Philosophy

IncomeNonStop, we strongly believe in the value of applying empirical, fundamental research to uncover the best quantitative stock selection strategies. We have done an exhaustive study of the historical characteristics that the best value and growth stocks possess, and this research forms the basis of every portfolio we offer. Our strategies select stocks in a logical, unemotional way, and they appeal to common sense: we look for stocks selling at a discount but showing good potential for growth.

We do not seek to deviate from our strategies, and we adhere to a disciplined systematic process. We believe that many money managers under-perform their benchmarks because of their inherent inability to divorce themselves from the emotions that often cloud good decision-making. Our process is consistent and rational; we do not let short term market fluctuations distract us from our longer-term goals. We allow our stock selection screens to add value over full market cycles and we generally stay fully invested in the market. And, unlike most other quantitative managers, our process is transparent.


Key Research

Value consistently outperforms growth
Several value factors significantly outperform the market over time. Our strategies emphasize a low price-to-sales ratio because of its long-term performance and consistency. We also believe that the price-to-sales ratio is more reliable than other value factors because it is a less manipulated metric.


Growth still has a role

Growth for growth's sake does not beat "the market" over time. However, there is still "value" in growth. Relative strength, or price momentum, is the growth factor we prefer. An object in motion tends to stay in motion-for a time.


Multi-factor models work best

Even stronger results in our strategies comes from models that leverage the best of value and growth combined. Historically, the effects of doing so are compounded, not averaged. The resulting portfolios have historically superior performance with better risk characteristics than single-factor models.