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Ex-post is a Latin term which, literally translated, means "after the fact." The opposite of ex-post is ex-ante, which, conversely, means "before the fact." Frequently, these terms are used to describe the method and timing of the collection of financial data. When data is obtained and analyzed in ex-post fashion. it is typically used to predict future earnings and losses.
The purpose of collecting and analyzing financial data is to establish, predict, and influence market trends. Collecting ex-post data on either a company's or an individual's gains and losses allows that information to be arranged in order to create a roadmap for anticipating future trends. This is typically displayed in graph format for ease of comprehension.
For example, if it is established through analysis of this type of data that a company traditionally tends to do poorly in the first quarter of each fiscal year, but it bounces back by the third, that information can be used to allay investor concerns about underperformance. Knowing these trends can help keep stock prices stable after a disappointing first quarter. Conversely, if the same company posts record profits in the first quarter, knowing the implications of the available ex-post data will allow investors to predict that the company is about to have an outstanding year in sales.
It is important to understand, though, that while such data can be useful, it provides no guarantees that a particular trend will continue indefinitely. At best, the collection and utilization of ex-post data is used to formulate educated guesses as to the future performance of a business or an industry. The data is not a guarantee of anything. In analogous terms, a financial professional using ex-post data is a bit like a meteorologist forecasting the weather from satellite and radar information. They are often correct, but mistakes are not only possible, they are expected from time to time.
Keeping all of this in mind puts ex-post data in its proper place. Although it is indispensable in a financial analysis's toolbox, it is not a perfect tool. In general, the more volatile the market — that is, the higher the level of fluctuation and swings in highs and lows compared to typical trends — the less useful this type of data becomes as a tool to predict future behavior. Sometimes the market diverges from its traditional trends, and the validity of the previously-collected data becomes irrelevant in light of current conditions.
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