22 North

22 North

Objective

Driven by machine learning and using factors identified by portfolio managers, 22 North seeks to generate persistent, uncorrelated alpha for investors in the strategy.

Overview

22 North uses machine learning for predictive purposes. Algorithms look for patterns in macro, fundamental, technical, sentiment, and factors data among various other datasets. The process is marked by large datasets, nimbleness, rules-based decisions, and human oversight. Portfolio managers and machine rules constantly evolve and adapt to the market feedback loop, with portfolio construction incorporating risk factors and volatility of machine learning outputs.

Portfolio Managers

Characteristics

Number of Positions: 20-40

Maximum Position Size: 10% at time of purchase

Universe: Russell 3000 (with market cap filter >$750m)

Maximum International Exposure: N/A

Market Cap Restrictions: > $750m market cap

Benchmark: Russell 3000 Total return Index