By Reshma Krishnan
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The fewer the assumptions, the stronger the forecast – at least in the beginning when you are learning how to model. Most investment Banking models end up running into 40 assumption sheets, each linked to another. While you might believe such minutiae makes a difference, it’s almost always just to make yourself feel better. Yes, your ability to understand every cost element is good, but its futile if your understanding of the industry works or its cost structure is weak. Key assumptions built into the forecast can also be lost, like trees in a forest. Links can be very hard to find. A simple forecast on the other hand helps you understand what drives basic line items while giving you the ability change basic assumptions. So for instance if you are forecasting the cost of a cup of tea, you break the cup of tea into its major elements, milk, tea, sugar. Three basic drivers, but if you decide to link the price of tea not to the retail rate but to an auction rate that is further linked to an auction house pricing, there are many chances your model will be faulty for no tangible benefit.
Forecasting is hard- if it wasn’t, financial modeling and forecasting would not be the number one skill required in financial services, especially Equity Research, or the most popular program in Financial Services Education. It requires patience and a deep thorough understanding of the industry. Forecasting is what Equity Research Analysts do all the time which is why Equity Research Analysts are industry specialists. You won’t find an analyst doing both steel and retail e-commerce. If you are not detail oriented, you are not going to be great at forecasting.
Your forecast is as good as your data, or your weakest link- using solid numbers always feels like an attractive proposition. Investment Bankers love to receive solid data from the clients. Equity Research analysts love to receive solid numbers from the industry or a company but what data do you trust. How often do you use that data? Can you remove the bias in the data. Data you receive from clients will almost always be optimistic, same with industry. Data you receive from Private Equity will almost always be pessimistic. There is bias in every data and your job is to remove bias.
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