As of October 31, 2018, the MSCI World Index delivered an annualized return of 10.02% while cash returned 0.34%, representing an annualized 10-year equity risk premium of 9.68%. There are two big questions on the minds of many investors: where will things go from here, and how we should position our portfolios as we potentially enter the “late cycle?” Read the full summary below.
Mean variance optimization (MVO) is a simple, yet well-regarded asset allocation technique designed to create a portfolio that maximizes it’s expected level of return for a given level of standard deviation. Many institutions construct diversified portfolios using this simple technique, attempting to maximize their risk-adjusted returns. While popular with many practitioners, MVO does have its drawbacks during implementation. The authors of this paper explore the applicability of constructing a portfolio using a risk parity approach, analyze the historical results, and discuss the benefits and issues with following this approach. Read the full summary below.
In “Technology, innovation and Disruption,” Jack Silbey and Filippo Rean highlight challenges for commercial real estate. As recent innovation in other industries has proceeded at a rapid pace, little has changed in the commercial real estate industry. The holding period for real estate is very long compared to other alternative asset classes, yet there is now a heightened pace of obsolescence. The industry is being disrupted. Commercial real estate is feeling the impact of innovation from exogenous industries and must adapt. Read the full summary below.
The CAIA Endowment Investable Index released each quarter in the Alternative Investment Analyst Review was introduced by Hossein Kazemi and Kathryn Wilkens in “A Simple Approach to the Management of Endowments.” Endowments and foundations are tax exempt and charitable organizations that rely on permanent pools of capital to fund their activities. Institutions such as colleges, universities, hospitals, museums, scientific organizations, charitable entities, and religious institutions own these pools of capital. Read the full summary below.
Traditional asset allocation methods don’t work well with alternative assets. This is because when alternative assets are included in a stock/bond portfolio they don’t fulfill requirements of an asset class factor model: 1) mutually exclusive assets, 2) exhaustive coverage of securities, and 3) asset classes each having returns that differ. In “Alternative Alphas and Asset Allocation,” Masao Matsuda argues that there is an artificial boundary between traditional asset classes and alternative assets. Read the full summary below.
Perhaps somewhat ironically, in the blockchain space which was originally implemented to eliminate middlemen (or trusted authorities), several intermediaries are now emerging. These middlemen provide a wide range of information on initial coin offerings (ICOs) to assist investors in their assessment of the opportunities. By the end of 2017, there were more than 51 platforms with 18 of them assigning ratings to ICOs. A natural question regarding the quality of the services provided by these middlemen then arises and is addressed in the paper, “New Blockchain Intermediaries: Do ICO Rating Websites Do Their Job Well?” by Dmitri Boreiko and Gioia Vidusso. Read the full summary below.
Investment banks offer access to both academic alternative risk premia (ARPs) and trading ARPs. Both include several distinct strategies, yet much heterogeneity exists within the same ARP strategies. This is in part, due to the many implementation choices available. Investors need to understand the risk and return characteristics of investable ARP products and how they may be similar or dissimilar to those factors that are well documented in the academic literature. In “An introduction to Alternative Risk Premia” Guillaume Monarcha surveys a wide range of ARP strategies available to investors and investigates their properties. Read the full summary below.
Two hedge funds reporting to follow the same strategy, may have very different return generating processes. As a result, statistical properties of their returns may be quite different. It is beneficial to investors to identify funds that perform differently than others labeled as following same strategy. The difference could provide alpha or indicate trouble that needs further investigation. The Editor’s Letter “Machine Learning and Hedge Fund Classification using a Self-Organizing Map” is concerned with the ability of a machine learning tool to make such identifications. It illustrates the ability of self-organizing maps (SOMs), which rely on artificial neural networks (ANNs), to group a set of long-short hedge funds into homogeneous groups. Read the full summary below.
In Hossein Kazemi’s Editor’s Letter “Risk Parity and Volatility Targeting Strategies: Recent Performance,” he highlights two volatility-based strategies that have recently increased in popularity. They are not active allocation strategies but are very different from traditional market capitalization weighting for strategic allocation. Recent news reports have speculated that fund flows to these volatility-based strategies have caused volatility to spike and equity prices to drop. Why are these strategies expected to work? How do they work? Do they impact the market? How have they performed and how are they expected to perform in the future? Read the full summary below.
How can an investor distinguish a good opportunity in the crypto-space from a poor one, or potentially even a fraudulent one? The paper by Jiafu An, Tinghhua Duan, Wenxuan Hou and Xinyu Xu summarized here seeks to answer this question for Initial coin offerings (ICO) tokens. Tokens are unlike cryptocurrencies such as bitcoin that are designed to enable transactions. ICO participation generally involves purchasing tokens from a blockchain based start-up company. Usually like the stocks of initial public offerings, the tokens are expected to subsequently rise in price as the company becomes successful. Read the full summary below.