3 questions for Petter:

Petter Dahlström
Petter Dahlström with the thesis New Insights on Computerized Trading.

How did you choose your topic for the dissertation?

I have a background at the Stock Exchange in Stockholm, and have been working with computerized trading strategies which is the topic of my dissertation. I knew my subject and had been considering pursuing doctoral studies for some time when I took the decision.

What research methods have you used?

Quantitative analysis of financial information with the aim to find evidence of causal relationship explained by theory.

What has been easy and difficult in your work with the thesis?

The main issue have been to plan the content of the thesis. Research is time consuming and one needs to invest a lot of time in collecting and preparing data. To write 4 articles in a few years implies that each article cannot be fully developed. Other than this, things went quite smooth. Research is both interesting and awarding.


Computerized trading may be viewed as an aspect of modernization of financial markets. This dissertation contains four articles that in different ways examine to what extent the modernization influences the economics of the markets.

Article 1 investigates transaction costs for large orders which are split up by execution algorithms to be executed in smaller pieces.  I find that the costs associated with not being able to execute all pieces are substantial. These costs can be lowered by speeding up the trading pace but at the expense of higher costs for the successfully executed pieces.

Article 2 investigates the strategies trading firms pursue in particular cases, known as toxic arbitrage opportunities. We find that trading firms, that otherwise behave as market makers, morph into liquidity takers as toxic arbitrage opportunities emerge. In contrast to common belief, market makers are net beneficiaries of toxic arbitrage, and this finding puts into question whether the amount of toxic arbitrage leads to wider bid-ask spreads.

Article 3 investigates the information content of limit orders in an alternative way by studying the price impact implied by the depth in the limit order book. I find that the price impact estimates are slightly lower relative to those from a structural vector auto regressive model, but slightly higher compared to those from a price impact regression. Thus, the limit order book implied price impact estimates match those from benchmark models, and this finding contradicts earlier research.

Article 4 investigates the economic rationale behind limit order cancellations. We put forth a model that explains the frequent limit order cancellations seen in today’s markets, and we test its predictions using a unique data set from Nasdaq. Our results points towards that frequent order cancellations is a benign feature of modern market making, as opposed to different types of manipulative behavior.