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### Can Artificial Intelligence and a Supercomputer Outperform the Markets? Exploring Hedge Funds’ Pursuit of Financial Dominance

Castle Ridge is tiny and based in a hedge fund backwater. But Adrian de Valois Franklin is betting …

In a skyscraper located in downtown Toronto, just a block away from the Hockey Hall of Fame, a small hedge fund is aiming to gain a competitive advantage in financial markets. Castle Ridge Asset Management is placing its bets on Wallace, a specialized supercomputer designed to drive the hedge fund’s trading strategies through artificial intelligence.

Traditionally, players in the hedge fund industry have pondered the potential of AI in outperforming the market. However, previous AI-driven trading initiatives often fell short of expectations, serving more as marketing ploys to attract client funds. The introduction of ChatGPT in November 2022 has breathed new life into this emerging cohort of AI-driven hedge fund participants.

Despite its modest size, with approximately $190 million in assets under management, Castle Ridge, established by current CEO Adrian de Valois Franklin in 2015, aspires to carve a niche for itself in the vast multi-trillion-dollar hedge fund landscape. Franklin is confident that the fund’s AI-based approach to forecasting market movements could position it as a significant player in the industry.

Franklin, a former investment banker with limited experience in quantitative trading, asserts that Wallace sets itself apart from other AI-powered hedge funds by continuously enhancing its models using evolutionary processes akin to selective breeding. Describing Wallace as a virtual multi-manager hedge fund where simulated portfolio managers vie for supremacy in the current market environment, Franklin highlights the supercomputer’s non-stop operation and independence from rest or motivation.

In essence, Wallace’s evolutionary mechanism involves the creation of numerous uniquely-weighted virtual investment portfolios daily, which are evaluated and ranked based on their suitability to prevailing market conditions. Franklin explains that Wallace selects the top-performing portfolios in an eight-hour cycle, granting them precedence for further development through a breeding process.

Castle Ridge has demonstrated some success in delivering investment returns. Since Wallace’s inception in 2017, the fund has achieved annualized net returns of 12.4%, surpassing the S&P 500’s 12.1% returns over the same period, as reported by MarketWatch. This performance is notable considering the competition from well-funded quantitative hedge funds like Two Sigma and D.E. Shaw, which are delving into machine learning and AI technologies.

According to Castle Ridge’s chief scientific officer, Alex Bogdan, Wallace’s evolutionary strategy offers a deeper level of comprehension compared to neural networks utilized by systems such as ChatGPT, which mimic human brain responses. Bogdan envisions these evolutionary processes as the future of AI, enabling machines to transcend mere imitation and progressively enhance their cognitive capabilities.

The roots of AI research trace back to the mid-20th century, spurred by advancements in computer science during World War II. Notably, British scientist Donald Michie’s experiment in 1961 led to the creation of the Matchbox Educable Nought and Crosses Engine (MENACE), a machine that mastered the game of tic-tac-toe using matchboxes and beads to strategize and learn from gameplay outcomes.

While Michie’s machine tackled a simple game, Wallace operates in the dynamic realm of financial markets, necessitating constant adaptation to evolving conditions. Castle Ridge’s success hinges on Wallace’s agility in responding to market dynamics, enabling it to predict market events ahead of official disclosures by analyzing data signals.

In conclusion, Castle Ridge’s innovative AI system, Wallace, represents a paradigm shift in the hedge fund landscape, leveraging evolutionary computing to navigate the complexities of financial markets and anticipate market movements with precision.

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Last modified: January 18, 2024
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