors to optimal project portfolio selection

The selection of projects and programs of work is a key function of both public and private sector organisations. Ideally, projects and programs that are selected to be undertaken are consistent with strategic objectives for the organisation; will provide value for money and return on investment; will be adequately resourced and prioritised; will not compete with general operations for resources and not restrict the ability of operations to provide income to the organisation; will match the capacity and capability of the organisation to deliver; and will produce outputs that are willingly accepted by end users and customers. Unfortunately,this is not always the case. Possible inhibitors to optimal project portfolio selection include: processes that are inconsistent with the needs of the organisation; reluctance to use an approach that may not produce predetermined preferences; loss of control and perceived decision making power; reliance on quantitative methods rather than qualitative methods for justification; ineffective project and program sponsorship; unclear project governance, processes and linkage to business strategies; ignorance, taboos and perceived effectiveness; inadequate education and training about the processes and their importance.

Return distribution predictability and its implications for portfolio selection

The inquiries to return predictability are traditionally limited to conditional mean, while literature on portfolio selection is replete with moment-based analysis with up to the fourth moment being considered. This paper develops a distribution-based framework for both return prediction and portfolio selection. More specifically, a time-varying return distribution is modeled through quantile regressions and copulas, using quantile regressions to extract information in marginal distributions and copulas to capture dependence structure. A preference function which captures higher moments is proposed for portfolio selection. An empirical application highlights the additional information provided by the distributional approach which cannot be captured by the traditional moment-based methods.

Contributing factors to optimal project portfolio selection

A modified Delphi approach has been applied in this study to investigate best practice and to determine the factors that contribute to optimal selection of projects. There are various standards and practices that some may recognise as representing best practice in this area. Many of these have similar characteristics and this study has found no single best practice. The study identified the factors that contribute to the optimal selection of projects as: culture, process, knowledge of the business, knowledge of the work, education, experience, governance, risk awareness, selection of players, preconceptions, and time pressures. All these factors were found to be significant; to be appropriate to public sector organisations, private sector organisations and government owned corporations; and to have a strong linkage to research on strategic decision making. These factors can be consolidated into two underlying factors of organisation culture and leadership.

Portfolio selection using artificial intelligence

The application of artificial intelligence in finance is relatively new area of research. This project employed artificial neural networks (ANNs) that use both fundamental and technical inputs to predict future prices of widely held Australian stocks and use these predicted prices for stock portfolio selection over a long investment horizon. The research involved the creation and testing of a large number of possible network configurations and draws conclusions about ANN architectures and their overall suitability for the purpose of stock portfolio selection.

Dynamic Server Selection in the Internet

As distributed information services like the World Wide Web become increasingly popular on the Internet, problems of scale are clearly evident. A promising technique that addresses many of these problems is service (or document) replication. However, when a service is replicated, clients then need the additional ability to find a "good" provider of that service. In this paper we report on techniques for finding good service providers without a priori knowledge of server location or network topology. We consider the use of two principal metrics for measuring distance in the Internet: hops, and round-trip latency. We show that these two metrics yield very different results in practice. Surprisingly, we show data indicating that the number of hops between two hosts in the Internet is not strongly correlated to round-trip latency. Thus, the distance in hops between two hosts is not necessarily a good predictor of the expected latency of a document transfer. Instead of using known or measured distances in hops, we show that the extra cost at runtime incurred by dynamic latency measurement is well justified based on the resulting improved performance. In addition we show that selection based on dynamic latency measurement performs much better in practice that any static selection scheme. Finally, the difference between the distribution of hops and latencies is fundamental enough to suggest differences in algorithms for server replication. We show that conclusions drawn about service replication based on the distribution of hops need to be revised when the distribution of latencies is considered instead.

Dynamic Server Selection using Bandwidth Probing in Wide-Area Networks

Replication is a commonly proposed solution to problems of scale associated with distributed services. However, when a service is replicated, each client must be assigned a server. Prior work has generally assumed that assignment to be static. In contrast, we propose dynamic server selection, and show that it enables application-level congestion avoidance. To make dynamic server selection practical, we demonstrate the use of three tools. In addition to direct measurements of round-trip latency, we introduce and validate two new tools: bprobe, which estimates the maximum possible bandwidth along a given path; and cprobe, which estimates the current congestion along a path. Using these tools we demonstrate dynamic server selection and compare it to previous static approaches. We show that dynamic server selection consistently outperforms static policies by as much as 50%. Furthermore, we demonstrate the importance of each of our tools in performing dynamic server selection.