|
Posted on April 3, 2013 @ 06:47:00 AM by Paul Meagher
In Bayesian Angel Investing, you calculate the prior and posterior probability of an investment outcome to arrive a good decisions regarding those investments.
Let us see how it might work in the context of making a decision to invest in a startup company.
When an investor encounters an opportunity to invest in a startup company their goal is likely not to make an investment decision right away, but rather a decision on whether it is worth allocating time to pursue the opportunity further.
So, if a proposal meets the investor's checklist of positive attributes:
+ good management
+ good idea
+ good business plan
+ good deal
This might get the Bayesian Investor sufficiently motivated to start calculating the prior probability that the startup company might be worth investing in.
So if you assign a prior probability of 60% that the company might be worth investing in, you will need more information to move the probability upwards in order to finalize any deal.
You will want to meet via email, phone, and possibly in person to further discuss the proposal.
A Bayesian Investor can move towards a final decision by setting a decision making threshold of, say, 80% on the prior probability estimate (e.g., that the company will be successful S or not ~S). If the prior probability estimate of the startup being successful reaches or exceeds 80%, then invest in the company. If further information causes the prior probability to go below 50%, then don't invest. Prior estimates beget posterior estimates which become the priors in the next round of due diligence.
The way a Bayesian Investor moves towards making an investment decision is by gathering more information about the company. The information that is gathered should be diagnostic of whether the company is likely to succeed. Similar to the way a medical doctor orders test to either confirm or dis-confirm an hypothesis related to the prior hypothesis (e.g., diagnostic possibilities - has cancer, does not have cancer).
We will try to formalize Bayesian investing more in a later blog post using this formula, p(H|E) = p(H∩E) / p(E), as our starting point (where H stands for Hypothesis and E for Evidence).
|
Sports Skate Boarding Entertainment and Innovative Concept
