However despite this I still think that analysis such as this has value for the reasons I outlined in Part 2 i.e. when it comes to valuing companies you can get into all sorts of complex analysis such as CAPM in order to reach a valuation but you are still guessing as no one can predict the future. I am not claiming that PR definitely led to the increase in share price, but merely pointing out that there are statistical, logical and common sense arguments that would imply that it can play a part. Consequently organisations should consider that PR’s potential value is likely to vastly exceed numbers generated through measurments such as AVE. Just ask Gerald Ratner the impact on your share price of bad PR

I agree with you that while these techniques can prove correlations there is then a leap of faith in establishing causality.

Much of our work involves showing correlations between editorial coverage and resulting effects such as increased sales or website traffic in the following days and weeks. In these situations it often makes sense to hypothesise that one causes the other 1) because of the time lag, 2) because the former is more likely to cause the latter rather than vice versa and 3) we have worked hard to disaggregate effects from other causes.

My worry with share price is that these first two points become blurred. You mentioned ‘chicken and egg’ in relation to communications vs the deal itself but there is another example of this. Sentiment can drive share price, but share price can also drive sentiment – after all news of share price increases tends to be positive. Share price can also drive…share price, since many traders have automatic systems that are programmed to buy or sell at a given price. It also affects psychology – people are more likely to want to invest in an asset that is gaining in value, particularly if ‘groupthink’ kicks in and people around us perceive the same. This is of course why we have asset bubbles and why we are all wise after they burst – “well it was obvious the housing market was overheated!”

All of this shows that markets are complex non linear systems with many feedback loops that are difficult to model using linear regression. The finest financial and statistical minds have real problems building predictive models. I know this because I was recently at a conference with some of them which certainly put a (somewhat sobering) perspective on what the rest of us can hope to achieve!