ONEIL GLOBAL ADVISORS, INC.

O'Neil Global Advisors Incorporated (OGA) is a global investment advisor founded in 2019. It is a wholly-owned subsidiary of O'Neil Capital Management Inc. OGA implements strategies that include both technical analysis strategies implemented by portfolio managers and quantitative systematic strategies developed by OGA’s quantitative strategies team. Both strategies utilize the O’Neil Methodology, which is based on technical analysis and a unique stock-picking process. OGA focuses primarily on equity investments in the US and other developed and emerging markets. It serves as the investment advisor, with discretionary trading authority, to pooled investment vehicles (Funds) and separately managed accounts (SMAs). OGA's services are tailored to the investment objectives and guidelines of Clients through a process described in Item 8.

OGA's investment strategies include both technical analysis strategies implemented by portfolio managers and quantitative systematic strategies developed by OGA's quantitative strategies team. Many of OGA's strategies seek to exploit the behavioral biases of discretionary managers, information asymmetries that arise from more effective processing of information, or inefficiencies during changes in market regimes. OGA believes that its multi-strategy approach has advantages in terms of diversification, scale, and allows it to grow through competitive selection. OGA’s algorithmic (“Algo”) strategies are generally more diverse and specialized than its individually managed SMA portfolios with the goal of creating multiple uncorrelated return streams. The Algo strategies invest in the US and other developed and emerging equity markets. The Algo strategies typically hold long positions in equities and broad market ETFs, however some Algo strategies take both long and short positions in US equities and/or broad market ETFs. Algo strategies are typically built around one or more factors discovered by OGA’s quantitative research program and investment models are generally derived through quantitative methods such as machine learning.

(Subscription Required)

(Subscription Required)