Microstructure (AssetPriceChange)

Tonight, I started programming a knowledge base in the Prolog computer language used in artificial intelligence. This will also form part of how I map out the underlying logics of two PhD case studies on strategic culture before I get to specific research methods (process tracing for one case study and thematic coding analysis for the other). Whilst coding I read Cullen Roche’s post on why stockmarket speculation is neither investment nor usually wealth generation:


Now, this doesn’t mean it’s impossible to become wealthy picking stocks on a secondary market.  There will always be people who “beat the market” and ride stocks to riches.  But that’s not the point of this story.  The point is, in the aggregate, the market returns the market return and the market return isn’t likely to make investors rich quickly in the aggregate.  In fact, you’re almost certainly buying an asset that has already made someone else rich well before you ever had the opportunity to own a claim on that asset’s cash flows.  This doesn’t mean that buying stocks and bonds is bad.  It doesn’t even mean you can’t “beat the market”, but we should be careful about the concept of “investing” and how it actually leads to us becoming wealthy.  You’re much more likely to become wealthy investing in your own ability to generate future production than you are by buying an asset that was actually someone else’s investment. [emphasis added]


Buried in Roche’s analysis is a comparison of cowboy intraday traders / momentum traders / trend-followers and Jack Bogle style index investment. This is like a battle between a discredited, naive form of active management and low-fee passive management that disrupts mutual funds. It’s a narrative that turns up in a lot of introductory books for retail investors. 

But there are at least two other options found more in funds management: (1) using arbitrage to find or to create market inefficiencies; and (2) trading the asset price changes that occur in market microstructure due to the order flow (and transaction / execution algorithms) of different market participants (such as proprietary trading desks; hedge funds; sovereign wealth funds; company directors; and noise traders). 

The first is a goal of activist hedge funds who use behavioural finance to create situations of crowded trades and rational herding; the second informs quantitative trading techniques like machine learning and statistical arbitrage. 

The first leaks to the media; the second uses news and text analytics to anticipate microstructure changes in asset prices. I spent a decade studying the first in media analysis, publishing and research. Now, I am interested in the quantitative insights of the second. This feels like a self-disruption of past work. 

System designers combine both options mentioned above in funds management: the first uses human intelligence; the second relies on computational intelligence. 

It will take a few years to complete the Prolog knowledge base project. 

When I’m done, I will have decomposed the relevant literature into white box models (Bayesian decision rules). Then, I will start on the black box version with live data. I have access to live data now – but I need more of a background in algorithms, machine learning, stream processing, and high-frequency econometrics – to really begin work on the black box version. 

The foundations are in place.