The Scanner Channel: Market Microstructure

In September, fellow financial blogger Rooster360 ran a series called Meditation on a Stock Operator which I really enjoyed reading. This brought back a lot of memories for me because I had the good luck to be recommended Reminiscences of a Stock Operator by my very first trading mentor. I remember vividly the excitement the book elicited in my 17 year old brain. During my first trades I would recite mantras gleaned from Edwin Lefevre’s amazing chronicle of Jesse Livermore (JL).

This got me thinking about the strategy JL used to make his money, which we might call something like trend following today. Livermore would much rather buy high and sell higher than buy the exact bottom in a stock. He believed that waiting for a break-out was a form of insurance: he would gladly pay a premium for the stock to know he was right by the price confirming his prior beliefs. If there was no price confirmation he could assume he was wrong and update his thesis. Livermore was thus an intuitive Bayesian.

Back in JL’s day, you had to be a full-time student of the market to consistently identify the kinds of breakout stocks he traded. Think about it: back then market data was disseminated via telegraph and displayed by kid’s shouting prices to each other and writing quotes on a chalk-board. Financial journalism was in its infancy. Information flowed inefficiently, making handsome fortunes for the data brokers of the time.

These days things are far more democratic. Algorithms have made it trivial to identify stocks hitting new highs, new lows, and all sorts of more complex filters. At least a dozen financial websites offer a stock screener to do just that. Oftentimes you don’t even have to look it up yourself. Anyone following a few financial bloggers’ Twitter and StockTwits accounts will get bombarded with such stock screeners all the time, and my followers are not spared from this deluge.

Despite their ubiquity, these lists provide an important channel for the dissemination of financial information. When prices are rising or falling rapidly its usually the result of some serious imbalance in supply or demand. Stock screeners focus a wider audience’s attention on stocks which are experiencing major liquidity imbalances. Illiquidity can lead to temporary aberrations in price that wouldn’t otherwise exist in a liquid market. By making more people aware of a stock which is “in-play” a widely read screener probably increases liquidity and trading volume in the stocks at the top of the list.

On the other hand, the act of broadcasting a list of winning stocks could highlight an important psychological component of the momentum effect. As Kindleberger noted, nothing is more disconcerting to a man’s well being than seeing one’s neighbor get rich. I would imagine that this psychological mechanism contributes to the momentum effect on the upside: seeing lists of stocks which have just made new highs stimulates the greed of readers. These stocks are by defintion, probably doing much better than the stocks in the reader’s current portfolio.

Could be an interesting line of research going forward, might start with a literature review in the meantime…


What’s heretical, powerful, and bound to become ubiquitous in the future?

Check out the release of SliceMatrix: a unique tool for visualizing the financial market using filtered correlation networks like minimum spanning trees


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