New York Investment Network


Recent Blog


Pitching Help Desk


Testimonials

"I made several great connections through your network. In fact, I was able to over fund my project. I also listed with another network that cost 3X as much and the leads were nowhere near as solid as the investors I met through this network. I will definitely only be using this network in the future. "
Jason A.

 BLOG >> Recent

Introduction to Bayesian Angel Investing [Bayesian Inference
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).

Permalink 

 Archive 
 

Archive


 October 2019 [1]
 September 2019 [1]
 July 2019 [1]
 June 2019 [2]
 May 2019 [2]
 April 2019 [5]
 March 2019 [4]
 February 2019 [3]
 January 2019 [3]
 December 2018 [4]
 November 2018 [2]
 September 2018 [2]
 August 2018 [1]
 July 2018 [1]
 June 2018 [1]
 May 2018 [5]
 April 2018 [4]
 March 2018 [2]
 February 2018 [4]
 January 2018 [4]
 December 2017 [2]
 November 2017 [6]
 October 2017 [6]
 September 2017 [6]
 August 2017 [2]
 July 2017 [2]
 June 2017 [5]
 May 2017 [7]
 April 2017 [6]
 March 2017 [8]
 February 2017 [7]
 January 2017 [9]
 December 2016 [7]
 November 2016 [7]
 October 2016 [5]
 September 2016 [5]
 August 2016 [4]
 July 2016 [6]
 June 2016 [5]
 May 2016 [10]
 April 2016 [12]
 March 2016 [10]
 February 2016 [11]
 January 2016 [12]
 December 2015 [6]
 November 2015 [8]
 October 2015 [12]
 September 2015 [10]
 August 2015 [14]
 July 2015 [9]
 June 2015 [9]
 May 2015 [10]
 April 2015 [10]
 March 2015 [9]
 February 2015 [8]
 January 2015 [5]
 December 2014 [11]
 November 2014 [10]
 October 2014 [10]
 September 2014 [8]
 August 2014 [7]
 July 2014 [6]
 June 2014 [7]
 May 2014 [6]
 April 2014 [3]
 March 2014 [8]
 February 2014 [6]
 January 2014 [5]
 December 2013 [5]
 November 2013 [3]
 October 2013 [4]
 September 2013 [11]
 August 2013 [4]
 July 2013 [8]
 June 2013 [10]
 May 2013 [14]
 April 2013 [12]
 March 2013 [11]
 February 2013 [19]
 January 2013 [20]
 December 2012 [5]
 November 2012 [1]
 October 2012 [3]
 September 2012 [1]
 August 2012 [1]
 July 2012 [1]
 June 2012 [2]


Categories


 Agriculture [71]
 Bayesian Inference [14]
 Books [15]
 Business Models [24]
 Causal Inference [2]
 Creativity [7]
 Decision Making [15]
 Decision Trees [8]
 Design [36]
 Eco-Green [4]
 Economics [12]
 Education [10]
 Energy [0]
 Entrepreneurship [59]
 Events [2]
 Farming [20]
 Finance [25]
 Future [15]
 Growth [18]
 Investing [24]
 Lean Startup [10]
 Leisure [5]
 Lens Model [9]
 Making [1]
 Management [9]
 Motivation [3]
 Nature [22]
 Patents & Trademarks [1]
 Permaculture [34]
 Psychology [1]
 Real Estate [2]
 Robots [1]
 Selling [11]
 Site News [15]
 Startups [12]
 Statistics [3]
 Systems Thinking [3]
 Trends [7]
 Useful Links [3]
 Valuation [1]
 Venture Capital [5]
 Video [2]
 Writing [2]