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GCM Research Papers


Volatility Drag
August 2020
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Abstract
In this research note, we explore the effect of volatility on compound returns. We start with the so-called volatility drag, which refers to the well-known, but sometimes counterintuitive, difference between the arithmetic and geometric mean returns of a portfolio. We then present some results that show the effect of leverage on terminal wealth.
In Search of Negative Beta
July 2020
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Abstract
Portfolio protection against sharp declines in equity markets is an important allocation consideration for investors, and has been highlighted amidst the recent crisis. In this brief note we take a closer look at the effectiveness of bonds to deliver a hedge during equity drawdowns, and find that while historically they may have performed well, it is not clear they will do so going forward.
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The Winter's Tail - Protecting Against Equity Selloffs
June 2020
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Abstract
The health crisis that started its grip on the world at the beginning of 2020 shook the financial markets in February and March, leading to sudden and massive declines in the equity markets. Investors with long equity exposure saw gains that had accrued over years wiped out in mere weeks. While the markets have recouped a significant amount of these losses in the medium term since, the crisis has brought renewed interest to investments and investment strategies that can offer effective portfolio protection in such tail risk events. In this note we compare and contrast several investment approaches by assessing their long-run and crisis performance. We find that all of them deliver protection to some extent, allowing an investor to determine which approach is most suited as an addition to their particular portfolio.
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Trend-Following: What's Luck Got To Do With It?
June 2020
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Abstract
Trend-following strategies experienced a wide range of performance outcomes during February and March 2020. In this paper, we illustrate how the precise sequence of events that occurred led to some strategies performing unexpectedly poorly, while others performed unexpectedly well. Our results suggest that should we see a similar situation replay again, where markets fall the same amount in the same period, but along a slightly different path, we would generally expect to see broadly positive performance and a narrower range of outcomes.
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Model Interpretability in Machine Learning
December 2019
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Abstract
Interpretability is an increasingly vital issue in machine learning. Computerized statistical modeling has become the de facto paradigm for quantitative decision-making in any number of fields, including healthcare, advertising, investing, and more. And yet, the relative opacity of many of these techniques can pose a real issue in sensitive applications. Furthermore, the inability to interpret a model's behavior removes an essential part of the feedback loop for the practitioner, who needs to have a good understanding of the model to know when it's bound to fail, or where it can be improved. In this note, we first review the canonical statistical machine learning problem, before describing the issue of model interpretability and some of the recent developments. We list some examples of both interpretable and non-interpretable models and explain some of the differences.
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Trading with an Edge
September 2019
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Abstract
Systematic as well as discretionary trading strategies attempt to forecast future returns to then position themselves in line with anticipated market moves. Intuitively, successful prediction should lead to a profitable trading strategy. By most standard measures, however, it appears that many well-known trading strategies ought not to be successful at all, as their success rate in predicting market moves is relatively low. Or to paraphrase loosely, most strategies, when viewed from a certain angle, are not much better than a random coin toss. This note illustrates why a small edge over a random positioning is all you need.
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Non-Linearity of Portfolio Optimization
May 2019
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Abstract
This note is an in-depth study of constrained mean-variance optimization in the context of combining several systematic trading signals. We analyze whether the solution of such optimization depends linearly on the input variables. The conclusion is the contrary that such portfolio optimization exhibits a multitude of non-linearity. We conclude by discussing implications for investors.
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When Market Neutral Isn't: Alternative Risk Premia and Equity Exposure
March 2019
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Abstract
As part of a quest to improve returns and better diversify portfolios, many investors have explored so-called Alternative Risk Premia strategies. One of the primary selling points of Alternative Risk Premia is the supposed low correlation to traditional portfolio investments since they are "market-neutral". However, as we demonstrate in this short paper, market neutrality does not always insulate a portfolio from traditional sources of risk.
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Equal Risk Contribution Portfolios
March 2019
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Abstract
Decades since the introduction of modern portfolio theory by Harry Markowitz in 1952, portfolio optimization remains an actively studied research problem. There exist any number of schemes for constructing the "optimal" portfolio, which run the gamut from simple rules of thumb to highly technical approaches founded on (e.g.) stochastic control theory. In recent years, a class of purely risk-based allocation programs have become popular. In this note, we review one method in particular: the equal risk contributions (ERC) portfolio. ERC is a robust option not only for building portfolios of assets, but also for combining trading models in a multi-strategy fund. We attempt to elucidate the ERC methodology and give some intuition for its behavior.
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Signal Processing
December 2018
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Abstract
Signal processing is a subject whose importance and potential may sometimes be overlooked. We often hear about advances in familiar domains such as computing, communications, and artificial intelligence, but it is signal processing which lies at the heart of these fields, and which facilitates many other cutting-edge research endeavors and everyday technologies. The objective of this article is to shed light on this discipline by touching upon the historical developments, providing a qualitative overview of the techniques involved, and elaborating on relevant practical applications. The article concludes with a light discussion of the applications of signal processing to systematic trading, where it is uniquely well-suited to the analysis of financial time series.
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The Speed of Trend-Following
March 2018
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Abstract
Trend-following strategies aim to profit from sustained directional moves in markets. A key decision a trend follower needs to make is what "speed" they wish to be - in other words, do they want to capture short, intermediate or long-term trends - and to parameterize their models accordingly. In this paper we present typical formulations of trend-following strategies, and investigate how their speed is set by their parameterization. We also show that despite formulation differences, trend-followers can be very similar at their core, and therefore can be highly correlated. Using two well-publicized CTA indices, we consider what value might be added to a portfolio by pairing a typical trend-following strategy with a source of alpha that aims to capitalize on short-term market behavior.
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Tail Risk Hedging
October 2017
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Abstract
Many investors have significant long equity market exposure and seek effective portfolio protection. Several strategies for tail risk hedging have been proposed to provide downside protection in equity market sell-offs, notably a) increasing fixed income allocation, b) buying protective puts through the sale of out-of-the-money calls (collars), c) hedging using VIX futures, and d) allocating to Managed Futures or other alternative risk premia strategies. In this paper we examine the popular strategies for tail risk hedging and highlight the cost-benefit of each.
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Directional and Cross-Sectional Risk Premia
October 2017
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Abstract
In recent years there has been a proliferation of alternative risk premia strategies taking advantage of well understood empirical market inefficiencies in order to add alternative sources of return to a traditional portfolio of equities and bonds These market inefficiencies are often referred to as factors In this study we examine the effects of trading three generic factors Momentum, Value, and Carry in either a directional or cross sectional portfolio construction framework We demonstrate how the choice of cross sectional or directional factor portfolio construction can affect the portfolio's leverage and transactions costs, market beta risk, and equity tail risk.
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Machine Learning
September 2017
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Abstract
Machine learning is more fashionable than ever for problems in data science, predictive modeling, and quantitative asset management. Developments in the field have revolutionized many aspects of modern life. Nevertheless, it is sometimes difficult to understand where real value ends and speculative hype begins. Here we attempt to demystify the topic. We provide a historical perspective on artificial intelligence and give a light, semi-technical overview of prevailing tools and techniques. We conclude with a brief discussion of the implications for investment management.
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Market Diversification
August 2017
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Abstract
Portfolio diversification is one of the most important and sought-after concepts in investing. A properly diversified portfolio will, on average, produce higher risk-adjusted returns than any single market investment, and has the potential to protect against drawdown exposure. However, due to market correlations, most investors realize a performance plateau, limiting the benefits of expanding a portfolio with additional markets. This market correlation structure can often make it difficult to achieve diversification.
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Equity-Bond Correlation
August 2017
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Abstract
The equity-bond correlation has been negative since the early 2000s. A certain conventional wisdom has developed that this negative correlation is natural and enduring. However, when we take a much longer historical perspective and examine data going back to 1870s, we find that the equity-bond correlation is highly dynamic and has gone through prolonged periods of positive correlation. As such, we should not dogmatically assume that the equity-bond correlation will be negative going forward, especially in the context of asset allocation and portfolio management.
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The Trendiness Of Markets
August 2017
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Abstract
Trend-following strategies seek to profit from sustained directional price moves (trends) in markets. In this analysis, we present the Trend Ratio and the Directional Indicator as means of quantifying the "trendiness" of markets at the individual level and in the aggregate. We observe the trendiness of recent and historical market environments and the impact on trend-following strategies. Market environments with a significant number of trends across a given investment universe often present the best opportunity for trend-following strategies. These opportunities may exist both in times of market duress such as the 2008 financial crisis and during relatively complacent market environments such as 2014.
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Global Macro Market Diversification and CTA Performance
August 2017
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Abstract
Trend-following, a main component of CTA strategies, relies in large part on market diversification to deliver attractive risk-adjusted returns. In this paper we define a quantitative measure of diversification in the global macro space and show evidence that it has explanatory power for CTA performance.
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