- CFA Exams
- 2024 Level I
- Topic 1. Quantitative Methods
- Learning Module 4. Probability Trees and Conditional Expectations
- Subject 3. Bayes' Formula and Updating Probability Estimates

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##### Subject 3. Bayes' Formula and Updating Probability Estimates PDF Download

Bayes' formula ties in nicely with the total probability rule. This rule is:

^{C})P(S

^{C})

Thus, with the total probability rule, we calculate an unconditional probability (in this case, the probability of event X) by using the fact that we know conditional probabilities of X given other events (S and S

^{C}) and their respective unconditional probabilities.

Bayes' formula is used when we know that the event whose probability we have just calculated (i.e., event X) has occurred, and we wish to evaluate conditional probabilities based on this fact.

Thus, Bayes' formula allows us to calculate P(S|X) and P(S

^{C}|X), (i.e., conditional probabilities) another way from how they appear in the above formula.

Effectively, we are using Bayes' formula to update our knowledge of a specific event occurring in light of new information received (an event has occurred).

The general formula is:

__Typical exam question__

An analyst has developed a ratio to identify a company's expectation of experiencing declining PE multiples over time. Research shows that 55% of firms with declining PEs have a negative ratio, while only 25% of firms not experiencing a decline in PEs have a negative ratio. The analyst expects that 15% of all publicly traded companies will experience a decline in PE next year. The analyst randomly selects a company and its ratio is negative. Based on Bayes' theorem, compute the probability that the company will experience a PE decline next year.

This question deals with applying Bayes' theorem to determine the probability that a company will experience a decline in PE.

We need to define the notation to begin with.

X

_{1}: PE will decline. P(

_{1}) = 0.15

X

_{2}: PE will not decline. P(X

_{1}) = 0.85

P (B|X

_{1}) = 0.55: ratio is negative when PE declines

P (B|X

_{2}) = 0.25: ratio is negative when PE does not decline

We are looking for the probability that PE will decline given a negative ratio.

Bayes' theorem can be applied as follows:

P (X

_{1}|B) = [P(X

_{1}) x P (B|X

_{1})] / Unconditional probability of the new information

= [P(X

_{1}) x P (B|X

_{1})] / [P(X

_{1}) x P (B| X

_{1}) + P(X

_{2}) x P (B| X

_{2})]

= [0.15 x 0.55] / [(0.15 x 0.55 + 0.85 x 0.25] = 28%

###
**User Contributed Comments**
11

User |
Comment |
---|---|

SamehHassan |
Bayer's Formula to be used only when the event occured or given |

ambar |
Best way to understand Bayes' formula is through tree model. It helped me a lot |

StanleyMo |
here is how i do for last exam: P (X1 / B) = P( B / X1 ) * P ( X1) / P (B) and P (B) = P ( B / X1)*P (X1) + P( B/ X2)* P(X2) Easier to understand. |

fedha |
thanks StanleyMo, much easier to understand |

chris12345 |
do we have pens and papers in the exams for drawing trees. I like climbing trees as well |

Nganeziyamfisa |
all is well. Keep up the good work. God bless us. |

Ed1001 |
this is tough |

challenge10 |
Another way of looking at the previous example: Probability of Experiencing a Decline in PE given a negative ratio = Portion of Total Population with a negative ratio AND decline in PE divided by Total Portion of Population with negative ratios (both with and without decline in PE). ie. P(AB) / P(A|B)P(B) + P(A|Bc)P(Bc) |

TiredHand |
Blimey are they trying to kills us? |

schweitzdm |
What is the new information being referred to in the final example here? |

hibaeldeek |
How I made it easy to myself: Prob demanded; prob(X1/B) =[ p(X1) . P(B/X1) ] / sum ( X1 . Prob (fixed cond/X1.....Xn . Prob(fixed cond/Xn) Fixed condition here is that it's a negative ratio (B) |

I am using your study notes and I know of at least 5 other friends of mine who used it and passed the exam last Dec. Keep up your great work!