Euclidean Technologies Launches Euclidean Fundamental Value ETF, Incorporating Machine Learning for Stock Selection

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Euclidean Technologies, a Seattle-based firm specializing in machine learning-driven stock strategies, is expanding its services to retail investors. Originally founded in 2008 as a manager of private hedge funds for accredited or high net worth investors, Euclidean now plans to convert its hedge funds into an exchange-traded fund (ETF) structure. This move aims to provide a broader range of investors with access to its machine learning technology, which identifies undervalued U.S. stocks. With the launch of the Euclidean Fundamental Value ETF on the New York Stock Exchange, Euclidean is poised to transition from a boutique asset manager to a firm that caters to a wider audience of investors.

Machine Learning and Euclidean's Approach:

While using advanced mathematical and computational techniques to select stocks is not new, Euclidean's utilization of machine learning sets it apart. Machine learning has the ability to uncover complex, non-linear relationships in high-dimensional data, according to John Alberg, co-founder and managing partner at Euclidean. Through extensive research, Euclidean has demonstrated that applying machine learning to identify companies as sound long-term investments can outperform traditional quantitative approaches to equity investing.

Euclidean's Successful Application of Machine Learning:

Euclidean's journey into machine learning-based investing began several years ago when it started exploring "sequence-to-sequence" learning—a technique commonly used in natural language processing tools such as ChatGPT—for quantitative investing. In March 2020, Euclidean deployed its model, and since then, its core fund has consistently outperformed S&P 500 total returns by approximately 3%. This success showcases the potential of machine learning in enhancing investment strategies.

Harnessing Language Models for Analysis:

Continuing its commitment to leveraging cutting-edge technology, Euclidean is currently researching the application of machine learning-based language models for rapid analysis of written or spoken language related to company performance. This includes extracting insights from SEC filings, earnings transcripts, news articles, analyst reports, and investor presentations. By utilizing these language models, Euclidean aims to gain a deeper understanding of companies and improve investment decision-making.

Expanding the Team and Euclidean's Background:

As Euclidean seeks to strengthen its position in the market, the firm plans to expand its workforce beyond its small Seattle-based team. Co-founders John Alberg and Michael Seckler have a successful track record, having previously founded an HR software company called Employease, which was acquired by ADP in 2006. Alberg acknowledges the profound influence of his late father, Tom Alberg, co-founder of Madrona Venture Group and a prominent figure in the Seattle tech community. Reflecting on his father's impact, Alberg expresses deep gratitude and acknowledges the significant role his father played in shaping his life and career.

Conclusion:

Euclidean Technologies' decision to convert its private hedge funds into an ETF structure marks a significant milestone for the firm. By integrating machine learning techniques into their investment strategies, Euclidean has achieved consistent outperformance compared to traditional quantitative approaches. With the launch of the Euclidean Fundamental Value ETF, Euclidean aims to extend its services to a wider range of investors, providing them with access to its advanced machine learning technology. As Euclidean continues to explore the use of language models for analyzing company performance, it is poised to shape the future of quantitative investing.