High Impact Insights for
Philosophy & Process
We strive to provide the most rigorous, credible, and easy-to-use investor insights into market opportunities based on empirical research. With 70 years of combined industry experience and over a decade working together, the research team brings a highly complementary mix of asset management and academic pedigree to our service offering.
Our products are for investors seeking data-driven conclusions based on the belief that history rhymes. We believe our team brings unusual statistical depth to bear when identifying these pricing errors and communicates them in a professional, simple and compelling manner. We do not make forecasts that are susceptible to typical human emotions of fear and greed.
Instead, we prefer to source, organize and deliver data-driven conclusions. We promise we’ll never promise to tell you “what the next Amazon” will be. Just the truth as history tells it.
Unlike traditional quantitative models which often begin and end in either value or momentum, the Kailash process works backward, assessing quality across dozens of metrics and then favoring shares whose quality may be mispriced. We believe this approach closely mirrors the process pursued by most fundamental investors who seek to arbitrage their incremental units of knowledge from their detailed study of fundamentals against the publicly recognized valuation and price.
Unlike many competitors, our products put principles before personalities. Our team will not publish something they do not agree on. You will notice an awful lot of “WE” in our writing and a dearth of “ME.” We are not a franchise built on “a person.” Instead, we are a team with a ferocious commitment to studying history and using data to dive down to empirical rather than emotional conclusions.
The process involves numerous orthogonal factors divided into five common-sense categories:
Balance sheet quality, earnings quality, valuation quality, analyst quality, and market quality.
Verify and validate the underlying data
Assess and compare each stock using the factors known for that stock
Dynamically compare the qualities of each company using sophisticated algorithms
Arrive at a relative ranking of aggregate attractiveness for each stock in the universe
Most attractive stocks are a the top and least attractive stocks are at the bottom
*Important note: Users should be mindful that much of the process’s strength stems from the dynamic interaction of the fundamental variables