3 frågor till Petter:

Petter Dahlström
Petter Dahlström med avhandlingen New Insights on Computerized Trading.

Hur valde du ditt ämne för avhandlingen?

Jag arbetade tidigare på Stockholmsbörsen med inriktning på datoriserad börshandel, vilket också min avhandling handlar om. Så jag var väl bekant med mitt ämne och hade funderat på att börja forska under ett flertal år.

Vilka forskningsmetoder har du använt?

Kvantitativa dataanalys av finansiell information där syftet är att styrka kausala samband som förklarar finansiell teori.

Vad har varit lätt respektive svårt i arbetet med avhandlingen?

Det svåra har varit att planera avhandlingens innehåll. Forskning idag är tidskrävande och kräver ofta att man investerar mycket tid i insamlandet och bearbetning av data. Att skriva 4 artiklar på några få år innebär att varje enskild artikel blir halvdant utförd. I övrigt har det inte varit några större bekymmer. Att forska är både intressant och givande.

Abstract

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.