Developing an Infrastructure Index in Accordance with Investor Expectations

Abstract: Infrastructure consists of facilities and services that are considered essential to the functioning and economic productivity of society (Preqin, 2022). The rapid economic growth over the past decades has led to an increase in the demand for fundamental functions such as energy, telecommunications, and transportation. Increases in infrastructure investments are required on a global scale, not only to support economic growth globally, but also to help fulfill the United Nations Sustainable Development Goals (SDGs). Despite the large demand for infrastructure investments globally, planned governmental investments in infrastructure are not enough to bridge the gap. In order to bridge the infrastructure investment gap, there is a need for institutional investors to intervene, both in private and public markets. However, investors need to be able to assess how the market is performing as well as have accessible investment products that are linked to the infrastructure asset class. The quality of such investment products is dependent on the indices they are linked to, making it essential that infrastructure indices reflect the asset class in the way investors expect them to. Therefore, there is a need to assess the performance of existing infrastructure indices as well as to find a methodology for constructing a new index that more accurately fulfills investor expectations. This thesis compares three existing infrastructure indices in terms of three investment characteristics that investors look for in infrastructure investments: risk adjusted returns, inflation hedging properties, and stability during market downturns. These characteristics are evaluated by measuring the sharpe ratio, correlation with the consumer price index, and downmarket capture ratio. The thesis also proposes a methodology for finding an index that more accurately represents the infrastructure asset class in terms of the three aforementioned investment characteristics. The methodology is based on parameter optimization to find the set of inclusion criteria that maximizes performance. The thesis finds that the infrastructure indices are adequate in terms of risk adjusted returns and inflation hedging properties, but that they do not show consistent performance during down markets. It is concluded that existing indices are somewhat sufficient in what they set out to do, but that there is room for improvement in capturing the desired characteristics. The results of the thesis also indicate that a new index that more accurately represents the infrastructure asset class can be constructed by implementing inclusion criteria based on filters related to financial ratios associated with infrastructure companies, such as fixed asset turnover and dividend yield. In conclusion, an index with a minimum dividend yield criterion and a maximum fixed asset turnover ratio criterion can be constructed to more accurately capture the key characteristics of the infrastructure asset class.