The most basic approach to investing is to have a linear narrative of cause and effect where an opinion about the movement of one variable leads to a knock on effect in the price another variable. Of course, this sort of investing can be dangerous because it’s easy to fall prey to other variables moving against you. Nonetheless the simplicity of developing out a narrative of events seems to be sufficient to make this style of investing remarkably popular. I suppose the underlying assumption is that, if your narrative is correct then on average the other variables with be with you as much as they are against you.
Nonetheless, even simple narratives can often rely on hidden assumptions that I feel it’s probably worthwhile to be systematic about laying out the narrative and the specific evidence that underpins each assumption.
Doing this, especially in highly liquid asset classes, where the trade volumes and consequently market discussion is so much higher means that if there is a change in one of the assumptions you as a trader may be able to react quicker to the changing situation.
To illustrate what I mean consider a simple narrative in which the price of iron ore depends upon the Chinese government’s decision to embark on a stimulus package or not.
As you can see the proposed narrative is that a Chinese government stimulus package would lead to an increase in Chinese iron ore demand which in turn leads to an increase in iron ore price. The arrows between represent the assumption that one variable acts on another (β) and crucially how strong this causal relationship is. For example, to what extent does Chinese iron ore demand determine iron ore price. What is really cool about this approach is in carefully laying out the assumptions it becomes very clear what data needs to collected to test each piece of the narrative and in particular the individual investor can start to leverage the huge weight of academic financial literature (particularly Econometrics research) that exists out on the internet largely untapped. Over time this framework allows you to track a simple narrative over several months and provide a context to interpret news events within.
For example, increase in Australian iron ore production not only increases supply but also decreases Chinese iron ore demands importance in determining iron ore prices.