The Bayesian Method of Financial Forecasting
https://www.investopedia.com/articles/financial-theory/09/bayesian-methods-financial-modeling.asp
The article starts of explaining the Bayesian Method and more specifically Bayes’ Theorem. From class we understand that Bayes’ Theorem is most often used to calculate posterior probability, the conditional probability of a future uncertain event that is based upon relevant evidence relating to it historically.
An example the article gives about how Bayes’ Theorem can be used in financial forecasting is how a change in interest rates would affect the value of a stock market index. Once again the key component of Bayes’ Theorem is using historical data as the backbone of your calculations.
In the table above we can use Bayes’ Theorem:
P(SI) = the probability of the stock index increasing
P(SD) = the probability of the stock index decreasing
P(ID) = the probability of interest rates decreasing
P(II) = the probability of interest rates increasing
Using Bayes’ Equation we know that P(SD|II)=95%. This means that after taking into account the probability of the effects of interest rates rising, we must update the probability of the stock market decreasing from 57.5% to 95%. Therefore, 95% is the posterior probability.
Bayes’ Theorem can be used in any scale depending on the amount of historical data is given for the calculations of the numbers. This example can be extrapolated to individual companies by using changes within their own balance sheets, bonds given changes in credit rating, and many other examples.