Honest and Quantamental Insights for Investment Managers
Philosophy & Process
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- Hand cleansed data
- Atypical data integrity to eliminate errors
- Amplify the efficacy of our fundamental factors
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.
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