Strategy
US Growth Equities
The Driehaus US Growth Equities team focuses on investing in US-listed equities of public companies with market capitalizations between $100 million and $15 billion, through the Driehaus Micro Cap Growth, Driehaus Small Cap Growth, Driehaus Small/Mid Cap Growth and Driehaus Life Sciences strategies. The strategies provide investors with high active share portfolios of companies experiencing positive fundamental changes in addition to exposure to positive growth inflections, earnings surprises and earnings revisions, factors that are positively correlated to alpha generation. The team is led by Jeff James who began his portfolio management career at Driehaus in 1998. He is supported by seven analysts that combined have over 50 years of Driehaus research experience.
Institutional Investors
Our institutional sales team will be pleased to address questions and requests related to separately managed accounts and/or institutional commingled vehicles that employ our investment strategies.
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Driehaus Micro Cap Growth Strategy
View DetailsDriehaus Micro Cap Growth Strategy
The strategy seeks to outperform the Russell Microcap® Growth Index over full market cycles.
Facts
- Strategy Inception Date
- Jan 01, 1996
- Total Strategy Assets
As of Feb 28, 2023
- $1,741M
Separately Managed Accounts
- Open to New Investors
- No
Funds
Driehaus Small Cap Growth Strategy
View DetailsDriehaus Small Cap Growth Strategy
The strategy seeks to outperform the Russell 2000® Growth Index over full market cycles.
Facts
- Strategy Inception Date
- Jan 01, 1980
- Total Strategy Assets
As of Feb 28, 2023
- $2,462M
Separately Managed Accounts
- Open to New Investors
- Yes
Funds
Driehaus Small/Mid Cap Growth Strategy
View DetailsDriehaus Small/Mid Cap Growth Strategy
The strategy seeks to outperform the Russell 2500® Growth Index over full market cycles.
Facts
- Strategy Inception Date
- Feb 01, 2012
- Total Strategy Assets
As of Feb 28, 2023
- $1,245M
Separately Managed Accounts
- Open to New Investors
- Yes
Funds
Driehaus Life Sciences Strategy
View DetailsDriehaus Life Sciences Strategy
The strategy intends to exploit the inefficiencies in how markets assign risk to development-stage and early-commercial stage healthcare companies.
Facts
- Strategy Inception Date
- Feb 01, 2019
- Total Strategy Assets
As of Feb 28, 2023
- $234M
Separately Managed Accounts
- Open to New Investors
- Yes
Philosophy & Process
The team employs a growth-oriented investment philosophy focusing on identifying company-specific growth inflection points and exploiting associated marketplace inefficiencies. Core to the philosophy are the beliefs that: earnings are the primary driver of equity prices over time, market expectations tend to be ‘anchored’ to historical information and points of inflection therefore introduce dislocations between market expectations and fundamentals which generate significant alpha capture opportunities. The team combines fundamental, macro/sector/industry and behavioral analysis in its investment process together with a nimble/active investment approach to quickly identify inefficiencies and generate a portfolio, which uniquely seeks to achieve superior aggregate growth rates as well as superior risk characteristics.
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What's Up In Private Markets?
The IPO window has been mostly closed, but there is a huge amount of capital available in private markets; does this rhyme with how the tech unicorns of the aughts came about? Read more on this in the Driehaus Life Sciences team's latest one-pager, "What's Up In Private Markets?".
The Rise of Bots - Evolution of OpenAI and ChatGPT
OpenAI is an artificial intelligence research organization founded in 2015 by a group of tech luminaries, including Elon Musk. The organization's mission is to develop AI technologies in a way that is safe and beneficial for humanity. One of OpenAI's key areas of focus is deep learning, a subset of machine learning that uses artificial neural networks to simulate the human brain's ability to learn from experience. OpenAI has made significant contributions to the development of deep learning algorithms, including the creation of the GPT (Generative Pre-trained Transformer) language models, which can generate coherent and contextually relevant text based on a given input.
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