April 2020 Kettera Strategies Trend Analysis Report
In the realm of hedge funds, the year 2021 (or the latest available data) saw a significant divergence between discretionary and quantitative global macro programs. This widest dispersion is primarily due to their fundamental differences in strategy and sector performance, particularly across asset classes like government bonds, currencies, and interest rate instruments.
Discretionary macro programs, relying on human judgment, are well-positioned to express nuanced big-picture views across various sectors. For instance, the discretionary macro team at Citadel, with its blend of fundamental analysis and quant models, outperformed by correctly forecasting aggressive rate hikes during 2022's inflation fight[1].
On the other hand, quantitative macro programs employ model-driven, systematic trend-following, and machine learning strategies. While these strategies had strong years like 2022 from classic trend following, their performance can become more "lumpy" or volatile in years without clear trends[4].
Sector Performance
- Government bonds: Discretionary programs' dynamic duration management helped reduce downside risk during sell-offs, preserving capital better than benchmarks[2]. Some global government bond strategies actively adjust duration exposure to maintain negative correlation to equities—a role bonds traditionally held but have struggled with recently.
- Fixed income sectors like CMBS and securitized assets offer idiosyncratic opportunities requiring active management, typically associated with discretionary strategies. Tighter credit conditions and changing economic patterns favour skilled discretionary managers focusing on multi-family residential, industrial/logistics, and selectively navigating retail and hotel sectors[5].
- Currencies and inflation products provide key tactical arenas where discretionary macro teams' interpretation of diverging monetary policies can outperform purely systematic strategies[1].
Performance Across Sectors
- Energy managers, particularly crude oil traders, faced challenges due to an odd storage constraint that caused prices to go negative.
- Short-term programs found profits in gold and crude oil, with most being positive on average.
- Agricultural programs outperformed their metals/energy trading peers.
The Eurekahedge Long Short Equities Hedge Fund Index, Eurekahedge AI Hedge Fund Index, Eurekahedge-Mizuho Multi-Strategy Index, BarclayHedge Currency Traders Index, BTOP FX Traders Index, S&P GSCI Metals & Energy Index, and S&P GSCI Ag Commodities Index are among the indices mentioned in the article.
Equities hedge fund strategies rebounded in April, with all equities style buckets being positive. Event-driven strategies charged back to life after suffering their worst months since inception in March. Most systematic trend strategies, on average, were positive, though not as dramatic as in recent months. Short-term "trend" models were profitable in all sectors except FX.
It's essential to note that the views expressed in this article are those of the author and not necessarily those of AlphaWeek or its publisher, The Sortino Group.
All Rights Reserved.
This article is categorized under Hedge Funds Guest Articles and Hedge Funds - Managed Futures.
[1] Source: AlphaWeek [2] Source: Preqin [4] Source: BarclayHedge [5] Source: eVestment
In the context of hedge fund strategies, data-and-cloud-computing and technology play significant roles in both discretionary and quantitative macro programs. For instance, discretionary macro programs, such as the one at Citadel, leverage technology to enhance their fundamental analysis and quant models, thereby improving their projection accuracy, as seen in predicting aggressive rate hikes for 2022. Meanwhile, quantitative macro programs employ technology and machine learning strategies for systematic trend-following, which can help minimize lumpy or volatile performances in years with unclear trends.
