Traditional beta is comprised of returns attributed to long-only exposure to general systematic risks. Alternative beta consists of exposure to alternative risks that can be achieved by various hedge fund investing techniques (derivatives, short-selling, etc.). Alpha is made up of returns in excess of beta and alternative beta due to manager skill.
What are the different types of alternative beta?Investable hedge fund indices, mechanical trading strategies (systematic, rules-based trading strategies that mimic hedge fund trades), and liquid indices (replication of hedge fund index returns using liquid, commonly-traded securities) are all examples of alternative beta.
What are the benefits of the Liquid Alternative Beta indices?Liquid Alternative Beta (“LAB”) indices aim to provide insight into the risk/return characteristics of hedge funds in aggregate by using liquid tradable securities. By doing so, these liquid replication strategies seek to provide hedge fund-like returns without direct hedge fund investment, thus enhancing liquidity and eliminating hedge fund headline risk. Such strategies are therefore suited to satisfy specific regulatory, liquidity, or transparency requirements.
Credit Suisse has a wealth of experience in working with hedge funds dating back to 1994. We have an in-depth understanding of the industry and are a leader in measuring hedge fund performance and working with hedge fund data, expertise gained through years of managing the Credit Suisse Hedge Fund Indexes (previously known as the Dow Jones Credit Suisse Hedge Fund Indexes). In fact, the Liquid Alternative Beta team that calculates and manages the LAB indices is part of the Beta Strategies group within Credit Suisse, the same area to which the Credit Suisse Hedge Fund Index group belongs. In an effort to leverage even more experience in constructing the replication models, we have partnered with leading academics that were among the first in the field of alternative beta research. We are pleased to be working with Professors Bill Fung, David Hsieh, and Narayan Naik in continuing to refine the algorithms used in our index models.