The only way we can tell whether a kid is tall or short is by comparing him to other kids his age. Our software devices the ratings by doing extensive peer comparisons. So if a software company’s Net Profit Margin is 15%, it will get a bad rating on this parameter, say 30 out of 100 because normal figure for software companies is 25%. However if a textile company has an NPM of 15%, it will get a very high rating, say 75 or 80 because this is a lot higher than what textile companies usually get.
We believe it is a lot more rational to use these ratings rather than raw figures. For example, instead of a thumb rule like “Debt-Equity Ratio should be less than 0.5”, we would prefer to use a thumb rule “Debt-Equity Ratio Rating should be greater than 70”. This is because the former rule will let in a lot of high debt software companies and will drop a lot of low debt oil exploration companies. The latter statement however is completely sector neutral. It will always get you a company with lower debt than most of its peers.
While assigning a rating, our methods check how current figures fare against the historical lows and highs. This gives another dimension to the peer comparisons and enables us to come up with more accurate ratings.
For example, in 2009 and 2013 when whole market was down, there was a lot of good buys available and our method predicted them. However in 2008, when market was at its highs, our method didn’t give high ratings to any company for about 6 months. Because of these comparisons, our ratings have a high probability of helping the user avoid overheated companies and buy oversold companies, considering the market conditions.