Aethon Fund has launched with $50m in initial capital, and is seeking to attract institutional investors with a systematic investment strategy that combines proprietary market signals with artificial intelligence-driven trade execution.
The New York-based hedge fund was founded by George Kailas, who said the strategy is built on more than two decades of research into identifying market signals that traditional quantitative models often fail to capture.
The launch capital includes an anchor allocation through a separately managed account from a fund of funds, alongside commitments from institutional investors and ultra-high-net-worth individuals.
Rather than operating as a conventional quantitative fund, Aethon says its investment process centres on a proprietary library of market signals that are combined with AI-powered portfolio management and execution.
The fund employs multiple systematic strategies across long, short, momentum, mean-reversion and accumulation styles, dynamically allocating capital towards those performing best under prevailing market conditions. Position sizing and risk management are governed by predefined rules, including profit targets, stop losses and time-based exits, while an overlay adjusts net market exposure according to changing volatility regimes.
Kailas said the fund’s competitive advantage lies in the quality of its proprietary signals rather than the scale of its data collection.
The signal library has been developed using market intelligence gathered through Prospero.ai, the retail investor analytics platform Kailas founded in 2019. According to the firm, the platform has built its datasets through interactions with more than 200,000 retail investors and now serves over 20,000 monthly active users.
As part of the launch, Kailas will transition from leading Prospero.ai’s day-to-day operations to become chairman, allowing him to focus on managing Aethon. Prospero.ai’s chief technology officer, Adam Plante, has been appointed chief executive, while Aethon said a portion of the hedge fund’s profits will be directed back to the retail platform.
The firm’s proprietary toolkit includes quantitative signals covering more than 2,000 equities, sentiment analysis across social media platforms, analyst-rating models that weight forecasts by historical accuracy, and predictive models designed to estimate return probabilities across stocks and exchange-traded funds.
In an unusual move for the hedge fund industry, Aethon said it intends to publicly demonstrate aspects of its signal technology by publishing examples showing how selected signals are generated for individual stocks, while retaining its proprietary investment process.
The firm has also assembled an executive team drawn from quantitative trading, artificial intelligence and financial technology backgrounds.
Chief technology officer Dave Lauer previously worked as a high-frequency trader at Citadel and Allston Trading and has advised US regulators on market structure. Head of AI Ezi Ozoani joins from Hugging Face, while head of trading Joe Bernstein previously spent eight years developing systematic trading strategies at Tower Research Capital.
The leadership team also includes Erik Smolinski as head of risk management, Dave West as head of infrastructure and Dr Matt Carter as head of communications.
The launch comes as institutional investors continue to increase allocations to systematic and AI-enabled investment strategies, with hedge fund managers increasingly incorporating machine learning tools into portfolio construction, signal generation and execution while retaining human oversight of research and risk management.
