Ai For Alpha, a pioneering fintech specialized in AI investment strategies, announces the launch of its latest innovation: the “Risk Off” CTA strategy.
Ai For Alpha has developed a cutting-edge technology called Decoding, which enables the creation of replication portfolios for both traditional and alternative strategies, including CTAs, Global Macro, and hedge funds global benchmarks. These portfolio models are implemented by major money managers and banks’ QIS desks to create market access investment strategies.
The Ai For Alpha “Risk Off” CTA strategy utilizes the Decoding technology to replicate a CTA managers benchmark while dynamically adjusting the allocation to reduce the exposure to “risk on” assets, resulting in a more defensive profile.
Context:
In recent years, particularly within the context of rising inflation, the correlation between equities and bonds has become increasingly unstable, challenging the diversification profile of traditional CTA strategies. In response, Ai For Alpha developed the “Risk Off” CTA strategy, which leverages its advanced Decoding technology to dynamically allocate to “safe assets” within a trend-following replication portfolio.
The “Risk Off” CTA strategy builds upon the foundation of the Ai For Alpha CTA replication model, renowned for inferring allocations from top CTA managers across a broad investment universe, including equity indices, bonds, interest rates, commodities, and currencies.
To further enhance the defensive bias of this allocation, Ai For Alpha introduced a risk-off filter within the replication process. This filter employs a non-linear hysteresis algorithm, inspired by principles from physics, to robustly classify assets into “risk on” and “risk off” categories. The filter is based on the rolling two-year correlation of each trend factor to a basket of global equity indices, ensuring stability and precision in asset selection.
“This new replication strategy combines Ai For Alpha’s recognized expertise in AI-powered investment replications with a portfolio adjustment that tilts towards a more defensive allocation,” says Béatrice Guez, CEO of Ai For Alpha.
An Enhanced Defensive Profile:
The “Risk Off” CTA strategy offers a significant shift in the CTA return profile, prioritizing trend positions with negative correlations to equities. While this approach may give up some risk-adjusted returns compared to traditional CTA strategies, it delivers a notably higher defensive profile during market downturns.
“The CTA replication can be classified as a proxy hedge and allows for better risk management in our clients’ portfolio construction,” adds Thomas Jacquot, Chief Revenue Officer. “It permits exposure to CTA strategies with some negative correlation to the equity market, showing a -35% historical correlation to US Equites versus -3% for traditional CTA Trend benchmark.”
Key Performance Highlights:
- Reduced Correlation with Equities: -35% correlation to the US Equities.
- Asymmetric Returns: The “Risk Off” CTA strategy enhances CTA diversification by providing an asymmetric return profile, offering investors a reliable proxy hedge against equity market corrections.
- Strong Downturn Performance: The strategy is particularly effective in downturn markets, with the average monthly performance significantly improving during periods of equity market decline.
Guez summarizes, “The Risk Off CTA strategy delivers substantially higher returns in severe downturns, with an average monthly performance of 3.7% when US Equities declines by more than 6%.”
Statistics (Dec 2004 – Sept 2024):
- Annual Total Return: 8.1%
- Volatility: 9.1%
- Max Drawdown: 15.7%
- Sharpe Ratio: 0.65
- Return/Max DD: 0.51
About Ai For Alpha:
Ai For Alpha is a leading provider of generative AI and AI-powered investment models to large financial institutions across Europe, the Americas, and Asia. The company has received numerous awards for its achievements in AI applied to finance, including the EIT’s European Digital Label for Innovation and the Women TechEU 2023 award. Ai For Alpha’s publications rank in the top 1% of most-read articles on SSRN, the world’s largest repository for research in social sciences.
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