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There is a new generation of investment managers which will disrupt asset management. These managers – which engage in Autonomous Learning Investment Strategies (ALIS) – use unstructured non-financial data, machine learning, and record-low computer processing and storage costs to run innovative investment strategies at lower costs vs. traditional fundamental and quantitative managers. ALIS managers typically don’t originate from the traditional finance world of Wall Street and MBAs, but rather they are run by PhDs and have their roots in the counter-culture arena which include gamers and hackers (though benign ones).
Historically, private equity General Partners (GPs) and their Limited Partners (LPs) have not paid a great deal of attention to the area of compliance management. This was likely a function of the nature of private equity investing where the focus was on long-term profitability as compared to day-to-day operational considerations potentially impacting the funds, coupled with a less restrictive regulatory environment. In the forthcoming book Private Equity Compliance: Analyzing Conflicts, Fees, and Risks (Wiley Finance, September 2018), the author provides a perspective on how we arrived at the current compliance environment as well as an overview of the historical development of the modern private equity compliance environment and other key issues.
It is widely accepted that a categorization methodology is necessary in order to make sense of size of the investment product landscape, a categorization methodology is necessary to allow the investor to make performance-based assumptions about a group of products as well as appropriately judge any given product against a peer group and benchmark. But discrete categorization poses challenges in a fluid investment landscape. This paper explores the current classification system used by three large industry participants, proposes a dynamic factor-based categorization methodology that can be easily customized to any given product, tests various methods of exploring that quantitative method, and shows that using out-of-sample data and dynamic factor-based categorization methodology significantly improves the future correlation between an alternative fund and its peer group.
Illiquid asset classes have become a significant contributor to return and risk for institutional investment portfolios. However, the dynamics of how these asset classes behave within multi-asset portfolios are not captured very well by traditional portfolio modelling processes. This paper explores how multi-asset investors can incorporate unique characteristics associated with illiquid asset classes into their multi-asset portfolio modelling to produce more complete risk and return estimates, as well as to inform future commitment/redemption activity.
A substantial body of academic research and a long track record of use in portfolios has led to a growing acceptance of factor investing within the investment community. Most of the academic research and practical implementation of factors has been done in the equity asset class, where factors have been used to explain equity risk and return. This paper explores the opportunities that exist when considering credit factors within fixed income.
This paper discusses the research behind why fund manager selection is so important in private equity and what we can learn from industry practitioners about what data to leverage during due diligence to make the most informed investment decisions.
Short volatility products were named as the main culprits for the market turbulence in early February of 2018. This article discusses how an Exchange Trade Fund (ETF) that is also accessible for retail investors can realize almost total loss in time span of just a few days. It explains how an Inverse Volatility ETF works and how the properties of the underlying VIX lead to times of both favorable and unfavorable risk profiles. Further points of discussion are how short volatility products make money in an environment of low volatility, their market power and why investors buy a product that has suffered disastrous losses.
Active investment management is crucially dependent on skill, i.e. the ability to deliver consistent outperformance. All types of unique investment skill form a space. Understanding a basis of this space helps build portfolios of active strategies. The author proposes one such basis by representing an arbitrary investment process as an abstract information processing system. The obtained 5-dimensional skill-based classification is meant to complement existing classifications. Its purpose is to assist investors in better understanding a menu of available investment strategies as well as to help asset managers to position themselves on that menu. This paper provides a detailed discussion of a risk premia related dimension of our proposed basis arguing that risk premia strategies require special skills.