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Reinforcement Learning for Electronic Market-Making

By Christian R. Shelton

Abstract

The Problem: Many economic markets, including major stock exchanges, employ market-makers to aid in the transactions and provide a better quality market. Market-makers supply an advantage to the market. By consolidating the trading in a few agents, the market becomes more efficient. Traders wishing to buy and sell do not need to find each other or wait for each other’s arrival. Additionally, by quoting a single price which is guaranteed for all traders, market-makers remove the price fluctuations thatoccur in markets where buyers and sellers must come to their own agreement for a price individually for each transaction. Markets with market-makers have greater volumes and better price stability. Many major markets are now electronic. The NASDAQ is a distributed trading system completely run through networked computers. It uses competing market-makers (usually one per major trading company) to maintain a high quality market. However, the demands on human market-makers are high. A typical market-maker will be responsible for 10 to 20 securities. At any given moment, it is only feasible for the market-maker to be actively attentive to 2 to 3 of them. The market-maker is generally losing potential profit or volume on the other securities. The last few years have also seen the growth of on-line trading systems. These systems are also entirely electronic and usually employ no market making. Orders are crossed against the other orders that happen to be present at that time of the trade and otherwise are dropped

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.4961
Provided by: CiteSeerX
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