EARNEST Partners is a fundamental, bottom-up investment manager. The Firm's investment objective is to outperform the assigned benchmark while seeking to control volatility and risk.
We are fundamental, bottom-up stock selectors. Companies are unique and we consider their differences in selecting what we believe are good investments. The first step in our investment process is to screen the relevant universe to identify stocks that we believe are likely to outperform based on their financial characteristics and the current environment. Using an approach called Return Pattern Recognition®, we seek to identify the financial and market characteristics that have been in place when an individual company has produced outstanding performance. We screen thousands of companies and select for an in-depth fundamental review those exhibiting the set of characteristics that we believe indicate future outperformance.
Companies are unique and we consider
their differences when selecting investments.
The companies identified in our screening process are put through a second more rigorous review, during which we develop an investment thesis for each company. This thesis must be tested. The test generally includes conversations with the company’s management team and industry specialists, review of the company’s financial reports, analysis of industry and company-specific studies, and independent field research. We seek companies in attractive industries with developed strategies, talented and honest management teams, sufficient funding, and strong financial results. The experience and diverse perspectives of our investment team members are an advantage in determining which companies we believe are best positioned to meet our clients’ investment objectives.
The final step in our investment process is to construct a portfolio that includes those stocks that we expect to have the best performance and that combines those stocks in a way that most effectively manages risk. Our clients are primarily concerned about the risk of meaningfully underperforming the assigned benchmark. Hence, we focus our attention on reducing this possibility. We use a statistical approach called downside deviation to measure and then constrain the likelihood of significantly underperforming the assigned benchmark.