Despite this we have only models for less than 20% of the listed companies in India. There are still many industries that are not covered even now and even many companies in the sectors that we have solved. This just goes on to show how large and vast the Indian equity markets are. Every few months new IPOs of very interesting companies come up. We are making it a habit of putting out models for IPO companies before the IPO – to allow investors to make their own unbiased decisions (after all one has to invest looking in mind the future and not the past performance as given in the prospectus). – This means the task is only getting tougher and bigger for us. But spare a thought for investors, who need to know, remember and process this ever increasing information pool.
Udit Garg
Published on 13/01/2017 12:00 AM
Financial models are tools used by analysts to estimate the future financial performance of any financial asset – be it equities, bonds, or complex derivatives and securities. Just like in fashion industry where a model is used to show the look of a fashion garment and value it, in finance, they are used to see how the cash flows of the asset will look like and then value them. In essence a financial model shows the analyst the future cash flows/profits etc of any financial asset.
Continuing from our previous post on Financial models… In this post we talk about the other two components of Financial models. The reason we have separated these posts is to differentiate the “Science” from the “Art”. We believe that Historical data and the Business Model represent the science of investing. They are quantifiable and objective in nature. Whereas the Assumptions for future drivers and valuations are more of Art. Future assumptions are always subjective in nature and can be debatable – perception for the future outlook of businesses can vary between investors, management and other stakeholders. Similarly valuation is a fluidic topic and is a subject of great debate and argument between investors.
In a previous post we talked about our handmade(!) financial models. But just because we have put in the effort to create these models, doesn’t mean we stop there. To our mind the models are only the starting point. We are building a large set of tools that will help users to make better sense of the data by Comparing it across dimensions such as companies, users, time, industries, geographies and more, Valuing it better using not only standard benchmarks, but also custom made ones, Estimates of market expectations at overall as well as granular level (think – volume growth expectations). Better trend analysis of expectations and actual performance – both markets and own … and as our product matures, there will be a lot more.
