HFs have a private nature and are not obliged to disclose their underlying investment practices or holdings to investors or the SEC. The general public has no access to this investment pool. In addition, HFs may use a wide range of financial instruments and any type of investment strategy even if this includes derivatives, short selling, leverage, real estate and illiquid or non-listed securities. Currently, there are more than 10,000 HFs and the first quarter of 2017 the total assets under management for the HF industry was $3.21 trillion, while the managed futures (CTA) industry was $381.1 billion according to BarclayHedge.
Unfortunately, there is no universal classification scheme for HFs. This is because HFs report (if they are willing) to various private database (DB) vendors and there is no a unique classification code for each HF strategy or style. DB vendors construct HF indices so as to track HF strategies’ performance and to build HF benchmarks for investors. However, the problem is that, very often, there are large differences between HF indices from different vendors, even when they are thought to represent the same strategy. Despite the fact that many investors use these indices as benchmarks, little is known about how these are constructed and what exactly represent in a practical manner.
In our study, we examine the HF index construction methodologies and more specific on the classification process by two DB vendors, namely HFR and S&P. We compare different methodologies using the same dataset and demonstrate using real data how HF indices can end up with different constitutes. We use the existing index engineering methodologies and compare them using the same dataset with practical examples, at the HF strategy level. We decompose these processes that most investors and researchers regard them as a black box. It is essential to mention that both vendors use similar ‘tree’ index structure and calculation using the NAV (net asset value) general principles. The main differences are in the classification methods.
We have some interesting results that shed light on HF index construction methodologies and more specific on the classification processes. Although the DB vendors use different methods and quantitative approaches, they are able to cluster HFs in a somewhat similar way. The implication is that the differences between the index vendors are mainly due to different datasets and the different inclusion criteria. Both vendors use rigorous quantitative and qualitative techniques through the due diligence process, so as to ensure that they produce high-quality representative indices. We demonstrate specific practical examples so as to support our findings.
Our study assists investors to understand the differences between the HF indices and select better benchmarks for their investments; it assists DB vendors to construct, collaborate or specialize in specific indices; it assists government authorities to collaborate with DB vendors to form a common HF pool with indices; it also assists researchers to have a better knowledge in HF indices when using them.
In addition, our study provides further research opportunities in the evaluation and identification of other quantitative techniques, beyond used by the underlying vendors. Another extension would be the examination of the best possible construction methods or practices in the index composition process in the HF industry. This could be done according to pre-specified criteria and investors’ needs.
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