Strategic Heat Map by Kettera - March Edition, 2020
In March 2025, the world of finance witnessed a mix of results for AI and machine learning (ML)-based strategies. Despite the growing adoption and success of these strategies, the impact of an unprecedented event during the month led to varying performances.
AI and ML strategies have been making significant strides, streamlining decision-making and responding effectively to dynamic conditions. Machine learning, in particular, has shown around 62% accuracy in predicting complex outcomes such as stock market highs and lows. This accuracy has been bolstered by the increasing integration of these technologies across various business areas, from analytics and marketing to sales and security.
However, the march towards AI advancement has also necessitated a rise in computational resources. Models have become more powerful, but this power comes with a higher energy consumption, necessitating improvements in hardware efficiency.
In March, AI and ML systems faced sudden changes in operational environments. While these systems are designed to respond to dynamic conditions, the unprecedented event of the month strained their predictive accuracy in some cases.
Equities strategies performed poorly in March, with most returns being worse than February. The Eurekahedge AI Hedge Fund Index, Eurekahedge Long Short Equities Hedge Fund Index, and a blend of the BarclayHedge Equity Market Neutral Index with Eurekahedge Equity Mkt Neutral Index were among those that struggled.
On the other hand, some discretionary global macro strategies redeemed themselves in March. Profits were accelerated by fundamental factors that began building in late January. The more successful global macro programs were short base metals and energy, long US fixed income, and some caught the "up-down" US dollar moves. The Eurekahedge-Mizuho Multi-Strategy Index also showed positive results.
In the realm of quant strategies, results were mixed, with single-digit gains or losses on the month. The CBOE Eurekahedge Relative Value Volatility Hedge Fund Index, for instance, posted modest returns.
Short-term and higher-frequency programs generated mixed to net positive returns in March, with the markets favoring higher frequency players. Systematic trend programs also had mixed results, with positive returns mostly in the fixed income, short term rates, and energy sectors.
In the agricultural commodities market, discretionary agricultural traders, particularly those specializing in relative value and spread trading, benefitted from demand destruction and short positioning in corn. Discretionary commodities managers focusing on energy and other industrial commodities, primarily base metals, excelled in March by correctly predicting the collapse.
The S&P GSCI Metals & Energy Index and S&P GSCI Ag Commodities Index, as well as the BarclayHedge Currency Traders Index and BTOP FX Traders Index, were among the indices affected by these market fluctuations.
While AI and ML strategies have demonstrated robust performance trends, the performance in March serves as a reminder that these systems, like any other, can be challenged by unprecedented events. The views expressed in this article are the author's and not attributed to any of the mentioned indices.
Sports strategies could benefit from the advancements in AI and ML, as these technologies have shown effectiveness in predicting complex outcomes and have been integrated across various business areas. However, the unexpected events in March 2025 tested the limits of these systems' predictive accuracy, demonstrating that while AI and ML can streamline decision-making, they may be susceptible to unprecedented events, much like sports teams face unforeseen challenges in their competition.
