Managing Partner, Sauma Capital LLC & Professor, Columbia University
Welcome to the third issue of the tenth volume of The Journal of Investment Strategies.
The first two papers in this issue focus on multifactor investment strategies. The cross-sectional approach to performance attribution is a simple and flexible tool that provides an intuitive and accurate explanation of portfolio returns in the context of multifactor investing. A tremendous amount of academic research has been published on multifactor investment strategies. It has been shown that, in the long term, a few factors have historically earned excess returns above the market. These two papers revisit the cross-sectional approach and address some practical approaches.
In our first paper, titled “Performance attribution for multifactorial equity portfolios”, Frederic Abergel and Thomas Heckel address the performance analysis of multifactor investment strategies. They reevaluate the cross-sectional approach to the performance analysis of multifactor investment strategies. Their main contributions are
- the use of a cross-sectional projection of asset returns onto the factor portfolio weights to form approximate portfolio returns;
- the introduction of nonlinear interaction terms between factors that reproduce the investment portfolio construction; and
- a decomposition of the portfolio performance as the sum of factor contributions.
The proposed method has several advantages over other time-series-based or general cross-sectional regression models: it reflects the current state of the investment portfolio, it is parsimonious in the number of explanatory variables, it leads to an approximation of the portfolio returns that has a small residual error, and it provides a straightforward interpretation of the portfolio performance in terms of the factors it is designed from. Abergel and Heckel first present and explain the method in detail and then discuss its applications to multifactor equity strategies.
In the issue’s second paper, “A practitioner’s view of the long-term and recent performance of multifactor investment strategies”, Ding Liu studies the performance of factor investment strategies from a practitioner’s point of view. First he creates a multifactor portfolio called the factor-tilted benchmark (FTB) using risk parity factor allocation. The FTB is created without practical considerations. Liu then examines the impact on historical performance by adding practical constraints to the FTB, such as no short selling, low portfolio turnover and low tracking error to the market capitalization benchmark and a limited number of portfolio holdings. He shows that, in the long term, multifactor portfolios in major equity markets still earn excess returns above those of the market with these practical constraints net of transaction costs. Liu then focuses on the recent disappointing performance of factor investing by estimating the probability of experiencing the recent performance or worse outcomes given prior history. While both the FTB and factor portfolios with practical constraints have fallen short of delivering their historical long-term outperformance in the last few years, factor portfolios with constraints have shown smaller performance slippage, and their recent performance looks more probable than the FTB based on prior history. Liu supports setting performance expectation on factor investment strategies using factor portfolios with practical constraints.
In “Forecasting volatility and market returns using the CBOE Volatility Index and its options”, the third and final paper in this issue, Spencer T. Stanley and William J. Trainor Jr. examine the CBOE Volatility Index (VIX) and its options. The VIX is the implied volatility calculated from short-maturity option prices on the Standard & Poor’s 500 (S&P 500) stock index. Stanley and Trainor’s findings demonstrate that VIX overestimates average volatility by approximately 3% but explains 55% of the S&P 500’s proceeding month’s volatility and 20% of its return. The smirks calculated from the VIX options’ implied volatility add additional explanatory power for the S&P 500 returns. None of the variables help predict the tail risk, skewness or kurtosis values.
A simple trading rule based on buying S&P 500 – whether VIX, the implied volatility from the options on the VIX or the VIX options’ volatility smirk decline – results in an additional 0.96% above the S&P 500 return in the following month. This only occurs approximately 10% of the time and would not beat a buy-and-hold strategy, but it could be used to adjust asset allocations at the margin. Buying equities only when the VIX decreases, which occurs approximately 50% of the time, outperforms a similar 50/50 stock/bond risk portfolio.
On behalf of the editorial board, we would like to thank our readers for their continued support and keen interest in our journal, and we hope you have been doing well throughout the Covid-19 pandemic. We look forward to sharing with you the growing list of practical papers on a wide variety of topics on modern investment strategies that we continue to receive from both academics and practitioners.
This paper revisits the cross-sectional approach to the performance analysis of multifactor investment strategies.
In this paper the author studies the performance of factor investment strategies from a practitioner’s point of view.
This paper examines the CBOE VIX, the VIX options’ implied volatility and the smirks associated with these options.